The Gut Microbiome's Influence on Cancer Progression and Treatment Outcomes Through the Gut-Brain Axis

Menatalla Aboukhalia, Verrazzano Class of 2026, completed major in Biology

My research explored how the tiny organisms living in our digestive systems called the gut microbiome affect the development and treatment of cancer through a communication system known as the gut-brain axis. This axis is the way our gut and brain send messages to each other using nerves, hormones, and immune signals.

Scientists have recently discovered that the health and balance of these gut microbes may not only affect our digestion and mental health but also influence cancer growth, response to therapy, and even survival rates. I focused on several types of cancer, including gastrointestinal, brain, breast, prostate, and pancreatic cancers. I analyzed over 100 studies to understand how disruptions in the gut microbiome (known as dysbiosis) are linked to tumor development. For example, harmful bacteria can produce toxins that promote inflammation and tumor growth, while helpful bacteria may boost the body’s immune response or improve the effects of chemotherapy and immunotherapy. One of the most exciting findings was that personalized treatment tailored to a patient’s specific microbiome could lead to better results in fighting cancer. In other words, understanding a patient’s gut microbiota might help doctors choose the most effective treatments. Some studies even showed that modifying the gut microbiome with probiotics, diet, or fecal transplants could improve treatment success.

I’ve always been fascinated by the connections between the body’s systems especially how something as "small" as bacteria could influence something as serious as cancer. I was drawn to this topic because it bridges biology, medicine, and even mental health. The more I read about the gut-brain axis and the microbiome, the more I realized how central this topic is to understanding modern disease. I also have a personal motivation: several family members have battled cancer, and I wanted to contribute to an area that might help improve care and outcomes in the future.

At first, I thought the capstone would mostly involve summarizing articles and writing a long paper. I underestimated the amount of critical thinking it required—especially when comparing studies with different methods or drawing conclusions from conflicting results. It wasn’t just about collecting information; it was about understanding patterns, questioning assumptions, and synthesizing new insights. That part was more difficult than I expected but also more rewarding.

The most challenging part was narrowing down the scope. The gut microbiome is connected to almost every part of health, and cancer is already such a complex disease. It was easy to get overwhelmed by the volume of research. Creating a structured methodology and sticking to my inclusion/exclusion criteria helped me stay focused. One of the easier (and more enjoyable) parts was presenting the material visually, designing charts and thematic summaries. I found that these visuals helped clarify patterns and communicate my findings more effectively. I was surprised by how fast this field is evolving. Some studies I found were already outdated after just a few years, replaced by newer, more advanced research using artificial intelligence to predict patient responses based on their microbiomes.

In the future, I’d like to explore how diet and lifestyle changes can be used alongside medical treatments to optimize the gut microbiome for cancer patients. I’m also curious about how the microbiome affects pediatric cancer and survivorship. Another promising area is the integration of machine learning to predict treatment outcomes based on a person’s microbiome profile. These tools could one day help doctors create highly personalized cancer therapies.

This research taught me how to critically evaluate scientific evidence and stay organized when working with a large number of sources. More importantly, it showed me that science is never static it’s a living conversation with new voices and discoveries all the time. I now feel more confident in my ability to take on complex problems and contribute to ongoing scientific questions. I’m leaving this experience with a deeper understanding of how biology, medicine, and technology come together and with a renewed motivation to pursue graduate studies in the health sciences.

Detecting Anomalies in Network Traffic Data

Isabel Loci, Verrazzano Class of 2026, completed major in Computer Science and minor in Mathematics

Somewhere in the last year, I fell down a rabbit hole of learning more about the most popular cyber-attacks in history. These attacks dated back decades ago, when the online world was still fairly new and became more complex as it developed. I have always been concerned about the safety and privacy of any information I use online, so it got me thinking: how do people mitigate attacks at such a high scale? At the time, I was participating in a one-year long data science boot camp, and we were focusing on building AI-models using machine learning. The base idea of machine learning is a system that is able to learn from previously collected data, detect patterns, and then make decisions on its own without any explicit programming beforehand. This method allows systems to handle unknown data well, as they can make decisions independently after training. Neat, right?

Theoretically, I thought that it should be possible to create a machine learning-based AI model that is able to distinguish normal network traffic from malicious network traffic. So, me and a few friends decided to bring this idea to life. Our project was built in Python and then deployed through HuggingFace. The final accuracy percentage of the model was 92.94%. Good, but could've been way better.

When I was brainstorming ideas with my mentor, Dr. Huo, I mentioned my project. I talked about how the methodology we chose to implement had a lot room for improvement, and that I would've liked to go back and start all over again if I knew what approach I wanted to try next. Then she suggested this simple, but very brilliant idea to me: What if my capstone was about a deep research on the topic of anomaly intrusion detection?

