Biomedical Research

Delivery of Nucleic Acid Therapeutics for Cancer Immunotherapy

By Ilma Khan

Published 6:08 PM EST, Fri June 11, 2021


Living in this advanced world today allows us to have access to new incoming ideas such as the use of various drugs that can have great potential and benefit different diseases such as cancer. In this research journal, they focus on the great potential of cancer immunotherapy using various immuno-oncology drugs. The use of nucleic acid therapeutics on different diseases, specifically cancer, has advanced day by day, showing us how different drugs have unique abilities and how these abilities have advanced in the world we live in today. Through trial and error researchers have figured out problems each therapeutic faces, for example, negative charges and hydrophilicity known as the attraction to water. In this research, we learn about the different drug delivery systems that can safely release and target specific cells. Some of the different drugs that can be used to target specific tissues and cells include small interfering RNA (siRNA) and messenger RNA (mRNA). A few of the discussed delivery systems that can safely transport these drugs to the targeted area includes the nanoparticulate drug delivery system of nucleic acid therapeutics using micelle and the delivery of cGAMP using liposomes (structured cGAMP vs. free cGAMP). After the experimenting and research, the results have displayed that these nucleic acid therapeutics have a remarkable amount of potential for a vast assortment of diseases. Each drug has unique skills and characteristics such as changing gene expression and regulating protein function for immune responses but also has challenges blocking the delivery systems to achieve effective transportation to the targeted cells. Each day new delivery systems are created and modifications are made to different therapeutics which would allow us to issue problems connected to the treatment in the immunotherapy of cancer.

General Analysis

Through the process of reading, analyzing, and critiquing this paper many reasons indicate why this paper makes a good contribution to society. The specifics in this paper allows us the readers and writers to dig deep and think. The use of different words we’ve never heard or seen and the use of deep topics that take time to understand makes the readers ask themselves questions and makes them left wanting more, therefore, resulting in researching deeper into the topic. This paper discussed the different nucleic acid therapeutics, the advanced delivery systems of these therapeutics, and the positive functionalities as well as the challenges of these therapeutics allowing the audience to see these therapeutics from all angles so he can add more to the research. Another reason why this paper is a good read is that the paper provides sufficient proof of how science and medicine have advanced together. The paper provides a visual timeline dating back to 1995 when the first description of CpG-dependent stimulation was created. This paper has contributed to the medical society by bringing in new medical knowledge related to different nucleic acid therapeutics and their delivery systems. This paper brought in new medical information by displaying the pros and cons of each drug and the different delivery systems that can be used to target specific diseases. This paper can allow other researchers to conduct experiments on therapeutics and use their information to see which delivery system will work best with the disease they are targeting. This paper presents proof and factual information about what therapeutics have the potential to use towards cancer immunotherapy, leading other researchers into using their research to help towards another experiment. 

Evaluation of Methods Used

The methods utilized in this paper allow for a variety of diseases to be treated, the research was open-ended and shows the potential for each therapeutic and how it can be delivered to target specific tissues and cells. Listing many options for delivery systems and many characteristics for each drug allows other researchers to solve other problems using this research. This research was done on the immunotherapy of cancer using nucleic acid therapeutics meaning it could work on a wide range of cancer types. The researcher’s methods brought proof indicating that these therapeutics could work on the immunotherapy of cancers, they displayed how these drugs can be used and which drugs we should use to be treating this cancer. The treatment of cancer is evolving every day, different medical researchers are looking to find a way to cure cancer and this research could play a big role in finding a sufficient treatment of cancer. As stated in this paper the FDA is starting to approve more studies involving nucleic acids and immunotherapy. Indicating that it will have a significant impact in treating different types of cancers and diseases.


There are negatives and positives to everything, there are side effects but these effects can also result in something life-changing. This paper provides a variety of information indicating the success of using these therapeutics on cancer immunotherapy but there are still concerns. Concerns regarding the different side effects on the human body after immunotherapy using these drugs, the survival rate, and the possibility of taking all or most of cancer away. Could these therapeutics lead to a new disease in the human body, taking away one cancer resulting in new cancer developing? There may be great proof and progress of using these therapeutics for different diseases but the challenges still exist. Challenges such as could this drug be toxic for the human body, the quantity of this drug vs. how much it costs and as well as what happens to the therapeutics inside my body after the treatment. This paper was published in the right journal for the correct audience that would put interest into this topic. This paper shows how the discovery of nucleic acid therapeutics can work towards advancing medicine and cure a life-threatening disease. 

Problems and Admirations

In this paper, I enjoyed the different points of view on each drug and delivery system. The method used in this paper allows the readers to deeply think about each topic and step on the journey to treating cancer. I admire the researcher’s evaluation techniques as they went in-depth and provided an outstanding amount of information on this topic, hooking the readers causing them to keep reading and researching more about this topic. The researchers brought hope and showed how advances in medicine today can change anyone’s life. After reading this paper I understood the writer’s thought process, I understood all the different ideas they were thinking and it allowed me to feel grateful for the new innovative ideas we have that can save someone’s life. After this paper is published it will show people the future we have that is full of ideas worthy of doing anything. It will allow others to take the little ideas and thoughts in their heads and turn them into something beautiful. This research will contribute to other studies and allow us to be one step closer on the journey to find a cure for cancer. 

Ilma Khan, Youth Medical Journal 2021


“Delivery of Nucleic Acid Therapeutics for Cancer Immunotherapy – ScienceDirect.” ScienceDirect.Com | Science, Health and Medical Journals, Full Text Articles and Books., Accessed 17 May 2021.

Biomedical Research

Promise and Peril: Machine Learning in Modern Cancer Treatment

By Sia Shah

Published 4:03 PM EST, Mon June 7, 2021


Machine learning in modern cancer treatment is a fast-growing field that promises to produce many scientific breakthroughs in the future. This article discusses both the promises and perils that come along with applying artificial intelligence to cancer treatment. With cervical cancer treatment, this growing technology can be used to assist doctors in cancer detection as well as to predict patient survival rates. In lung cancer treatment, artificial intelligence platforms are again used to make predictions for patient health in addition to analyzing images for a more accurate prognosis. Finally, machine learning is also able to predict the survival rate and metastasis for different forms of brain cancer and provide medical students with realistic surgical simulations on how to operate. However, while there are a multitude of promises for the future of AI in medicine, integrating new technologies into a previously established field does have disadvantages. The constant evolution of software and technology means that operators require constant training to be able to handle the tasks Furthermore, the lack of doctor-patient feedback can take a negative toll on patients’ mental health and privacy. The automation of various processes, comes at the cost of various jobs, of people who originally performed these tasks manually. Therefore, when implementing AI into the medical field it is important to acknowledge the great promises the technology has, but also to weigh the negative effects that may result from its application.


One of the most important parts of practicing medicine is decision-making, a skill that relies heavily on judgment. Cancer treatment, or Oncology, is a medical speciality where decision-making is incredibly important because of the unpredictable responses to treatment and change in a patient’s condition. This is where artificial intelligence (AI) comes into play. It is a promising tool that can objectively interpret cancer images and predict a cancer patient’s outcome- essentially mimicking the cognitive functions of humans. Research has shown that AI has the potential to exceed human performance in certain areas of medicine. Multiple examples of useful areas within AI will be discussed in this paper including two main ones. The first is detection or determining which objects are located within the body by analyzing images. The second task is characterization, separating tumours into groups based on physical appearance (Bi et al., 2019). Both of these tasks are a crucial part of making clinical decisions.

Machine learning (ML) is a subset of AI (Fig.1) that has been widely used in current healthcare applications since it uses data to train computational systems without the need for explicit programming. These computer programs can learn and improve from experience, unlike traditional computer programs that require specific instruction at each step, which makes them incredibly useful in the field of science (Ahuja, 2019). Allowing machines to make predictions based on a pattern that they have recognized. With the use of ML, a computer can use previously labeled data or even the pattern found in the data, and make predictions about it. In particular, ML excels at finding indistinct patterns which are undetectable to humans, in larger sets of data. ML also enables an algorithm to perform a task such as making medical decisions or driving a car while also correcting its own mistakes. Deep learning is a subset of ML that uses structures similar to a brain neural network in order to identify patterns within large datasets. CNN’s or convolutional neural networks are other subsets of ML that will be discussed and are generally applied to classification as well as analysis of patient scans (Hashimoto, Rosman, Rus, & Meireles, 2018).

In the future, AI analysis has the potential to work its way into all parts of patient care. Before surgery, it can help track the activity of a patient and access electronic health records. During surgery, it could assist the surgeon in making quick decisions based on the patient’s vital signs. After surgery, it can continue to collect and analyze patient data (Hashimoto, Rosman, Rus, & Meireles, 2018). This paper will discuss the application of AI to cervical, lung, and breast cancer treatment including the use of detection machines, segmentation techniques, and prediction algorithms, as well as weigh the challenges and social aspects of introducing AI to the medical field.

Figure 1. All types of Artificial Intelligence that will be discussed in this paper can be broken down as seen in the figure below. Within AI or all machines which are programmed to think in the way humans do, one can find machine learning. This is a more specific form of AI that is centered around the ability to improve performance based on past errors. A subcategory of machine learning is neural networks. Most studies that are discussed in this paper are on platforms and algorithms that are created with neural networks, or systems of algorithms that can use data to recognize patterns and then for this purpose, make predictions using that data. The final two categories, which are both subsets of neural networks, are deep learning and convolutional neural networks. Deep learning further imitates the way human brains think and learn. Convolutional neural networks are simply layered neural networks.

The Application of AI in Cervical Cancer Treatment

When dealing with AI and cancer detection, one of the most prominent issues that comes up is the invasiveness of diagnosis as well as how many cases are missed. While it can be cured if found at an early stage, many women die every year because their cancer was not detected early enough and symptoms did not appear until the cancer was too far advanced to treat. One of the few cancers that will be discussed in this paper is cervical cancer. Cells in the cervix can either be squamous cells, which when infected cause squamous cell carcinoma, or glandular cells which causes adenocarcinoma (P & M, 2018, p. 1). Because cervical cancer is difficult to detect and hard to treat if it has progressed too far, automated machines that can detect cervical cancer could significantly improve the survival rate of women suffering from the disease.

A study performed in 2018 proposed an automatic detection assisted by artificial intelligence which could detect cervical cancer in patients. The first was a preprocessing step and it involved taking a cervical cancer image and enhancing the contrast of it for better visibility using Oriented Local Histogram Equalization. Certain features such as roundness, sides, and circularity were then extracted from the image and used to train the neural network. The features were extracted to discriminate between a healthy cervical image and a cancerous one. The neural network classifier would then identify the cervical image as either benign or malignant by comparing it to the features used for training. For the classification of the tumour, a feed-forward backpropagation neural network was used to make the classification reach the highest possible accuracy. This type of neural network is built using three layers. The input layer accepts the elements of the features that were extracted. Three “hidden” layers in between all with different functions are also used, each of which has a different number of neurons, or calculated inputs from the previous layer. An example of this would be the average of all of the results from the previously hidden layer. The output layer is the response of the neural network and classifies the image as either normal or cancerous (P & M, 2018, p. 1).

Another important part of screening is finding cancerous lesions in images. Segmentation is a difficult but necessary part of this. The most common test for cervical cancer screening is cervical cytology or the pap smear test which screens for malignant tumour cells in the cervix. A positive cytology test can show different types of abnormal epithelial cells such as atypical squamous cells or atypical glandular cells. Segmentation is the process of separating masses in an image and is the most important step of cytology as it identifies cells based on their structures and morphology. In the majority of cervical cancer cases, cell segmentation is followed by abnormality classification which is frequently performed by feature-based machine learning algorithms as well as deep-learning approaches. Feature-based classification is based on feature extraction. Common features include the size, shape, colour, and texture associated with malignant tumours. Once feature extraction is complete, multiple different algorithms can be used for classification. A radial basis function support vector machine was developed that could classify images blocked into six different categories including blocks with many white cells, blocks with normal epithelial cells, and blocks with suspicious epithelial cells. The researchers expressed that the blocks with suspicious cells had a considerably different texture and colour features which set them apart from the others (Conceição, Braga, Rosado, & Vasconcelos, 2019, p. 21). The support vector is different from the layered neural network because instead of passing through a series of layers, there is only one function that separates or classifies the inputs into multiple categories. This method of classification skips the segmentation step entirely and saves a lot of time. Artificial neural networks, an unsupervised classifier, meaning they do not require inputs of labelled data to be trained, are another type of classifier that can study cell images and determine their level of abnormality.

Deep learning classification in the form of a convolutional neural network is a classification that can be performed without segmentation. On the other hand, this type of network does require far more computational time and high numbers of labelled data sets, making them impractical in clinical settings (Conceição, Braga, Rosado, & Vasconcelos, 2019, p. 22). 

With the help of such techniques, the survival rate can be increased and the chance of complications occurring can be decreased. These two measurements can also be predicted by Artificial Intelligence to ensure proper treatment and patient comfort. In an experiment to test survival rate prediction, a data set was collected from a total of 102 patients, all with cervical cancer that had already undergone initial surgical treatment. The researchers identified 23 demographic variables including age, BMI, and hormonal status, and 13 tumour-related parameters including tumour size and a number of lymph nodes, to direct the experiment. The computational intelligence methods that were applied had not yet been used to predict patient survival for cervical cancer treated by radical hysterectomy. Six of these were classifiers: Probabilistic neural network (PNN), multilayer perceptron network (MLP), gene expression programming (GEP), Support Vector Machines (SVM), Radial Basis Function Neural Network (RBFNN), and the k-Means method. The prediction ability of these models was determined by measuring accuracy, sensitivity, and specificity. The best results in the prediction of 5-year overall survival in cervical cancer patients who had already undergone radical hysterectomy came from the PNN model (Obrzut, Kusy, Semczuk, Obrzut, & Kluska, 2017, p. 4). The PNN model similar to the feed-forward backpropagation neural network mentioned earlier is made up of an input layer, a pattern layer, a summation layer, and an output layer. The PNN model along with other AI methods can be applied to various medical classification jobs (Fig. 2). 

The prediction of complications occurring during or after surgery is also vital to determine a patient’s chance of survival. One study was performed on 107 individuals with cervical carcinoma who had undergone surgery, and a cervical biopsy was taken to determine an AI algorithm’s ability to diagnose cancer. Complications around the time of surgery were evaluated both during the operation and post-operation. The gene expression programming (GEP) algorithm which makes and advances computer programs, was used for this study. The GEP was compared with the multilayer perceptron (MLP), the radial basis function neural network (RBFNN), and the probabilistic neural network (PNN), all of which are feed-forward neural networks. Each of the tested models was ranked based on their specificity, accuracy, and sensitivity. The highest accuracy was found in the MLP neural network. Complications near and around the time of surgery occurred in 47 patients although most of these were minor complications that did not severely harm the patient or put their lives in danger. Other more serious complications were found in 7 of those patients and included pulmonary embolism, or a gastric ulcer rupture (Kusy, Obrzut, & Kluska, 2013, p. 4). This study goes to show that it is imperative to identify any risk factors of surgery and choose the appropriate course of treatment as soon as possible because if products to remove cancerous tissues are postponed, the patient’s chance of survival is likely to decrease.

Figure 2. This figure is an example of a neural network that was created using a series of layers. A specific neural network that is similar to the one portrayed above is the feed-forward backpropagation neural network that was used for a 2018 study on the detection of cervical cancer. Another PNN model was also discussed in which there were three hidden layers in addition to the input and output layers. Patient scans were the input for this algorithm, like most other detection algorithms. The hidden layers included a pattern layer and a summation layer both of which had distinct functions and helped perform the calculations and an output, the prediction of 5-year patient survival, was reached.

The Application of AI in Lung Cancer Treatment

Like cervical cancer, lung cancer is life-threatening and is actually one of the leading causes of deaths in the world, therefore accurate diagnosis and treatment planning are extremely important for a patient’s survival. Recent breakthroughs of artificial intelligence, and specifically deep learning algorithms that can solve complex problems by analyzing images, are giving scientists hope. Researchers of one study developed a deep learning model to aid in lung cancer diagnosis to help reduce the work of pathologists. A convolutional neural network was trained to classify small patches of a histopathological image of a lung as either malignant or benign and had an accuracy rate of close to 90% (Wang et al., 2019, p. 8). This method would enhance the diagnosis of lung cancer by allowing for incredibly fast tumour detection when the region being studied is relatively small. Aside from diagnosis, the prognosis is one of the key parts of cancer treatment. Predicting if a tumour will recur and how long a patient will survive are crucial to determining the proper course of treatment. Wang’s team developed yet another CNN model that could segment slide images by the boundaries of the nucleus. Different features of the nucleus were extracted and used in a model that predicted the chance of recurrence (Wang et al., 2019, p. 9). 

Furthermore, scientists have found a relationship between a patient’s genetic files and pathological phenotypes, and genetic mutations that cause tumours. Such biomarkers are evolving and can be a useful tool in helping physicians with the screening and detection of lung carcinoma. An ideal biomarker is one that indicates biological, pathogenic, and pharmacologic processes and responses, and can impact clinical decisions in order to benefit a patient. Additionally, when being used for undefined pulmonary nodules, a biomarker should have the ability to predict and anticipate the diagnosis of cancer so that treatment can be administered as soon as possible and overdiagnosis is avoided. Scientists have established a few promising biomarkers (Fig.3), such as urine and saliva, that are currently used. Blood is another biomarker that can be used for lung cancer screening as it can help to identify and study the tumour and the space surrounding it, any metastases, and the patient’s immune response. Sputum, which comes from the airway epithelium can also be used for lung cancer and is able to supply data about any changes in a molecular structure close to the tumour cells. Autoantibodies are another form of biomarker which develop as a result of the formation of a tumour before any symptoms appear on images (Seijo et al., 2019, p.5). These autoantibodies have been discovered in all types of lung cancer, meaning in the future they could be indicators of lung cancer. Further studies are examining the rise of newer biomarkers that can be used alongside AI to decrease lung cancer patient mortality rates. A specific nano-array sensor which runs off of artificial intelligence, and has the potential to distinguish benign tumours from malignant ones, was used in a study to diagnose 17 different diseases from exhaled breath samples and there was an accuracy rate of over 85% (Seijo et al., 2019, p. 8). Other prediction models that use AI were also able to identify malignant tumours from harmless nodules, promising a bright future for AI-based diagnosis

AI platforms that use deep learning are being considered as a tool in fighting lung cancers is deep learning. Deep learning models allow researchers to remove certain characteristics for data that is imputed as well as have many layers and kernels, neurons in the layers between input and output layers, that allow them to perform many functions using the removed characteristics (Wang et al., 2019, p. 5). Deep learning has the ability to recognize complex data patterns, requiring no human input, and systems that use deep learning are not subjective the way human physicians are. A more specific class of deep learning is convolutional neural networks or CNNs. These models learn features from images and can eventually even predict outcomes. CNNs have been used in classification, segmentation, and detection, learning from histopathological and radiographic imaging, showing great potential in both areas. The automated feature extraction that deep learning models can complete is a huge advantage as manually removing features from pathology images is very time-consuming when the problem is challenging and complex or when researchers do not know very much about the input data and their relations with the outcomes that the model will predict. 

Like any disease, it is also always helpful if physicians are able to predict the chance of survival of a patient. A recent study was completed on medical images and information about tumours that could be helpful in prognostication efforts. 1194 individuals with NSCLC, who had been treated with either radiation or surgery, had CT scans taken of them and different elements that would determine a prognosis, Kwon as prognostic signatures, were detected using a convolutional neural network (Hosny et al., 2018, p. 1). CNN was highly successful in separating patients based on their chance of mortality. The network was also trained to predict the likelihood of a patient’s survival, 2 years after the start of their treatment. After the experiment was complete, the scientists dove in even deeper to get a better understanding of the different features detected by CNN and found specific areas that had the greatest impact on predictions made by the platform. To understand which regions in the CT images are responsible for the predictions made by the neural network, activation maps were created over the final convolutional layer. The intensity of the gradients in this layer determines how important each node actually was for the prediction. Most of what contributed to the prediction were from large areas of space both within and around the tumour, with higher CT density, and the areas with a lower density, such as the uncommon vessels, did not contribute very much to the predictions (Hosny et al., 2018, p. 12). Normal tissues such as bone tissue, which is of higher density, was ignored by the network as it appeared in most if not all images and had no significance on the tests. All of the actions that such a network takes- extraction, selection, prediction- are automated and have no data to back up why a certain prediction was made which makes it hard to prepare for failure. Although limitations do exist, there are possibilities for tools that can be created. An imaging tool with the ability to classify more specific information and identify treatment pathways would be helpful in managing all patients who suffer from NSCLC.

Lung cancer screening in developed countries is generally carried out with the use of LDCT or low-dose computed tomography. Although LDCT might be the favorable pathway for lung cancer screening and detection in the United States and other developed countries, developing countries face other challenges that makes it harder to integrate technology such as LDCT into routine clinical practice. It is very hard to develop programs that can screen for lung cancer in underdeveloped countries due to the vast amounts of pulmonary tuberculosis and chest infection cases. These conditions have similar symptoms to lung cancer such as fever, anorexia, weight loss, and cough, however individuals with histories of smoking, and a hoarse voice tend to be diagnosed with lung cancer (Shankar et al., 2019, p. 7). One of the most harmful consequences of using LDCT is that benign intrapulmonary lymph nodes and non-calcified granulomas are often hard to distinguish from pulmonary nodules, leading to many false positives, and thus unnecessary radiation which will eventually lead to the actual formation of cancer (Shankar et al., 2019, p. 7). Solutions for this issue include using computer aided diagnosis systems that are more sensitive with their detection, along with biomarkers that were previously mentioned, which can make screening more efficient. 

While LDCT is optimal, risks such as radiation and overdiagnosis along with cost make it hard for scientists to introduce at higher levels. Such methods that use AI have hopeful implementations in imaging and radiology, such as cancer detection and assistance making decisions, and the application of AI to pulmonary oncology will open up many pathways for diagnosis and prognosis using clinical, pathological, and morphological features of patient scans.

Figure 3. One of the things that can be used to determine if a patient has any genetic mutations are biomarkers. Examples of promising biomarkers are blood, sputum (mucus found in the airway epithelium), and autoantibodies. These biomarkers can be used to detect tumor cells because scientists are aware of how these biomarkers look in a normal state, however when a tumor has formed, the biomarker’s appearance will change due to surrounding cancerous cells.

The Application of AI in Brain Cancer Treatment

Another type of aggressive cancer that is hard to diagnose is brain cancer. When applied to data from MRI scans, AI has great potential in the field of neuro-oncology and is multi-purpose as it can help establish how harmful a tumor is, find invading gliomas, predict the chance of recurrence and survival of patients, assess the physician’s skills, and simulate cranial surgeries to strengthen neurosurgical training. 

Like in cervical cancer, segmentation is a large part of diagnosis of brain tumors through radiomics, and can be performed by deep learning AI platforms (Rudie, Rauschecker, Bryan, Davatzikos, & Mohan, 2019, p. 3). From these images, different features are extracted including size, shape, textures, and patterns. Machine learning platforms are then used to find relationships between the features, and determine the prognosis of the tumor. MR spectroscopy which compares the chemical composition of normal brain tissue with abnormal tumor tissue is used to classify gliomas and glioblastomas into different grades, depending on severity, and has the ability to identify regions where lymphocytic cells have invaded the tumor. Once found, the current treatment method for glioblastoma and gliomas is a resection of the tumor along radiation or chemotherapy using a medication called temozolomide (Rudie, Rauschecker, Bryan, Davatzikos, & Mohan, 2019, p. 8). However an effect that radiation and chemotherapy sometimes have is pseudoprogression, an increase in the size of the primary tumor or the appearance of a new lesion.

Machine learning devices that take patient images into account can be used in such instances to predict if a pseudoprogression is likely to occur. Researchers recently performed a study where an AI system was developed to outline characteristics of cancer cells in tissue grafts from patients that came from both the primary tumor and brain metastases, tumors in the brain that have formed from the original tumor. A live cell imaging algorithm was combined with AI and was used to study the movement of cells toward the area with damaged tissue and make out any differences between cells with and without brain metastatic potential. The study presented a device that could make calculations and predictions with the help of AI. The platform would be able to use a 3D measurement of cancer cells behavior in a BBB, or brain-blood barrier model outside of the organism and determine which cells have a brain metastatic characteristics. The visual differences between cancer cells that can form metastasis in the brain and those that cannot are very slight but the studies that used AI to identify these distinctions showed a large difference in the behavior of cancer cells and normal cells when they came across the BBB, making the AI device a very helpful tool for recognizing preseudopregrossions and potentially predicting them (Oliver et al., 2019, p. 4).  However there are always limitations that come along with medical breakthroughs such as these. While the ex vivo model in this experiment is able to identify differences in cells that are able to and not able to cross the barrier, the characteristics of cells with metastatic potential are still inconclusive. There are not yet enough features of a cell which platforms can detect that will allow an AI algorithm to accurately predict if a cell will metastasize. Furthermore, a brain cancer patient’s brain will have already changed in some way before diagnosis, making it more prone to the formation of metastases (Oliver et al., 2019, p. 8).

After it is properly developed, the use of AI to detect cells with the potential to metastasize in the brain will increase survival rate. Artificial intelligence can also be used to predict these chances of survival for patients suffering from cancer. A recent study used an artificial intelligence tool called the DeepSurvNet that runs off of neural networks in order to determine brain cancer patients survival rates and sort them into four different classes, based on just their histopathological images. To train the model, researchers used a dataset created using the medical records of brain cancer patients with 4 different types of brain cancer. 4 classes were used to classify patients by the time between their brain cancer diagnosis and death. Multiple regions of interest in the tumors from the imaging slides were also allocated to each of these classes. The model was then tested using completely new sets of data from histopathological slides. Glioblastoma tissue sections stained with H&E dyes of 9 new patients were analyzed. The device classified most patient samples in a single class, which was anticipated as the regions of interest are all taken from the same tissue sample (Zadeh Shirazi et al., 2020, p. 9). With the use of the DeepSurvNet classifier, physicians can use the difference between tumors that allow for different lengths of brain cancer patient survival to create specialized treatments and significantly decrease patient mortality.

In addition to it being hard to diagnose, because neurological cancer is such a rare condition, doctors do not get to see many patients with it and therefore lack training. Artificial Intelligence that can deliver feedback based on users touch, has the potential to create a realistic environment for trainees to practice their surgical skills without having to operate on real patients. Particularly, surgical simulations can be used in training for neurosurgery as the tasks required in the field are very technical and need to be performed under large amounts of pressure since one mistake could lead to severe consequences. A study on The Virtual Operative Assistant, shows the benefits of using AI to conduct training that tests cognitive skills and determines the level of psychomotor expertise that an operator possesses through the use of a surgical simulation. During the experiment, 50 participants, all with differing levels of expertise were recruited and classified into two groups: skilled and novice. The classification was completed after all of the participants were asked to complete a complex virtual reality simulation where they had to remove a brain tumor located beneath the pia mater subpial tissue, a type of connective tissue, using two devices-one in each hand. In order for the Virtual Operative Assistant to perform the classification, over 250 performance metrics were generated that were representative of differing levels of expertise in the surgical field and only 4 metrics with the highest level of accuracy were chosen after careful consideration and selection processes (Mirchi et al., 2020, p. 4). After the machine learning algorithm computed its classification between “skilled” or “novice”, it also gave users a breakdown of its assessment on both the safety and movement metrics, and rather than assessing each metric on its own, the Virtual Operative Assistance included the relationship between metrics, allowing students to recognize that one strong metric may be making up for poor performance in another one. The three forms of feedback that the Virtual Operative Assistant is able to give users, auditory, text, and video-based, is what makes it extremely beneficial in the world of science. This new technology enables scientists to understand the expertise of an individual, identify cognitive expertise in tasks that are much too complex for human teachers to notice, and mimic real life training, all making it the perfect tool for simulation-based learning.

Evolving technologies and Further Use for Medical Education

Simulation-based training systems, such as the Virtual Operative Assistant, are able to develop checklists that evaluate different skills using machine learning algorithms (Sapci & Sapci, 2020). While there are numerous applications for AI cancer treatment: screening and detection, survival rate prediction, and surgical simulations which allow doctors to more efficiently develop surgical skills and treat patients, AI platforms do possess many challenges that come along with using them. Of the different challenges that AI platforms in medical training pose, feedback and liability issues are two of the most prominent. A study done at Mount Sinai Hospital created a type of AI technique known as deep learning using data from 700,000 patients (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019). Their algorithm was highly accurate and was able to diagnose conditions that even experts struggle to diagnose, such as schizophrenia. However, AI systems often lack the ability to provide users with a proper explanation for how a certain answer or prediction was reached (Fig. 4a). These algorithms cannot properly understand the cognitive thinking of learners and therefore cannot properly train them in the actual areas where they need to be trained. This brings about the issue of liability because it becomes very hard for patients to trust a system that cannot provide an explanation for how it reached a diagnosis, and if a calculation were to be made incorrectly that puts a patient in danger, then it is not known whether the doctor, the hospital, or the company that developed the AI device is liable (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019).

Because of their ineptitude to comprehend the emotional reasoning of users and provide appropriate feedback, AI-powered teaching platforms enable students to “cheat” the system. Many algorithms in artificial intelligence teaching tools do not actually train surgeons and increase their skill level. Rather, they make the assumption that a student is skilled in a certain area because they were able to accomplish one certain task. In the case of the Virtual Operative Assistant, this ability to cheat (Fig. 4b) can be credited to the relatively broad parameters that classify students as either skilled or novice (Mirchi et al., 2020, p. 16). In the experiment completed using this specific AI platform, there was a misclassification where 4 participants that were actually at the novice level were labeled as skilled. Such errors make it difficult to trust AI-powered teaching tools and implement them into routine medical practice and surgical training.This is where human expertise comes in and proves essential in the learning process. If AI platforms undergo diligent training alongside human experts that can properly assess the algorithm and recognize specific markers of a good surgeon, then cheating the system would be much less likely. Furthermore, the issue of learners feeling a disconnect from their teacher due to lack of feedback and properly backed up explanations, which can actually damage a students skill level, can be resolved by human interaction (Chan & Zary, 2019). AI could be substantially more useful for tasks such as computerised testing and cancer screening or diagnosis, but if physicians and AI-based machines are able to work in harmony then patient outcomes are guaranteed to improve as AI has the potential to process large amounts of data including medical reports, notes from pharmacists, and genetic reports, as well as analyze all of it. Nonetheless, a major thing that it cannot take the place of is the beauty of doctor-patient and doctor-student interactions (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019).

Figure 4a. This figure demonstrates the lack of explainability in “blackbox” algorithms. The term “blackbox” means that while the algorithm is producing an educated response, scientists are not able to access the process being completed inside the algorithm. The algorithm does not give them feedback so doctors are not able to provide patients with adequate information to back up the reasoning behind their decisions. In this figure, the same patients scans are being input into two algorithms and both come up with the same patient diagnosis however one algorithm allows doctors to have an understanding of the decision making, while the other is a complete “blackbox”. Due to their multilayer non linear structure the Virtual Operative Assistant, a deep learning model, is considered a “blackbox” and criticized for its lack of transparency.
Figure 4b. A potential pitfall that can occur with these “blackbox” algorithms is the possibility of cheating. In the case of the Virtual Operative Assistant as shown below, the parameters for classification as either skilled or novice are relatively broad. For example, if the algorithm determined that a participant’s performance was ‘poor’, they would most likely be classified as novice. However, due to the broad parameters, there is still likelihood that they could be classified as skilled. In situations with physicians and patients where the doctor has to make a life-saving decision, the possibility for such a mistake is unacceptable and can prove to be detrimental.

Doctor-Patient Feedback and Interpretation

AI in the healthcare field is expected to grow rapidly in the years to come, but with growth comes limitations, which is why it is crucial for AI to be implemented into the healthcare system with ethical and legal aspects in consideration. A large imitation is that AI systems do not have feelings and can’t care for or have sympathy for patients in the same way that doctors do. The “quadruple aim” of healthcare consists of improving experience of care, improving health of populations, reducing per capita costs of healthcare, and improving the work life of healthcare workers (Kelly, Karthikesalingam, Suleyman, Corrado, & King, 2019, pg. 1). But healthcare systems are struggling to meet these goals.

The FDA has already cleared close to 40 devices that run off of AI and can be used for medical purposes. one of these is the IDx-DR, a system that can output a screening decision without the help of human interpretation of the image or results. The system then recommends the physician either rescreen or refer them to a specialist (Gerke, Minssen, Cohen, Bohr, & Memarzadeh, 2020, pg. 5). However while AI can improve imaging, diagnosis, and surgery, it will be difficult to manage AI when informed consent is considered. It is a common question whether it is the physician’s responsibility to inform the patient about AI and the way it works, and if the doctors have to even inform the patient at all that AI is being used. Some argue that it doesn’t matter how an AI system reaches its prediction, but more important is if the decision is correct but this can cause an issue in certain cases as many machine learning algorithms are known as “black boxes”, even the inventor does not know how the program reaches its final decision. The datasets being used to train the algorithms also need to be reliable, trustworthy, valid, and effective- the better the training data, the better the accuracy of the AI algorithm. Even after the first model is developed, the program will need further tweaks to be made. This includes data bias. Many AI algorithms have proven that they do have a bias when dealing with ethic origin, gender, age, or disabilities. These biases could lead to false diagnosis and jeopardize the safety of patients by making treatments ineffective (Gerke, Minssen, Cohen, Bohr, & Memarzadeh, 2020, pg.10). If an AI algorithm outputs a recommendation for treatment that a human physician would not have picked, therefore making it wrong, and the physician decides to use it anyways and it harms the patients, then it is likely that the physician is at fault for medical malpractice. It is also important to think about hospitals and if it becomes their fault that they bought and applied the AI systems to their practice, but this is why AI should be used more for assistance with decision making, rather than fully depended on.

For patients suffering from kidney failure, or end-stage renal disease, renal transplants are the best option for a patient to survive, yet dialysis is often a more reasonable choice due to the shortage of organ donors. However, currently dialysis software is not equipped to respond to unanticipated events that can occur during dialysis treatment. Miniature artificial kidneys that can provide a personalized dialysis treatment and are capable of real-time monitoring are currently in the process of being developed (Hueso et al., 2018, pg. 5). Data analytics supplied by fields of artificial intelligence such as machine learning and computational intelligence are expected to principally play a role making sure these new dialysis technologies are both efficient and safe for patients. Due to the complexity of the technologies involved in the creation of these dialysis machines, there are challenges for implementation of the devices in healthcare and biomedicine. Data analytics along with AI provide the baseline for medical decision support systems, but application of these AI algorithms in the medical field has its challenges, the biggest being the ethical issue (Hueso et al., 2018, pg. 2). Although this device will make the jobs of medical professions easier, it may make interactions with patients uncomfortable and lose trust in their doctors. For example, these automated devices do not have the ability to explain the reasoning for their decision and empathize with patients the same way that doctors can, making it hard for patients to understand their own course of treatment. AI algorithms are not able to express the relationship between the data they have observed and the outcomes that have been formed as a result of it. 

While it is possible for an algorithm to overcome all of these challenges, human-computer interactions are one of the key aspects in gaining a better understanding of the way algorithms interpret data. Multiple algorithms produce great results but lack the ability to explain why they landed at those particular results. Even if scientists are able to understand the math that is involved in creating the algorithm, it is virtually impossible to determine which model made a specific decision. This is problematic as it has rendered many algorithms as untrustworthy, uninterpretable and unexplainable. Therefore there is a tradeoff between performance and expandability. Deep learning models have a very high level of performance but they are hard to interpret while linear regression models decision trees are relatively to interpret but have poorer decision making skills. Interpretability of AI algorithms is the ability of a human to understand the way it made a connection between features extracted and its predictions. Approaches to solving this interpretability issue can be categorized into two categories: whether they need internal information such as parameters to operate (also known as the level of transparency) or the amount of accessibility there is to the internal information of the model. Methods that require access to the internal information are considered to be working on “white boxes” (Reyes et al., 2020, pg. 2). An example of this is a CNN or convicted neural network where a radiologist uses a given layer of network to create a map which can be laid on an image that shows which regions are important for predicting if the patient has a disease. Black boxes on the other hand do not need access to this internal information and instead work directly with the input and output of the model to analyse how changes to the input will change the output. There are multiple visual techniques that give insights into the way that AI algorithms behave and they arrive at certain decisions. Two of these basic approaches are partial dependence plots and individual conditional expectation plots. Both methods are used to interpret black-box models and show the way a model’s predictions are dependent on the features. This helps predict which features will change the prediction when their value is changed (Reyes et al., 2020, pg. 2).

The goal of interpretability isn’t exactly to understand exactly how an AI system works, but to have enough information to understand it to the best extent possible and it is not always necessary. A wrong diagnosis in radiology can lead to extreme consequences for a patient, but reading images is prone to interpretation errors. Interpretability is a fast evolving field that has been at the center of AI research with great potential for future development of safe AI technologies (Kelly, Karthikesalingam, Suleyman, Corrado, & King, 2019, pg. 1). But before AI can be implemented into various tasks within radiology, task-specific interpretability solutions are required, and if algorithms known as “black boxes” are used in medicine, they need to be used with a great deal of judgement and responsibility. AI developers should be aware of the many consequences of algorithms and unintentionally lead to and make sure they are created with all patients in consideration. Doctors and surgeons being involved in this process can increase its efficiency significantly. If the interpretability of algorithms can be improved, then human-algorithm interaction would be smoother and the future adoption of AI with consideration of data protection, fairness and transparency of algorithms, and safety, would be supported by a large number of physicians.

The Impact of AI on Jobs

While many physicians may support the implementation of AI, no machine can work at its full potential without the presence of doctors, but studies have shown that medical students are not spending enough time getting acquainted with newer technologies that involve Artificial Intelligence. Currently, medical education is centered around 6 major areas: medical knowledge, communication skills, patient care, professionalism, practice based learning, and systems- based practice (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019). Most of this training focuses on taking in large amounts of information and applying this information to patient care- a process based mostly on memorization. In order to improve outcomes in clinical settings, students need to learn how AI functions and how it can augment their work. The many promises of AI include automated image segmentation, detection of cancerous lesions, and comparison of images. While it can be fatiguing for human pathologists to detect small traces of cancer on a slide, AI systems are not affected by this and can scan a number of slides without losing their accuracy. AI can also help physicians to improve the quality of patient care by taking care of repetitive tasks and tedious tasks and managing large amounts of data, in addition to being another opinion for making decisions.

With AI algorithms showing such great amounts of promise in radiology, pathology, and cardiology, a question that arises is while AI algorithms replace human physicians? Recent data expresses that when considering its image and predictive analysis, AI might soon prove to be more efficient than radiologists. However, it is likely that AI will not replace the role of general physicians, but rather augment them. For example, an AI system is able to take over the job of a factory worker who performs a certain task repeatedly, but in the case of replacing medical professionals, AI is unable to engage in interactions with patients that are crucial in gaining their trust, and restoring them. One study on this topic deals with breast cancer. It suggests that digital mammography is not perfect and only has a sensitivity of around 85%. The other 15% that is not detected is a result of human error- what radiologists are able to identify on scans. Furthermore, the question of whether or not this practice is ethical is an important one. While replacing human workers with AI systems, it may benefit the economy as a whole, but the effect that it has on individuals whose jobs have now been taken away, is detrimental.

In most cases, technology is designed to perform a specific task which changes the demand that workplaces have for certain skills. These changes can influence the skill requirements, social well-being of workers, and career mobility, for different occupations. Limitations on data to train AI algorithms will restrict these skill pathways, but scientists can surmount this obstacle by prioritizing data collection that is detailed and responsive to real-time changes in the labor market (Frank et al., 2019). This improved data collection will enable the use of new tools that rely on data, including machine learning systems that more accurately reflect the complexity of labor systems. New data will also lead to new research that will strengthen our understanding of the impact of technology on the supply of and demand for labor

However, AI systems do tend to result in a number of false positives, resulting in extreme measures being taken without certainty of harmful cancer presence in the body. This is where radiologists are still crucial in the medical field, even with the presence of AI. Furthermore, false positives are an issue which can lead to anxiety, along with unnecessary biopsies and tissue removal(Ahuja, 2019). AI has the potential to assist and augment physicians rather than replacing them entirely by combining data and providing help in the decision making process by recommending certain treatment options. AI can also remove some of the burden of work from physicians by performing tedious tasks. Speech recognition is another useful device that can replace keyboards and decision management can help physicians to make more informed decisions that take into account both patient outcome and cost of treatment (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019).

Figure 5. As AI continues to develop, the impact it has on jobs will grow increasingly. This is because AI algorithms have a set of skills which humans do not. For example, AI algorithms will never get tired, they are able to perform tedious tasks without the quality of the job going down. AI algorithms are also able to detect tumors in a way the human eye cannot and can perform calculations much faster than the human brain. All of these skills allow algorithms to make informed decisions and predictions. Because of their ability to make informed decisions, AI has begun to take the jobs of receptionists and programmers who do tedious work. AI also has the potential to take the jobs of physicians however the lack of doctor-patient interactions may make patients uncomfortable in a hospital setting. Taking jobs away from human laborers will also have a devastating impact on the US economy in the long run.

Ethical Considerations

Due to the fact that the field of AI is a relatively new form of technology, it’s implementation in the real world raises a number of questions about the ethical side of the technology. Within the AI algorithms themselves, one prominent issue is model bias. The data that is being used to train such algorithms has the potential to be influenced by multiple outside factors including a bias towards the humans who collected the dacollectors of the data. As a result, an algorithm may be biased towards a specific group when predicting whether or not an individual should receive a certain treatment. It is important for researchers to consider this aspect of AI and work towards mitigating the effect of such biases. Data not representative of a large population can result in a model that is biased to subjects highly prevalent in the data set. In addition, for the highest level of fair and accurate model performance, it is imperative for scientists to split data so that platforms can be tested with images separate from the training data.

The first issue that seems to directly influence patients is how although AI may not entirely replace doctors, it will significantly alter relationships between patients and their physicians and nurses. Many companies that distribute electronic health records however have overlooked this disadvantage and have focused on only the positive aspects including how AI will be able to simplify interactions with complex data and reduce the time it takes to complete tasks. However to many patients, it is incredibly important for their own comfort and satisfaction to maintain relationships with their doctors. If AI algorithms are set to take over scheduling appointments, making payments, and even running follow-up visits, then this doctor-patient interaction time will be compromised. Furthermore, it is important to take into account the immense amount of data that algorithms require access to for training. While the majority of companies are sure to keep their data protected in order to abide by HIPAA, a privacy law that creates national standards to protect personal medical data, some organizations do allow their data to move freely in and out of their company. This sacrifices patient privacy and security in a way that didn’t have much of an effect before AI was implemented into medicine. Finally, the legal responsibilities that come along with having a hospital run by AI are vast. For any negative consequences that could have been fixed previously, oftentimes at first glance it seems that it is the responsibility of the provider of the AI algorithm. Providers do need to be certain that their algorithms they are providing to hospitals use relevant and accurate data that can make decisions in the most beneficial way possible, but questions surrounding this topic remain unanswered. One could also argue that negative outcomes are the doctors’ fault because they relied too heavily on an algorithm instead of using their own expertise to make a decision. In the end, it is the responsibility of contributors- providers of AI, developers of AI, patients, doctors, and all others involved in the process- to make sure artificial intelligence develops in the medical field in a safe and ethical manner.


Through its multitude of uses across the field of medicine and oncology specifically, AI has the potential to transform the way physicians work, and the way patients are treated. Within cervical cancer, lung cancer, and breast cancer, AI algorithms are able to detect lesions in scans and use segmentation techniques to separate masses within such an image and identify cells based on their structures. Furthermore, they have the ability to utilize these images and extracted features in order to classify images and predict the possibility of recurrence and even chance of patient survival. There are currently a number of platforms being developed which can perform these tasks and a number more being developed to teach medical students how to interact with technology and practice their skills in a real life setting before actually doing so. In the future, AI is set to enable faster and more accurate diagnosis, reduce human errors that are a result of fatigue, complete repetitive and tedious labor taste, decrease medical costs, perform minimally invasive surgery, and increase survival rates. Specific examples of  prospective applications for AI as the field grows, are in analyzing relationships between patient outcomes and treatment administered to patients, diagnosis, forming protocol for certain treatments, personalized medicine, and patient care. However, despite these fascinating advancements in technology and medicine and the tremendous potential AI has to revolutionize medicine, there are still some things that AI is not able to accomplish. It lacks the ability to have social interactions with patients in a way that humans doctors can, and will continue to take jobs from employees around the world as its role expands. In order to surmount these obstacles, scientists will have to consider how far patients are willing to go in regards to putting trust in their doctor as well as the economic impact of AI and how it could in turn harm the economy by taking away large numbers of jobs. In order to improve success of AI in the fields of cancer detection, diagnosis, and treatment, these factors must be taken into account.

Sia Shah, Youth Medical Journal 2021


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Biomedical Research

Why Is the Discovery of New Antibiotics So Difficult?

By Adaora Belonwu

Published 5:49 PM EST, Thurs June 3, 2021


Selman Waksman first used the word antibiotic as a noun in 1941 to describe any small molecule made by a microbe that antagonizes the growth of other microbes. Nearly 80 years later, as of 2019, 123 countries reported the existence of extensive multi-antibiotic resistant tuberculosis. Furthermore, a year prior to this, Isabelle Carnell-Holdaway a cystic fibrosis sufferer was put in ICU after an aggressive infection of Mycobacterium abscessus, a relative of tuberculosis, spread to her liver putting it at risk of failure. With no new classes of antibiotics discovered and available for routine treatment since the 1980s, she was left with a 1% chance of survival. However in under two years, Isabelle went from receiving end-of-life care to preparing to sit her A-levels and learning to drive. It had taken an experimental bacterio-phage therapy treatment instead of antibiotics to save the life of a girl with a seemingly untreatable bacterial infection. This article will explore the factors responsible for hindering the discovery of a possible antibiotic that could have treated Isabelle: antimicrobial resistance, their misuse and the brain drain in research and development due to a failure of sufficient financial incentive for pharmaceutical companies.


The first antibiotic was discovered by Alexander Fleming in 1928. Nearly 100 years later, we now have over 100 different antibiotics available which fit into one of two categories: bacterio-static and bactericidal. The former slows the growth of bacteria by interfering with the processes the bacteria need to multiply, and include nucleic acid synthesis and enzyme activity and protein synthesis. The latter, with the example of penicillin, works to directly kill the bacteria for example by interfering with the formation of cell walls.  

Resistance Mechanisms

The main problem that made Isabelle’s treatment so difficult was resistance. Bacteria are termed drug-resistant when they are no longer inhibited by an antibiotic to which they were previously sensitive. At the moment an estimated 700,000 people are estimated to die each year from drug resistant infections, a statistic projected to rise to 10 million by 2050. This resistance can present itself in one of four ways. First, the bacterium can reduce intracellular accumulation of the antibiotic by decreasing permeability and/or increasing efficiency of efflux pumps to pump the antibiotic away. For example, the determinants improve efflux pumps located in the surface of bacterial cells, improving their ability to remove tetracycline. Second, resistance can occur in the method of alternating the target site of an antibiotic that reduces its binding capacity and thus its uptake. An example of this would be the OprD proteins. These are porins, meaning they mediate the uptake of molecules and preferentially block drugs like Imipenem. Moreover, other bacteria can acquire the ability to inactivate or modify the antibiotic. Penicillin’s efficacy can be undermined by the release of beta-lactamase. This is an enzyme produced by the target bacterium which essentially renders penicillin’s action on cell wall synthesis useless. Finally, bacteria can also modify metabolic pathways to circumvent the antibiotic effect. Quinolones target bacterial gyrase genes associated with the supercoiling of DNA. Under normal circumstances when the gyrases are inhibited, the DNA is unable to reorganise itself during cell division. A mutation in a gyrase gene allows for cell division to go on as normal but diminishes the effect of quinolones. Thus, one reason that makes the discovery of new antibiotics so difficult is because bacteria are equipped with several different mechanisms that encode and develop methods undermining the fundamental ways that antibiotics work.

How Is Resistance Acquired?

Resistance arises through the mutation or sharing of DNA using mobile genetic elements. The latter can occur in one of three ways. One way is through the use of viral mobile genetic elements during transduction. This happens when bacterial DNA is accidentally packaged in a bacteriophage capsid after infection. If this capsid binds to a recipient cell, and injects the foreign DNA, leading to the successful recombination of the donor DNA into the genome of the recipient, the bacteriophage can help transfer resistance genes. Another way this transfer can occur is through the use of plasmids during conjugation. Plasmids are extrachromosomal loops of DNA that replicate independents of the bacteria’s genophore and can be transferred when physical contact is made between two cells, followed by the formation of a pilli bridge that enables the transfer of a plasmid (which may also contain a gene for antibiotic resistance). Finally, resistance genes can also be transferred horizontally during transformation. Several antibiotic resistant pathogens are capable of this process, including Escherichia and Klebsiella which are leading causes of antibiotic resistant infections acquired within hospitals. The process of transformation happens when genes are released from nearby microbes and are taken in directly by another. This means that a single bacterium can also lead to other bacteria, previously sensitive to antibiotics, to inherit these mutations without needing to be direct offspring, perhaps ensuring that the whole microbial community is protected from the antibiotic, rendering them useless.

As aforementioned, the reproduction of the mutant resistant bacteria is also paramount in understanding the difficulty of new antibiotic discoveries. Resistance is an adaptation that occurs as a result of directional selection. When antibiotics are introduced into a community of bacteria, a selection pressure is created. Due to initial extensive genetic variation, there will be some bacterial species that inherently have alleles, allowing for resistance, which allows them to survive, to reproduce, and pass on the alleles that code for resistance to their offspring. Those without the allele for resistance die off. Thus, resistance becomes a selective advantage, and the allele frequency increases within the population. In ideal conditions, some bacterial cells can divide by binary fission every 20 minutes. Therefore, after only 8 hours, an excess of 16 million bacterial cells carrying resistance to a given antibiotic can be produced: in the wrong hands, a new antibiotic could be rendered useless overnight. For contrast, millions of years of evolution occurred before primates emerged with an enzyme that could efficiently digest alcohol, and even with this useful mutation, alcohol poisoning is still currently a problem, with alcohol-specific deaths in the UK reaching 11.8 deaths per 100,000 people in 2019. Thus, another reason that can be attributed to the difficulty of antibiotic discovery is the basic biology of bacteria which allows them to adapt to selection pressures and evolve at an exponential rate. 

Exacerbating Factors

Contextual scenarios in which antibiotics act as a selection pressure is not limited to its use in treating infections in patients, but also within the agricultural industry–  something which is becoming a growing hindrance to the efficacy of existing antibiotics and responsible for the rise of superbugs such as MRSA. According to research published by Public Health England, more than 20% of antibiotics prescribed in primary care in England are inappropriate (i.e used in cases where unnecessary, such as treating viral infections).* This statistic demonstrates the need for antimicrobial stewardship in a society that treats this marvel of biology as a limitless commodity. Furthermore, there is a strong link between increasing rates of antibiotic prescription and emergence of resistant bacteria meaning that there is an increasing need for more narrower spectrum drugs to prevent a complete antimicrobial apocalypse. 

Linking to this, our dependence on the use of extremely narrow spectrum potent antibiotics is being threatened by the agricultural industry. According to statistics from the UN’s Food and Agriculture Organisation, at any one moment around 20 billion animals are being kept as livestock. To keep maintenance costs cheap, they are often kept in unhygienic and extremely small, cramped spaces: the optimum breeding ground for disease. Antibiotics tend to be used as a catch-all to both treat illness in some and act as a prophylaxis in others. This system has led to increasingly more bacteria that are resistant to antibiotics. Though there are strict rules stipulating the rules of using strong antibiotics against already resistant bacteria to counteract this, it is not enough to keep up with the growing disparity between resistant bacteria and the development of antibiotics against them. In late 2015, China reported the existence of bacteria displaying resistance against colistin. This was a surprise, as the drug was rarely used (as liver damage is a common side effect) up until this point existing only as a good last resort option for complex infections occurring in hospitals. The resistance to colistin came about as a result of millions of farm animals in Chinese pig farms being given colistin over the course of many years. As aforementioned, this acted as a selection pressure, eventually leading to the increase in pigs carrying colistin resistant bacteria, and eventually crossing over to humans through the food chain. Therein lies a huge threat to the discovery of new antibiotics: finding a balance between mitigating side effects to allow safe use in humans and finding one strong enough to deal with strains already resistant to those that are almost too unsafe for human use.


One reason for the decline in antibiotic discovery is a lack of financial incentive for pharmaceutical companies. To refer back to Isabelle’s case, phage therapies are considered to be approximately 50% cheaper than antibiotics. Furthermore, as mentioned in a Ted talk by Gerry Wright, antibiotics have become so unprofitable that only 4 major pharmaceutical companies still have active antibiotic research programmes. Profit margins for antibiotic discovery are low in a pay-per-pill system since good medications will only be used once and in circulation with other ones to combat the possibility of resistance in the long term. As a result, production of treatments to regulate cancer or muscular-skeletal disease symptoms are most prominent in pharmaceuticals due to their repeated, long term use.

FDA drug approvals by classification 2020, courtesy of Nature Reviews, Asher Mullard 

 In an attempt to shift profits away from the volume of medication sold, in June 2020, UK Health Secretary Matt Hancock announced the adoption of a ‘Netflix Subscription Model’. This scheme attempts to tackle the growing global health threat by de-linking incentive payments to pharmaceutical companies from sales, offering guaranteed income for innovative treatments. Similarly, Germany has implemented a process where higher prices will be awarded for particularly important antibiotics. However, even if incentives such as these help to create new antibiotics, another pivotal question remains: how to ensure that existing and new medicines get to patients in low and middle income countries. With almost 2 billion people without access to antimicrobial treatments (LEDCs being disproportionately represented), failure to improve access for antibiotics, will limit efforts to tackle resistance everywhere.


In summary, the rate of emergence of new pathogenic bacteria greatly surpasses that of antibiotic development. As stated previously, the leading factor behind this issue is the versatile methods bacteria use to develop and spread resistance, something excavated by overprescription and misuse in the agricultural industry. Furthermore, the current economic model for the pharmaceutical industry doesn’t provide enough financial incentive to motivate enough companies to invest in innovations aimed to aid and tackle this problem, leading to some governments potentially turning to an alternative where they “pay more to use less”. 

Adaora Wu, Youth Medical Journal 2021


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Dedrick, Rebekah M., et al. “Engineered Bacteriophages for Treatment of a Patient with a Disseminated Drug-Resistant Mycobacterium Abscessus.” Nature Medicine, vol. 25, no. 5, 2019, pp. 730–733., doi:10.1038/s41591-019-0437-z.

England, Public Health. “Research Reveals Levels of Inappropriate Prescriptions in England.” GOV.UK, GOV.UK, 27 Feb. 2018,

Gallagher, James. “Phage Therapy: ‘Viral Cocktail Saved My Daughter’s Life’.” BBC News, BBC, 8 May 2019,

Lerminiaux, Nicole A., and Andrew D.s. Cameron. “Horizontal Transfer of Antibiotic Resistance Genes in Clinical Environments.” Canadian Journal of Microbiology, vol. 65, no. 1, 2019, pp. 34–44., doi:10.1139/cjm-2018-0275.

Myszka, Kamila, and Katarzyna Czaczyk. “Mechanisms Determining Bacterial Biofilm Resistance to Antimicrobial Factors.” Antimicrobial Agents, 2012, doi:10.5772/33048.

Plackett, Benjamin. “Why Big Pharma Has Abandoned Antibiotics.” Nature, vol. 586, no. 7830, 2020, doi:10.1038/d41586-020-02884-3.

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Shaunacy Ferro. February 19, 2013. “When Did Primates Learn To Metabolize Alcohol? A Chemist Reenacts Drunk History.” Popular Science, 26 Apr. 2021,

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“Http://” 2016, doi:10.18411/d-2016-154. HM Government

Tackling antimicrobial resistance 2019-2024 – the UK’s five-year national action plan.2019 (Available at:)

Biomedical Research

Artificial Blood-A Mystery Soon to be a Reality

By Pratiksha Baliga

Published 3:03 PM EST, Tues June 1, 2021


Blood is a transport liquid pumped by the heart to all parts of the body and vice versa to repeat the process. It is also a tissue with a collection of similar specialized cells that serve particular functions. It is composed of red blood cells for transportation of oxygen and carrying back of carbon dioxide to exhale, white blood cells to fight infections, platelets to heal by clotting of blood in injuries, and plasma for circulation of platelets and blood cells in the body.

Artificial blood is a blood substitute used to mimic and fulfill some vital functions of the biological blood like the transport of oxygen and carbon dioxide. It is useful during life-sustaining conditions with serious blood loss. They cannot carry out secondary functions like fighting infections. Nowadays with the growing need for blood and reduced availability of donors, the creation of artificial blood is a need for millions. 


Human blood has a composition of products that need to be added to make artificial blood. First and most importantly it should ensure compatibility thus in this case the types of blood should not vary and be similar for all. It should also fulfill the purpose of the patient’s safety, to be able to process and remove the disease-causing agents like microorganisms, bacteria, and viruses, being pathogen-free. Secondly, the important aspect is regarding the transportation of oxygen and carbon dioxide. This feature is considered to be fulfilled by recent research. Third, it must be shelf-stable. Human blood can only be stored for a relatively short period. According to the Red Cross, storage of blood cells is done in the refrigerator at 6°C for 42 days, while platelets can be stored at room temperature in agitators only for 5days. So unlike donated blood, artificial blood should be able to be stored for at least a year or more. 


Perfluorocarbons are a group of human-made chemicals composed of carbon and fluorine. They are thought to be used to design artificial blood and dissolve about 50 times more oxygen than blood plasma. They are inexpensive and do not require any biological materials. In making it useful for artificial blood ongoing research is in talking terms in which certain hurdles have to be overcome. Firstly, correction in its solubility in water has to be brought about, which can be achieved by its combination with emulsifiers that can suspend its tiny particles in the blood. Secondly, the quantity of Perfluorocarbons has to be large as they carry much less oxygen than hemoglobin-based products. Thus improved emulsions will be developed in the subsequent time, the process of which has begun. 

Haemoglobin Based Products

Hemoglobin carries oxygen from the lungs to the other body tissues. Artificial blood made according to this principle has an advantage due to its natural function. Unlike in Perfluorocarbons, the oxygen covalently bonds to hemoglobin. It has another advantage of eliminating blood typing as they aren’t contained in a membrane and are different from the whole blood. Along with having the pros, hemoglobin-based also has some cons. Firstly, stability is an issue.

Secondly, its raw material cannot be used as it would lead to breaking down into small toxic products inside the body. Therefore artificial blood hemoglobin-based should be made by resolving these issues. The stability of it can be brought about by chemically cross-linking or using recombinant DNA technology to produce modified proteins.

The cross-linking has specific chemical cross-links which prevent dissociation to dimers or monomers. Therefore these hemoglobins attain the properties of solubility and stability. Thus it is expected that these modified ones should have a greater ability to carry oxygen than our red blood cells. The research is going on and its availability is expected to be within some years.

Process in Making

Perfluorocarbons involve polymerization reactions for the making of artificial blood. Hemoglobin-based products are mostly preferred which can be used by isolation or synthetic production with the use of amino acids. It also uses specific types of bacteria and materials needed to incubate it like warm water, glucose alcohol, urea, acetic acid, and liquid ammonia. Later the process involves molecular modification followed by reconstitution in an artificial blood formula. A strain of E.coli bacteria is taken and in three days the harvesting of protein is done along with the destruction of the bacteria. 

Then starts with the fermentation process where bacterial culture is transferred to the test tube containing all the required nutrients for growth. This step results in bacterial multiplication and transferring to a seed tank which later is transferred to a fermentation tank. This leads to the production of hemoglobin after which the tank is emptied and the isolation process begins. Here the hemoglobin is isolated with the centrifugal separator and purified by fractional distillation. Lastly from here, it is transferred for final processing. It’s mixed with water and electrolytes for the production of usable artificial blood which is later pasteurized and packaged.


The various types of artificial blood are restricted to certain limitations. Researchers and scientists are also coming up with the idea of using hematopoietic stem cells for the production of artificial blood. This is said to have similar morphology with a similar amount of hemoglobin as the natural red blood cells. The Food and Drug Administration is examining the safety of this blood along with its cost-effectiveness. It may also serve as a blood donor to all common blood types. Along with all this currently, many companies are working on the production of safe and effective artificial blood by clearing the various limitations. In the future, it is anticipated that new materials to carry oxygen in the body will be found. Additionally, long-lasting products will be developed, along with the products that perform other functions of the blood effectively. Hopefully, investigations carried out will be beneficial in the upcoming times.

Pratiksha Baliga, Youth Medical Journal 2021


(1)Patrick Davis, C. (2020, July 17). What Is Artificial Blood and Why Is it Used? MedicineNet.

(2)-, -. (2019, October 29). Artificial Blood: Unsolvable Biological Puzzle Or Soon-To-Be Reality? The Medical Futurist.

(3)Sarkar, S. (2008, July). Artificial blood. Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine.

Biomedical Research Neuroscience

‘Patient H.M’ – An unsung hero: The forgotten man who forgot everything

By Asmita Anand

Published 4:40 PM EST, Sun May 23, 2021


In recent decades, scientists have made huge progress discovering how our identities, and memories are made and stored. A patient that transformed our understanding of the way  memory functions are organised in the human brain, is  referred to as ‘the man who couldn’t make memories’; Henry Molaison possessed one of the most famous brains worldwide and bestowed unique insights into the inner-workings of human brains.

Who Was He?

Figure 1: HM in 1953 before his surgery (

Henry Gustav Molaison, also known in medical literature as patient H.M. to protect his identity, was born on February 26, 1926 in Manchester, Connecticut.

As a child, he had a relatively normal childhood. Although it wasn’t long after a minor head injury and a family history of seizures (although the exact aetiology behind his seizures remains uncertain), that Molaison began suffering from severe epilepsy. At the age of 10, he started having absence seizures and 6 years later he developed generalised tonic-clonic seizures. His seizures greatly impacted his daily life and led him to drop out of high school. Later he was also unable to maintain his job and function independently. Molaison’s case was so severe that it couldn’t be treated pharmacologically with high doses of anticonvulsant medication.

After nearly 10 years he turned to Dr William Scoville, a renowned daredevil neurosurgeon of his time, with hope to lead a normal life once again. At the age of 27, his hippocampus was removed in an experimental procedure in an attempt to alleviate the impact his seizure had on the quality of his life. He underwent a ‘bilateral medial temporal lobectomy’, which surgically removed the medial temporal lobe on both sides of his brain. This included the hippocampal complex, parahippocampal gyrus, the uncus, the anterior temporal cortex, and the amygdala, according to Scoville’s own illustrations of his surgical technique. However in around 1992-199, MRI scans revealed that the surgery was less extensive than he thought, but enough to cause the damage it did. [1]

Figure 2: Diagram depicting HM’s brain after surgery compared to a normal human brain (

Although Dr Scoville hoped it would cure the epilepsy, he still wasn’t completely sure whether it would be successful or if there might be any long lasting side effects of this procedure. As a result, both of his thoughts were correct. Molaison’s seizures had stopped but unfortunately he was also left with long term memory loss, leaving him constantly living in the present tense. Later Scoville admitted that the operation was a tragic mistake and has spoken strenuously about the dangerous implications of bilateral mesial temporal lobe surgery.

Different types of Amnesia

There are multiple types of amnesia, such as Retrograde, Anterograde, Transient global and Infantile amnesia. Retrograde amnesia is when someone is unable to recall events that occurred before the development of the amnesia and is commonly used in films and media. [2] Whereas anterograde amnesia refers to a decreased ability to retain new information and is typically caused by brain trauma. [3]

Molaison developed a peculiar form of amnesia and suffered from both partial retrograde amnesia and anterograde amnesia. The latter meant he lost the ability to form new memories, such as the inability to remember what he had eaten for lunch that day, tests that he just done minutes before and names he had just been introduced to. Scoville wrote: “After operation this young man could no longer recognise the hospital staff nor find his way to the bathroom, and he seemed to recall nothing of the day to day events of his hospital life. There was also a partial retrograde amnesia.” [4] This meant that while he could recall memories from his childhood, he was unable to remember almost 11 years of events prior to the operation. 

However, both his personality, intellectual abilities and perception remained unaffected and his IQ increased from 104 to 117. [5] Molaison still had the ability to form non-declarative memories, allowing him to still acquire and develop motor skills, which led to Brenda Milner’s discovery of the distinction between procedural and declarative memories. While his mind became like a sieve, through other testing performed by Milner she discovered that he still possessed short term memory. This led to the notion that this too existed in a separate brain structure to the one he lacked.

Short Term and Long Term Memory

Molaison’s misfortune ended up as a milestone in our understanding of the brain as up until it occurred memory wasn’t thought to be localised in one area of the brain. Dr Scoville and Brenda Milner were the first to make observations and report his case in 1957 in the “Loss of recent memory after bilateral hippocampal lesions”. Since he had difficulty remembering doing the tests in the day, Molaison never grew tired of the numerous experiments he partook in.

It is thanks to Molaison, that today we know that intricate functions are directly connected to distinct regions of the brain. The hippocampus, which is embedded deep into the temporal lobe, plays an important role in forming, retaining, and recalling declarative memories and spatial relationships. It’s also where short-term memories are turned into long-term memories.

Five decades later, referred to as Patient H.M., Molaison’s case grew in popularity due to the publication, which has thoroughly been cited numerous times. Researchers arrived at the conclusion that short term memory was not connected in any way to the medial temporal lobe structures. A particular researcher out of the 100 who studied him, Suzanne Corkin, spent the most time with Molaison interviewing him and working with him for 46 years. In her book “Permanent Present Tense: The man with no memory, and what he taught the world”, Dr Corkin covers how Molaison’s mind was used to understand how our minds and memory work. It also covers his early life and key childhood memories from their personal conversations or careful reporting and research. She wrote about how she went from viewing him as a “subject” to seeing him as a human being. Molaison’s life was not easy as he often struggled at times. After a while he came to understand that while others could retrieve and store memories, he could not. Nevertheless, he remained positive, coping well with his difficult situation and he acts as a true inspiration for his extreme resilience. H.M. once poignantly remarked that “every day is alone in itself. Whatever enjoyment I’ve had, and whatever sorrow I’ve had”. [6] 

His Legacy

Figure 3: Photography by Spencer Lowell (

Sadly Henry Molaison passed away at the age of 82 due to respiratory failure. Despite his death in 2008, his brain still continues to excite and offer further investigation into memory as there is still much to uncover. Mr Molaison was much, much more than a research specimen but a person who despite facing grave misfortune, still managed to show ‘the world you could be saddled with a tremendous handicap and still make an enormous contribution to life.’ [7] Columbia pictures and Scott Rudin have even acquired rights to develop a film based on his life.

As Dr Corkin described as “a beautiful finale to his enduring contributions”, his frozen brain was cut into 2,401 slices postmortem, which have been photographed and digitised into a high-resolution, 3D model for further anatomical analysis, in which we can even view individual neurons!

Molaison once commented: “It’s a funny thing – you just live and learn. I’m living and you’re learning.” Henry Molaison leaves behind a legacy (quite literally through the preservation of his brain!) which shall be remembered by us all and stay within our own memories. His forgetfulness has revolutionized our understanding of the brain, which we can still learn so much from till this date.

To end, as Dr Corkin said “Henry’s disability, a tremendous cost to him and his family, became science’s gain”.

Asmita Anand, Youth Medical Journal 2021 


[1] Annese, J. (2014, January 28). Postmortem examination of patient H.M.’s brain based on histological sectioning and digital 3D reconstruction. Nature Communications.

[2] I. (2020, November 25). Retrograde Amnesia | Symptoms, Causes, Illness & Condition. The Human Memory. amnesia is a form,that occur after the onset

[3] Cherney, K. (2018, September 18). Anterograde Amnesia. Healthline. amnesia refers to a,is a subset of amnesia.

[4] Lichterman, B. (2009, March 17). Henry Molaison. The BMJ.

[5] Scoville, W. B., & Milner, B. (1957, February). Loss of recent memory after bilateral hippocampal lesions. NCBI.

[6] Loring, D. W., & Hermann, B. (2017, June). Remembering H.M.: Review of “PATIENT H.M.: A Story of Memory, Madness, and Family Secrets”. Archives of Clinical Neuropsychology.

[7] Adams, T. (2018, March 22). Henry Molaison: the amnesiac we’ll never forget. The Guardian.

Halber, D. (n.d.). The Curious Case of Patient H.M. Brainfacts.

Gholipour, B. (2014, January 28). Famous Amnesia Patient’s Brain Cut into 2,401 Slices. Livescience.Com.

Shah, B. (2014b, July 1). The study of patient henry Molaison and what it taught us over past 50 years: Contributions to neuroscience Shah B, Pattanayak RD, Sagar R – J Mental Health Hum Behav. Journal of Mental Health and Human Behaviour.;year=2014;volume=19;issue=2;spage=91;epage=93;aulast=Shah

Hodges, J. R. (2013, November 23). Memories are made of this. Oxford Academic.

Shapin, S. (2017, June 19). The Man Who Forgot Everything. The New Yorker.

Billington, A. (n.d.). Scott Rudin Developing Feature Film About Henry Molaison. FirstShowing.Net. a cue from The,in medical circles as H.M.

Biomedical Research

Preventing Nerve Cell Deterioration After Traumatic Brain Injury

By Kyle Phong

Published 1:46 PM EST, Sat May 15, 2021


Traumatic brain injury (TBI) is often caused by a blow to the head and currently affects around five million people across the US. It is known to cause several neuropsychiatric conditions such as psychosis, mania, and Alzheimer’s disease, and can also lead to nerve cell deterioration. At the Harrington Discovery Institute in Cleveland, Ohio, Dr. Pieper and his team have discovered a way to prevent TBI-induced nerve cell deterioration in the brain.  They also found a possible explanation for the relationship between TBI and Alzheimer’s disease.

Osmosis, “Traumatic Brain Injury (TBI)” 


To explore the connection between Alzheimer’s and TBI, Dr. Pieper used previous knowledge of tau and acetylation in patients.  Tau is a protein in nerve cells that help guide nutrients throughout the neuron.  However, tau tangles with other tau molecules in patients with Alzheimer’s disease, resulting in weak synaptic communication between neurons and becoming acetylated-tau.  While experimenting with mice, Dr. Pieper found high levels of acetylated-tau (ac-tau) in different forms of TBI.  The elevated ac-tau persisted chronically if left without treatment.  Furthermore, patients with Alzheimer’s disease had even higher levels of ac-tau if they had a history of TBI.

Labiotech, “Healthy Neuron vs Alzheimer’s Disease Neuron”

Dr. Pieper’s team found two anti-inflammatory drugs (salsalate and diflunisal) that helped to protect the mice’s neurons from deteriorating after TBI.  These two medications inhibit the enzyme that causes tau acetylation, therefore preventing the transformation into ac-tau.  Upon this discovery, the researchers analyzed over seven million patient records regarding the usage of salsalate and diflunisal and realized that these medications were associated with a decrease in Alzheimer’s disease and TBI cases.  Additionally, they compared these two drugs with aspirin, a common anti-inflammatory drug, that does not prevent acetylation.  Dr. Pieper did not find any evidence of aspirin showing the same neuroprotective activity as salsalate and diflunisal.

Knowing that tau is a protein that diffuses from the brain into the bloodstream, the researchers wondered about ac-tau levels existing in the blood of TBI patients.  For both mice and humans, there was a significant increase of ac-tau in the blood.  However, these elevated levels returned to normal when treated with medications such as salsalate and diflunisal, showing again that they effectively protect nerve cells from deterioration. 


Dr. Rosa, the co-author of this study, explains that this newfound knowledge can have a variety of uses in the clinical setting.  The research team is continuing to examine ac-tau and its relationship with neurodegenerative diseases.  Additionally, they will study salsalate and diflunisal to see whether these drugs can be used as an established neuroprotective medication for humans. 

Kyle Phong, Youth Medical Journal 2021


News Medical Life Sciences, “Researchers discover a new way to prevent brain nerve cells from deteriorating after injury”, 13 April 2021

Cell, “Reducing acetylated tau is neuroprotective in brain injury”, 13 April 2021

Osmosis, “Traumatic brain injury: Clinical practice” Image

Labiotech, “How AC Immune CEO Andrea Pfiefer is Tackling Alzheimer’s Disease” Image, 1 August 2018

Biomedical Research

How Can Music Help?: Exploring Music-Based Interventions for Adolescent Mental Health

By Fatou Yeli Kourouma

Published 10:17 PM EST, Tues May 11, 2021


Poor teen mental health is a prevalent problem in our world, affecting nearly 1 in 5 adolescents globally. Due to widespread structural issues, most cases are not diagnosed, and many teens do not receive treatment. The negative effects of untreated mental disorders can extend long into adulthood [16]. Teens are characterized by their reverence for playing music in cars, using melodies to let go of their existential angst, and having their favorite artists connect them to larger communities. In a situation where external factors make it hard for teens to seek treatment for mental health issues, this review explores music-based interventions as a powerful tool to combat the growing teen mental health crisis.  

The Issue of Teen Mental Health

In recent years, the magnitude of poor mental health in adolescents has become increasingly clear. Globally, nearly 20% of adolescents face a mental disorder, depression and anxiety are leading causes of illness and disability, and suicide is the third leading cause of death in older teens [14][16]. 

The gravity of the situation has been exacerbated by the COVID-19 pandemic, which has particularly affected teens. Since the onset of global restrictions, instances of serious teen mental health claims have doubled and feelings of anxiety and depression have risen, while perceived life satisfaction has decreased considerably [4] [9].

Despite the overwhelming evidence demonstrating the impact poor meal health has on the world’s youth, most cases are left untreated. In the US alone, 50% of adolescents with treatable mental disorders received no treatment in 2016 [11]. These numbers are considerably worse in lower-income countries that do not have strong mental health infrastructures [7]. The undertreatment of mental disorders in adolescence leads to adverse effects long past teenage years. Half of lifetime mental disorders begin in teenage years, demonstrating the importance of proper mental care in those formative times[8].

Barriers to Treatment 

As evidenced by the sheer amount of teens that do not receive treatment for mental health issues, there are many social and environmental barriers that make receiving treatment difficult. Major barriers teens face in receiving professional help include lack of mental health knowledge, fear of embarrassment and stigma, lack of trust of mental health professionals, and the high financial costs associated [13].  

Common symptoms of poor mental health are rarely treated as such in our society, with many touted as “moodiness”. This makes it difficult for many teens to identify their emotions as worthy of professional help. In addition, many teens don’t feel comfortable sharing their deepest problems with someone who is essentially a stranger. Confidentiality with sensitive information is imperative in a relationship with a mental health professional and a teen, but many nearly 68% of teens cited it as a reason they do not seek mental health care [13]. Fear of facing judgment due to mental health stigma in society also discourages teens, with many feeling embarrassed to even seek help. Furthermore, access to mental healthcare and funds to pay for said care is out of reach for many. In many areas around the world, there is a chronic lack of mental health care facilities which makes it nearly impossible to seek help. Another major factor is race and cultural background, with minority youth less likely to receive quality care in countries like the US and the UK [2]. All these factors render it nearly impossible for some teens to seek professional help and contribute to the rising numbers of adolescents with untreated mental disorders.  

Music as Therapy 

By activating areas of the brain associated with memory, triggering the release of “feel-good” neurotransmitters like dopamine, and connecting different areas of the brain, music is considerably therapeutic [6]. Harnessing the inherent healing qualities of music, music therapy is the credentialed use of music to treat a range of mental and physical conditions like Alzheimer’s, PTSD, and depression [1]. 

This unique form of rehabilitation is markedly effective for teens and young adults with mental and emotional disorders.  Along with ongoing treatment, music therapy has been shown to increase self-esteem and lower levels of depression in teens [12]. Moreover, student refugees in North Korea and Australia who had music therapy implemented in their school said they felt they were able to manage stress and anxiety better as a result of the treatment. Music therapy has even been shown to help lower levels of depression in teens with substance abuse issues and reduce symptoms of post-traumatic stress disorder in teens who experienced major trauma  [10].

Music therapy is incredibly unique as it is able to break down many barriers to treatment faced by teens. By not placing a focus on verbal communication, music therapy allows many teens to feel comfortable dealing with their mental health issues and may lessen the fear of stigma [11]. Music therapy is usually done in community and school-based contexts, which greatly increases accessibility and in many cases lowers financial strain.

Even without the use of music therapy, teens can use music to improve their mental and emotional wellness in a number of ways. By playing an instrument, singing, songwriting, and simply listening to music, teens can reap the many benefits of sound. Adolescents have cited a range of social and emotional benefits from singing and making music [3][15].  Songwriting has been shown to increase feelings of social connection and decrease perceived depression in college students  [5]. In addition, teens can use mobile apps like and Tunewell that feature music and exercises curated by board-certified music therapists.


Levels of poor teen mental health are alarming around the world, with numbers seeming to rise. Due to a range of environmental and societal factors, many teens are unable to receive the treatment they need which leads to life-long implications. By raising accessibility, lowering fear of stigma, and limiting financial strain, music therapy interventions may be a viable solution to poor adolescent mental health.

The future of music therapy to help alleviate the mental and emotional strain many teens carry is bright. If implemented in schools, community centers, or other places where teens congregate, group music therapy sessions can be accessible for teens around the world while breaking down barriers that make mental healthcare unreachable for many. Teens around the world need help, and the solution may be closer than we think.

Fatou Yeli Kourouma, Youth Medical Journal 2021


[1] American Music Therapy Association. Music Therapy and Mental Health. American Music Therapy Association. Retrieved: 14/03/2021.

[2] American Psychological Association. (06/2019). Child and Adolescent Mental and Behavioral Health Resolution. American Psychological Association. Retrieved: 15/03/2021.

[3] Bryant, Sharon. (09/06/2014). The Positive Influence of Playing Music on Youth. NAMM Foundation. Retrieved: 16/03/2021.

[4] Fair Health. (02/03/2021). The Impact of Covid-19 on Pediatric Mental Health: A Study of Private Healthcare Claims. Fair Health. Retrieved: 16/03/2021.

[5] Gee, Kate, Hawes, Vanessa, Cox, Nicholas. (05/03/2019). Blue Notes: Using Songwriting to Improve Student Mental Health and Wellbeing. Frontiers in Psychology. 10.3389/fpsyg.2019.00423. Retrieved: 16/03/2021.

[6] Global Council on Brain Health (2020). Music on Our Minds: The Rich Potential of Music to Promote Brain Health and Mental Well-Being. AARP. Retrieved: 16/03/2021.

[7] Kataria, Ishu, Kocher, Erica and Stelmach, Rachel. (08/09/2020). Shining a Spotlight on Adolescent Mental Health in Low- and Middle-Income Countries. RTI International. Retrieved: 15/03/2021.

[8] Kessler, Ronald C  et al.  (07/2007). Age of onset of mental disorders: a review of recent literature. Current Opinion in Psychiatry. Volume 20 Issue 4 page 359-364. Retrieved: 15/03/2021.

{9] Magson, Natasha R et al. (27/10/2020). Risk and Protective Factors for Prospective Changes in Adolescent Mental Health during the Covid-19 Pandemic. Journal of youth and adolescence. DOI: 10.1007/s10964-020-01332-9 . Retrieved: 15/03/2021.

[10] Mcferran, Katrina Skewes. (2019). Adolescents and Music Therapy: Contextualized Recommendations for Research and Practice. Music Therapy Perspectives.   doi:10.1093/mtp/miz014. vol. 38, no. 1, 2019, pp. 80–88. Retrieved: 14/03/2021.

[11] Mostafavi, Beata. (18/02/2019). Half of U.S. Children with Mental Health Disorders Are Not Treated. University of Michigan Health Lab. Retrieved: 14/03/2021.

[12] Porter, Sam, et al. (2017). Music Therapy for Children and Adolescents with Behavioural and Emotional Problems: a Randomised Controlled Trial.  Journal of Child Psychology and Psychiatry. doi:10.1111/jcpp.12656. vol. 58, no. 5, 2017, pp. 586–594. Retrieved: 14/03/2021.

[13] Radez, Jerica et al. (21/01/2021).Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies. European Child & Adolescent Psychiatry. Page 183-211. Retrieved: 15/03/2021.

[14] Unicef (08/2019). Mental Health. Unicef Data . Retrieved: 14/03/2021.

[15] Welch, Graham (02/2012). The Benefits of Singing for Children. University College London Institute of Education. Retrieved: 16/03/2021.

[16] World Health Organization (28/09/2020). Adolescent Mental Health.World Health Organization. Retrieved: 14/03/2021.

Biomedical Research

Replicative Crisis: Mapping Cellular Fates and Identifying Determinants between Cell Survival and Death

By Ania Grodsky

Published 11:00 PM EST, Tues May 4, 2021


In order to divide indefinitely, most cancerous cells activate the enzyme telomerase, which elongates telomeres. To express telomerase, cells typically survive a state known as replicative crisis. Cells in crisis divide abnormally, promoting the generation of chromosomal rearrangements and cytosolic DNA species (micronuclei and chromatin bridges). These anomalies lead the cell towards one of two fates: survival through chromosomal aberrations that promote telomerase activation, or cell death through the detection of cytosolic DNA by cGAS. This mechanism of cell death may be circumvented by the exonuclease TREX1, which degrades cytosolic DNA prior to cGAS activation. Nearly all cells die during crisis; the possibility that a cell will activate telomerase through chromosomal aberrations is extremely small. This work explores two areas critical to understanding crisis-related mechanisms: the generation of chromosomal aberrations and cytosolic DNA species during crisis and determinants of cell death versus survival during crisis. Understanding these domains is critical to understanding the process of cancer cell immortalization.


The first section of this paper will discuss pathways and potential cell fates during crisis. Included in this review is a figure depicting possible stages and outcomes of cells in crisis. In order to understand crisis intermediate steps and outcomes, it is critical to understand the mechanism through which cells enter crisis.

Entering Crisis

Telomeres, the ends of chromosomes, consist of non-coding TTAGGG nucleotide repeats that shorten each time the cell divides. When telomeres become critically short, cellular senescence is initiated by the p53-dependent ATM kinase pathway. This process establishes the Hayflick limit, which states that cells can divide 40 – 60 times before becoming senescent.

However, in cells lacking p53, critically short telomeres cannot execute the ATM kinase pathway to promote senescence (Maciejowski and de Lange 2017). Instead, telomeres continue to shorten to the point that they are physically unable to hold TRF2, a telomeric protein critical to preventing telomere fusions. Chromosomes consequently fuse due to non-homologous end joining pathways. During anaphase, fused chromosomes are drawn to opposite poles of the cell. These chromosomes then pull on each other and form a bridge-like structure known as a chromatin bridge (CB).

This state of fused telomeres and chromatin bridges propels the cell into a state known as replicative crisis, which acts as the final tumor suppressor before cell immortalization. Crisis is best characterized by chromosomal instability; chromosomal aberrations, aneuploidy, and cytosolic DNA species are common (Nassour et al. 2019). 

Ultimately, however, crisis causes mass cell death (Maciejowski and de Lange 2017). Very rarely, a cell survives crisis by activating the tert gene, which encodes the enzyme telomerase. Telomerase adds TTAGGG repeats to the end of the telomere. With this gene active, cancer cells are able to thwart the Hayflick limit and proliferate indefinitely.

In this study, I will discuss various cell fates succeeding CB formation and the impact of these fates on cell survival. I will also review specific chromosomal aberrations that arise during crisis and are linked to telomerase activation

cGAS and TREX1

The second section of this paper will discuss the consequences of cGAS versus TREX1 binding to cytosolic DNA during crisis. Crisis generates cytosolic DNA species such as micronuclei and chromatin bridges (Maciejowski et al. 2015, Nassour et al. 2019). These structures typically have unstable nuclear envelopes prone to rupture (Maciejowski et al. 2015, Hatch et al. 2013, Nassour et al. 2019), consequently exposing DNA to the cytosol and cytosolic DNA-sensing enzymes cGAS and TREX1. cGAS and TREX1 bind competitively (Ablasser et al. 2014) and are critical to determining cell fate.

cGAS most commonly triggers an inflammatory response upon detection of cytosolic DNA by activating STING, which phosphorylates IRF3, ultimately causing the production of type I interferons (Motwani et al. 2019). This pathway is referred to as cGAS-STING. It is important to note that cGAS cannot distinguish between foreign and non-foreign DNA (Chen et al. 2016). In occurrences of invading DNA from agents such as viruses, cGAS may prove useful. However, if self-DNA is released from the nucleus, cGAS may trigger an unnecessary inflammatory response.

To prevent self-DNA from triggering cGAS-STING, the cell employs two defensive mechanisms: RPA and Rad51, as well as TREX1 (Wolf et al. 2019). RPA and Rad51, components of the DNA damage response system, bind to small fragments of ssDNA to prevent them from leaking out of the nucleus through nuclear pores. TREX1, a 3’ exonuclease, quickly degrades any DNA that manages to escape the nucleus. TREX1 is a membrane-bound endoplasmic reticulum (ER) protein (TREX1 – Three-Prime Repair Exonuclease 1); its exonuclease domain is located at the N-terminus and is exposed to the cytosol (Lehtinen et al. 2008), while the C-terminus attaches the protein to the ER (Chowdhury et al. 2006). Because of its location in the ER, TREX1 has near-immediate access to any exiting DNA.

The cGAS-STING pathway has recently been found to promote autophagic cell death in cells undergoing crisis (Nassour et al. 2019). TREX1 therefore promotes cell survival by preventing cGAS activation, as seen in Maciejowski and de Lange 2015 and Mohr et al. 2020. However, TREX1 seems to typically bind prior to cGAS to cytosolic DNA (Wolf et al. 2019, Mohr et al. 2020). It is therefore critical to determine why cGAS-mediated death is widespread during crisis.

In this study, in addition to pathways through crisis, I will discuss potential determinants of cGAS versus TREX1 binding to cytosolic DNA species and will specify topics that require additional research.

Pathways through Crisis

The first critical hallmark of crisis is CB formation. CBs have two possible fates during crisis: breakage and persistence (Maciejowski and de Lange 2017).

Fates Following CB Breakage

Bridges are prone to breakage within 1mb of the telomeres (the location of fusion) and at various fragile sites scattered throughout the genome (Lo et al. 2002, Hellman et al. 2002). CB breakage is most commonly followed by breakage-fusion-bridge (BFB) cycles (Guo et al. 2019). These cycles often result in regional amplifications; uneven breakage causes the now-larger chromosome to contain two copies of the gene(s) surrounding the breakage site. Genes near the ends of the chromosome or near fragile sites are prone to amplification.

Regionally amplified sequences may remain in the DNA, but may also cause double minute (DM) formation (Maciejowski and de Lange 2017). DMs are small, extrachromosomal, circular DNA fragments that lack regulatory elements. DM formation from normally repressed genes, such as tert, provides these genes with a greater chance of expression (the DNA is likely still regulated epigenetically). DMs have three primary fates: replication and further amplification of the gene (Ly and Cleveland 2017), which could enhance oncogene expression; reintegration into other locations in the genome, which causes chromosomal rearrangements; or nuclear bud formation (Fenech et al. 2010). Nuclear buds may detach from the nucleus to form micronuclei (MN). Consequences of MN will be discussed in a subsequent section.
Two mechanisms exist to terminate BFB cycles: break-induced replication and telomerase healing. Break-induced replication translocates the equivalent missing fragment (in size and location) of one chromosome to the chromosome undergoing BFB. These translocations are most frequently nonreciprocal (Murnane 2006) and may cause chromosomal rearrangements that interfere with existing gene regulation by enhancers and repressors. Telomerase healing may also interrupt BFB cycles (Maciejowski and de Lange 2017). Telomerase healing occurs spontaneously upon bridge breakage that results in complete loss of telomeres (Greider et. al 1994) and most commonly results in deletions and loss of heterozygosity (LOH) (Maciejowski and de Lange 2017).

MN may be generated if some fragments of the broken CB are not incorporated into the daughter nuclei. However, MN experience numerous issues with nuclear envelope (NE) assembly. MN only assemble “core” proteins, proteins found within the spindle region during division. “Non-core” proteins, found outside the spindle region do not assemble. The nuclear pore complex fails to assemble as well (Liu et al. 2018). Hatch et al. 2013 reported Lamin B1 deficiencies in MN and attributed the issue to an incomplete nuclear envelope formation process. Lamin B2 and A/C deficiencies have also been discovered (Petsalaki et al. 2016, Nassour et al. 2019).

MN NE instability cannot successfully be resolved by primary NE repair mechanisms. In primary NE repair, ER tubules/sheets spread around chromatin at the ruptured area to form a new NE, but in MN, these tubules/sheets physically invade the chromatin (Hatch et al. 2013). Because TREX1 is bound to the ER, it is able to interact with the chromatin upon invasion. This interaction is dependent on TREX1’s c-terminus anchorage to the membrane; TREX1 with mutated c-terminal domains do not degrade MN DNA (Mohr et al. 2020). TREX1 consequently degrades the DNA. 

Chromothripsis (chromosome fragmentation and random, highly error-prone repair of the resulting fragments) has been reported in MN (Zhang et al. 2015), but chromothripsis in MN has not been linked to TREX1. TREX1 is known to cause chromothripsis in CBs (Maciejowski et. al 2015). Based on Mohr et al. 2020’s and Maciejowski et al. 2015’s observations, it is likely that TREX1 causes chromothripsis in MN. Still, this finding has not yet been formally reported. If this theory proves to be correct, the post-chromothriptic MN may then be re-incorporated into the primary nucleus, where transcriptional activity on these chromosomes may increase (transcriptional activity in MN is highly disputed [Guo et al. 2019]). 

However, if TREX1 fails to degrade the exposed DNA, cGAS binding and cGAS-STING activation may occur. As observed in Nassour et al. 2019, this has the potential to lead to death through autophagic cell death.

If the MN resulting from CB breakage have substantial lamin (which is possible, although less likely), the MN may persist, move to another cell, re-incorporate to the nucleus, or undergo chromosome pulverization and chromothripsis (Hintzsche et al. 2017). Researchers have reported conflicting information regarding the transcriptional activity in MN, and it is therefore unclear to what extent micronuclear DNA is transcribed (Guo et al. 2019). Persistence of MN may therefore affect the cell as do deletions and LOH or may have little impact at all. The impacts of MN relocation between cells are also dependent on transcriptional activity. Re-incorporation has little impact unless the MN is re-incorporated into a new chromosomal location, where it could interfere with normal enhancer and repressor behavior.

Chromosome pulverization occurs when cell cycle signals from the primary nucleus influence the cell cycle process within MN, forcing MN to move forward in the cycle no matter their state of readiness (Guo et al. 2019). This results in premature chromosome condensation, which leads to chromosome pulverization. Chromothripsis follows, and the resulting fragments undergo error-prone repair that essentially randomly stitches fragments together. These scrambled chromosomes can be reincorporated into the primary nucleus during mitosis exit, leading to drastic genome rearrangements within the primary nucleus.

It has been reported that CB breakages lead to MN formation 70% of the time (Hoffelder et al. 2004). However, it has also been found that CB bridges lead to MN formation only 12.66% of the time (Rao et al. 2008). Although these numbers vary greatly, it is highly likely that cells in crisis will at some point form micronuclei. In the Nassour et al. 2019 simulation of crisis, IMR90 cells divided approximately 27 times (45-day crisis duration, approximately 40 hour cell doubling time [JCRB Cell Bank 2015]). Although Nassour et al. 2019’s crisis was simulated, the number of cell divisions is most likely not extremely different during actual crisis. It is therefore extremely likely that MN will be generated at some point (and multiple times) throughout crisis divisions.

Fates Following CB Persistence

Instead of breaking, the CB may continue to stretch until the NE undergoes transient rupture due to a lack of lamin to sufficiently enclose the chromatin (Maciejowski et al. 2015). In this scenario, chromothripsis ensues. During NE rupture, TREX1 enters the nucleus and degrades the CB into ssDNA. The bridge then splits to form two nuclear buds (Fenech et al. 2010). Each nuclear bud fuses with a daughter nucleus, allowing the fragments to return to a primary nucleus. Meanwhile, nuclear APOBEC enzymes mutate the ssDNA fragments. The ssDNA then undergo haphazard repair, generating a completely randomly arranged chromosome. Not all fragments are re-ligated together though; some are simply deleted, while others form double minutes (Ly and Cleveland 2017). Alternatively, if TREX1 is unable to degrade the DNA before cGAS detection, the cGAS-STING pathway becomes activated, possibly causing cell death (Nassour et al. 2019). However, although it seems logical that failed TREX1 degradation of CBs would lead to cGAS activation and death, cGAS-mediated death from CBs specifically has not been reported. cGAS has been shown to accumulate at CBs, though (Mohr et al. 2020).

Cell fusion can also occur if CBs do not break and sufficient lamin is available. This leads to tetraploidization (Pampalona et al. 2012). Because tetraploid cells have multiple centrioles, they undergo multipolar mitosis, which has a wide range of consequences (Pihan 2013). For instance, multipolar to bipolar spindle remodeling can occur. During this process, multiple centrioles align on the two poles of the cell as closely as possible to correct centriole positioning. This process consistently results in aneuploidy and commonly generates MN as well. Mitotic slippage and nucleokinesis without cytokinesis can also result from multipolar mitosis. Cells resulting from either of these processes are highly polyploid and are either mostly or completely incapable of division. The final two fates of multipolar mitosis are mitotic catastrophe and postmitotic apoptosis, both of which result in cell death.

Determinants of cell death versus cell survival during crisis

In most cases, TREX1 binds or attempts to bind prior to cGAS (Wolf et al. 2019, Mohr et al. 2020). In addition, cGAS-STING does not typically cause cell death; this pathway normally produces an inflammatory response. In non-crisis cells, autophagic removal of MN cells does not result in cell death (Rello-Varon et al. 2012). It is therefore critical to understand how mass cell death occurs during crisis by cGAS-STING-mediated autophagic cell death (ACD). Cell death by cGAS-STING is likely promoted by two elements: TREX1’s potential inability to sufficiently degrade DNA and altered cGAS-STING death promoting pathways.

TREX1’s Potential Inability to Sufficiently Degrade DNA

Oxidized DNA is resistant to TREX1 degradation (Gehrke et al. 2013). Reactive oxygen species (ROS) also cause increases in the levels of autophagy-related proteins p62 and LC3. Accumulation of ROS during crisis may therefore promote degradation by cGAS. Although ROS and crisis have not yet been studied, the following are speculatory sources of ROS generated by crisis:

  1. ROS may be generated as a result of aneuploidy. Due to significantly varied gene expression during aneuploidy, the frequency of misfolded proteins increases and places stress on the ER. This stress can trigger the unfolded protein response (UPR). UPR in turn causes mitochondrial stress and increased levels of ROS (Newman and Gregory 2019).
  2. NE rupture can cause PML bodies, which are involved in sensing oxidative stress in the nucleus, to move into the cytoplasm. This limits the nucleus’s ability to detect and respond to ROS. Similarly, mitochondria may also move inside the nucleus and generate ROS. The nucleus may reseal before the organelles can return to their proper cellular locations (Houthaeve et al. 2018).
  3. Lamin deficiencies can contribute to ROS generation and sensitivity. Lamin B deficiencies are more commonly observed (Hatch et al. 2013, Petsalaki et al. 2016), yet lamin A deficiencies have been reported as well (Nassour et al. 2019). Lamin A/C depletion is directly linked to oxidative stress by causing nuclei hypersensitivity to the effects of ROS (Houthaeve et al. 2018, Shimi and Godlman 2014). Lamin B depletion causes an increase of nucleoplasmic Oct-1, a protein that usually binds with Lamin B. Upon Oct-1 NE departure from the lamina, proteins that limit oxidative stress become downregulated (Malhas et al. 2019, Shimi and Goldman 2014).
  4. PARP activity is upregulated in cells with chromosomal instability (Khan et al. 2018). PARP activity depletes NAD+, causing mitochondrial stress and ROS generation (Murata et al. 2019).

Possible non-ROS related causes of cGAS activation generated by crisis are as follows:

  1. Because TREX1 interacts with PARP1 during DNA damage repair (Miyazaki et al. 2014), and PARP activity is upregulated in cells expressing features of chromosomal instability (Khan et al. 2018), TREX1 may have limited substrate availability during crisis.
  2. In MN specifically, because TREX1 tethering to the ER membrane is required for TREX1 resection of DNA (Mohr et al. 2020), a mechanism caused by ER stress (Newman and Gregory 2019) could potentially interfere with ER tubule invasion or TREX1 anchorage. 
  3. A mechanism may exist for TREX1 to detach and reattach to its c-terminus. This unknown mechanism may cause TREX1 to become detached from the ER during crisis. Mohr et al. 2020 found that TREX1 cannot degrade MN DNA if TREX1’s c-terminal domain is altered so that TREX1 is not bound to the ER. Since TREX1 is involved in PARP1 activity during DNA damage repair, TREX1 translocates to the nucleus. However, in order for TREX1 to enter, it must lose its c-terminus (Brucet et al. 2007). This mechanism could prevent TREX1 action in MN, but could also promote TREX1 activation on chromatin bridges (because TREX1 would already be inside the nucleus).
  4. The amount of DNA within the cytosolic formation may influence protein response as well. Some MN or CBs may simply contain too much DNA for TREX1 to successfully degrade before cGAS activation. Larger MN or longer CBs have so far not been specifically linked to crisis, but such a connection may exist. 
  5. The location of the MN could also play a role in determining protein response. In the nucleus NE, TREX1 binds preferentially to escaping DNA because of TREX1’s proximity to the nucleus (Wolf et al. 2019). MN located near the nucleus may be prone to TREX1 detection (because TREX1 is located in the ER), but MN farther from the nucleus may be prone to detection by cGAS. Further MN distance from the nucleus has not yet been associated with crisis, but it is possible that this relationship exists.

Altered cGAS-STING Pathway or Altered Pathway Impacts

Because cGAS-STING does not typically cause cell death, conditions within crisis cells may alter the cGAS-STING pathway and the impacts it has on the cell. However, very little is known about autophagic cell death, especially pertaining to cGAS-STING and crisis. This area requires much more research.

cGAS-STING has been reported to cause death by the NLRP3 inflammasome in myeloid cells (Gaidt et al. 2017). NLRP3 inflammasomes primarily exist in immune cells but have also been reported in some non-immune cells (Walenta et al. 2018). Although an NLRP3-based mechanism of death is unlikely, it should not be eliminated.

In a more general sense, cell survival of crisis requires telomerase activation. Because the chance of an aberration specifically activating telomerase is very rare, and cells exist in crisis until they activate telomerase or die, some cause of death is likely to arise before telomerase is activated. Even if these factors are not commonly found during some crisis, some factor (either listed or unlisted) is likely to be prevalent enough at some point during crisis cell divisions to promote cGAS activation and cGAS-STING-mediated death.


As crisis is a fairly novel field, there is limited understanding on the precise mechanisms of this state. Many questions remain, especially regarding determinants between cell survival and death. To conclude, I will present my recommendations for specific crisis-related mechanisms that should be investigated more thoroughly.

Research concerning whether or not ROS are present in cells undergoing crisis is critical because there are numerous plausible causes of ROS generation during crisis (discussed above). If ROS are present, other relevant investigations can be conducted based on the possible origins of ROS listed above. Other important research areas regarding TREX1 and cGAS binding during crisis include TREX1 and PARP1 interactions during crisis, ER stress and its impact on ER tubule invasion, the mechanism of TREX1 c-terminus loss and whether or not this mechanism occurs during crisis, the size of MN and the corresponding likelihood of cGAS-STING activation, and the proximity to nucleus of MN and the corresponding likelihood of cGAS-STING activation.

As mentioned previously, significantly more research is required regarding how cGAS-STING causes death in crisis cells. Although ACD is suggested, ACD does not occur in non-crisis cells (Rello-Varon et al. 2012). Factors that promote ACD in crisis cells require additional research as well. Although possibly less important, research on the role (if any) of inflammasomes in crisis may help uncover possible mechanisms of cell death, as well.

In regards to pathways through crisis, further research defining the mechanisms of TREX1 action is needed. TREX1 action in MN is ER-dependent, while TREX1 action on CBs requires that TREX1 detach from its c-terminus. Research regarding ER tubules during TREX1 invasion of transiently ruptured primary nuclei would be particularly enlightening.

Continuing research in this field is critical to understanding how cancer cells become immortal. When a thorough understanding of the crisis process is reached, work can begin to identify preventative measures against cell immortalization.


I primarily used Google Scholar to identify sources. To find articles on Google Scholar, I frequently used quote searches to find articles that include all desired words. This was particularly important because my work is centered on the topic crisis. Because the word crisis may also be used in various other contexts, the research included the word “telomere” in the search bar as well. This type of search was also useful to see if articles have been written demonstrating a connection between two or more phenomena (eg. lamin and ROS).

I frequently used articles from PubMed, Elsevier and Nature, but other databases and journals were used as well. I had access to database and journal subscriptions through a parent’s university account. Articles were selected based on to what extent they aligned with the research objectives. When searching for information within articles, I used ctrl f to find key words and the chrome extension Chrome Regex Search to locate multiple key words in one query. Chrome extensions Diigo and Weava were used to highlight and annotate articles.

Ania Grodsky, Youth Medical Journal 2021


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Biomedical Research

A Brief Overview of Telomeres

By Saharsh Satheesh

Published 1:07 PM EST, Mon May 3, 2021


For centuries, scientists have pondered about the human body and the structures that make us who we are. In recent decades, advances in genetics have helped us understand more about the human genome and the processes by which we are able to live. One notable advancement was in the study of telomeres. It had been known that telomeres, which are caps on the end of chromosomes, help protect the chromosome. However, its composition and true use was not well understood.

Cells constantly divide, and every time they divide, the DNA copies as well. When this occurs, the telomeres at the ends wear down. In humans, telomeres consist of a repeating sequence of 5’-TTAGGG-3’. This sequence can be repeated over 3,000 times and some cells can reverse the process of losing telomeres by using the enzyme telomerase, where it functions by adding telomeres. Telomerase is usually inactive in somatic cells but can be active in fetal tissue and germ cells.


As cells replicate, these telomeres shorten, and studies have shown that this is associated with aging. A paper by Masood A. Shammas explains that “Telomere length in humans seems to decrease at a rate of 24.8–27.7 base pairs per year [12,13]. Telomere length, shorter than the average telomere length for a specific age group, has been associated with increased incidence of age-related diseases and/or decreased lifespan in humans [10,14,15]. Telomere length is affected by a combination of factors including donor age [16], genetic, epigenetic make-up and environment [17–20], social and economic status [21,22], exercise [21], body weight [12,23], and smoking [12,24]. Gender does not seem to have any significant effect on the rate of telomere loss [13]. When telomere length reaches below a critical limit, the cells undergo senescence and/or apoptosis [25,26].”

Thus, according to Shammas, although “telomere length shortens with age, [the] rate of telomere shortening can be either increased or decreased by specific lifestyle factors. Better choice of diet and activities has great potential to reduce the rate of telomere shortening or at least prevent excessive telomere attrition, leading to delayed onset of age-associated diseases and increased lifespan.”

Future Prospects

Much is still to be understood about telomeres and the secrets that they hold. Scientists are currently studying how telomeres may be useful in better understanding and possibly preventing cancer. Cancer cells are able to use telomerase to continuously replicate, but, according to Jerry W. Shay, “inhibition of telomerase may thus represent a novel anticancer therapeutic approach. If we can suppress telomerase, we may be able to drive cancer cells into a growth arrest state. Many laboratories, including [his] own, are studying this at the present time, and the preliminary results are very encouraging.”

Saharsh Satheesh, Youth Medical Journal 2021


Shay, Jerry W. “Do the Telomeres in Cancer Cells Shrink?” Scientific American, Scientific American, 8 Jan. 2001,

Shammas, Masood A. “Telomeres, lifestyle, cancer, and aging.” Current opinion in clinical nutrition and metabolic care vol. 14,1 (2011): 28-34. doi:10.1097/MCO.0b013e32834121b1

“Telomere.” Wikipedia, Wikimedia Foundation, 2 Apr. 2021,

“Telomeres and Telomerase (Article).” Khan Academy, Khan Academy,

“What Is a Telomere?” Facts, The Public Engagement Team at the Wellcome Genome Campus, 25 Jan. 2016,

Biomedical Research

Smart Inhalers

By Pratiksha Baliga

Published 1:57 PM EST, Sun May 9, 2021


Inhalers are medical devices used to treat chronic obstructive pulmonary disease such as asthma by delivering bronchodilator medication to the airway directly, making use of a fast and sharp inspiratory force, without passing through the blood. The medicine within inhalers uses lactose molecules. Chronic Obstructive Pulmonary Disease affects over 200 million people around the world whereas asthma affects another 300 million. The chronic respiratory disease makes up a little over 8% of the world’s chronic disease burden. In order to mitigate the clinical severity of these conditions, the patients have to adhere to a strict medication schedule, with the dosage and timing being adjusted to achieve control of their symptoms.

With modern times coming into play, inhalers are powered by different forms of technology giving rise to a new generation of devices named Smart Inhalers. They are present with extra digital features of connection with mobile applications and help doctors and patients to manage asthmatic conditions in a better and improved way. 

Working and Features

Monitoring of medication schedules and dosage reminders is its main feature. A sensor on the inhaler communicates with the mobile application via Bluetooth to keep a  track of the inhaler using data in the app. The app records the date, time, and even the location of each dosage intake, and then using this information schedules the user’s next dose reminder. Some of these inhalers use a built-in sensor that is integrated into the body of the inhaler itself, while others, for example, can use propeller sensors which are external sensors that can be attached to various kinds of inhalers. Along with these features these inhalers also have high pollen and pollution alerts or the ability to sense if the user forgets to take their inhaler with them when they leave home. They can also indicate when the patient is overusing their preventive medicine pointing towards poorly controlled asthma.

Nowadays a smart cap called CapMedic for smart inhalers is used by people with asthma, chronic obstructive pulmonary disease, and other respiratory disorders. It is available only by prescription. It has to be placed on top of the inhaler and it houses sensors that guide users and collect data for remote patient monitoring. It connects via Bluetooth providing direct communication with patients for better effectiveness in management. The caps are reusable and rechargeable emitting visual, audio, and haptic signals letting the users know when an inhaler has been adequately shaken and is fully upright, among other parameters that ensure a full dose of medication. It fits on most of the smart inhalers available and pairs with an app on the phone that can send data to a clinician. It also incorporates a spirometer which measures the air capacity of the lungs.

World’s First Smart Inhaler

The world’s first smart inhaler was recently approved by the FDA, and named Teva’s ProAir Digihaler (albuterol sulfate). It was introduced to the United States market through an sNDA application and was said to join Proteus Digital Health as being a potential game-changer for digital medicines.The ProAir Digihaler is built on the RespiClick inhaler formulation. It has a sensor that tracks when it is used in real-time and syncs this data to a mobile app. The patient has to use an app to scan a QR code at the top of the inhaler, which will sync the inhaler to the app. The ProAir Digihaler can determine how well the patient uses it, as the sensor measures a breath actuation and sees how well a patient inhales giving them a rating.

The ProAir Digihaler is built on the RespiClick inhaler formulation. It has a sensor that tracks when it is used in real-time and syncs this data to a mobile app. The patient has to use an app to scan a QR code at the top of the inhaler, which will sync the inhaler to the app. The ProAir Digihaler can determine how well the patient uses it, as the sensor measures a breath actuation and sees how well a patient inhales giving them a rating.

Patients can view their event data through the ProAir companion app, which provides tips to improve inhaler techniques and offers medication reminders if they choose. They can also transmit the data directly to their doctors to keep a track of their condition, its management and bring about improvement in their treatment plan.


Following the wave of connected devices and smart health technologies coming up in the healthcare sectors, the future of digital health innovation will be brighter than before. Smart inhalers are considered by many the way the future will progress, essentially breathing fresh air into the management scope of chronic respiratory treatments in the years ahead. Its demand is considered to increase at a significant rate with novel opportunities to address more challenges associated with accessibility, quality, effectiveness, efficiency, and cost of healthcare.

Pratiksha Baliga, Youth Medical Journal 2021


[1]Berg, J., Arundhati Parmar  |  2:05 pm, A. 14, Stephanie Baum  |  7:30 am, A. 20, Elise Reuter  |  3:14 pm, A. 23, Anuja Vaidya  |  2:15 pm, A. 23, & Frank Vinluan  |  11:26 pm, A. 23. (2020, January 23). FDA clears smart inhaler cap from Cognita Labs. MedCity News.[2]Thomas, D. L. (2021, January 11). What are Smart Inhalers? News.

[2]Thomas, D. L. (2021, January 11). What are Smart Inhalers? News.