It all clicked then. I had previously read one or two works of people that attempted the same project, but not much more beyond that. What I didn't realize was just how many ways data scientists had solved this problem with in the past, not with just traditional machine learning methods but also deep learning ones. My work revolved around studying as many research papers as I could, summarizing my findings, and presenting them in the form of a survey paper.

What I found hardest (and a little funny) was how when I came across a concept I hadn't heard of before, four more fundamental concepts were attached to it that I could absolutely not exclude from the survey paper. I spent hours upon hours studying these concepts, breaking down complex formulas and comparing results of different methodologies simply to understand the hidden connections between them. There is a general step-by-step process that data scientists follow when building a ML model, and it is the following: sourcing the data to be used for model training, data preprocessing, selecting the appropriate algorithm, model training, and finally model evaluation. Data preprocessing is important because a dataset could have issues such as missing values, duplicate columns, bad column names and inconsistent feature names, all of which can interfere with the accuracy of the model. Then the dataset could have too many samples of one attack and not enough of another attack, needing to be balanced. Like we mentioned before, ML algorithms are split into two categories, and each category is split into several unique algorithms. After the model is trained, it then needs to be evaluated using industry standard performance metrics that give an estimation on how the model is doing. If the methods are implemented correctly, the metrics will reflect the true performance of the model. Just be careful, a score that is too good to be true is often misleading! We strive to be as close-- but not too close-- to the 100% accuracy score.

This has been the most extensive research I have gotten to work on throughout my undergraduate years, and it has sparked a love in me to do more. Already I wish to delve into more papers written for different fields. I want to study Literature, Psychology, Physics, the Arts, and so much more. It has changed the way I interact with the world around me and how I decide to embrace and utilize new information.

Working on a thesis might have not been my first idea for completing my capstone, but I am so glad it's what I chose in the end.

Improving Sepsis Prevention and Early Detection in Immunocompromised Patients Through Targeted Nursing Interventions

Aldina Tafa, Verrazzano Class of 2026, completed major in Nursing and Psychology, and minors in English Linguistics and Speech Language Pathology

Working on my capstone project became one of the most meaningful academic undertakings of my nursing education. When I first began this project, I knew sepsis was a critical issue in healthcare, but I had not yet understood the extreme vulnerability of immunocompromised patients or how consistently their early symptoms can be overlooked. As I progressed through the literature and developed my analysis, I realized how crucial nurses truly are in bridging the gap between early recognition, prevention, and lifesaving intervention. The process changed the way I view nursing practice, health equity, and my own role as a future clinician.

One of the most important things I learned through this project was how different sepsis looks in immunocompromised populations. Many patients do not present with the “classic” signs that nurses are traditionally taught to look for, no fever, no elevated white blood cell count, and often no obvious signs of infection. Understanding these atypical presentations deepened my appreciation for the complexity of nursing assessment and the level of critical thinking required to protect high-risk patients. I learned that early detection is not just about following a set of guidelines; it requires situational awareness, strong clinical judgment, and a willingness to question whether something subtle might actually be the first sign of a life-threatening decline.

This project also taught me how essential prevention truly is. Much of the existing literature focuses on treatment, what to do once sepsis is already present, but preventing it from occurring in the first place is where nurses have the greatest potential to save lives. As I reviewed studies from 2020–2025, I found myself becoming increasingly passionate about hand hygiene initiatives, aseptic technique, early warning tools, and patient education. It was eye-opening to realize how many infections are preventable, and that something as “simple” as consistent hand hygiene can literally cut the risk of sepsis in half for immunocompromised patients. Working through the evidence reinforced how powerful nursing interventions are, even when they seem routine or basic.

A major part of this capstone experience was collaborating closely with my mentor, who encouraged me to dig deeper into the policy and systemic side of sepsis prevention. Through her guidance, I learned how research connects to real-world nursing practice, hospital protocols, and national initiatives like the CDC’s Sepsis Core Elements. It helped me see how bedside nurses contribute not only to individual patient outcomes but also to broader quality improvement and institutional change. This mentorship made the project feel personal, meaningful, and tied to my future role in nursing leadership.

If I were to continue developing this research, I would want to build a project that examines sepsis prevention directly within clinical environments, particularly in oncology units, transplant floors, and long-term care facilities, where immunocompromised patients are cared for daily. I would also like to explore how technology, such as machine-learning prediction models, can be integrated into nursing workflows to support earlier recognition of subtle symptoms.

Another area worth expanding is patient and family education: creating clear, accessible tools that teach high-risk individuals how to identify early infection signs before a hospital visit.

Animated Farewells: How Children's Cartoons Help Young Audiences Understand Death and Grief

Dalia Omar, Verrazzano Class of 2026, completed major in Psychology and minors in Arabic and Biology

My research examined how children's cartoons portray death and grief, and how these portrayals help young viewers understand emotional experiences they may not have language for yet. I focused on four well-known animated movies and shows like The Lion King, Bluey, Up, and Coco, and analyzed how each one introduces loss in a way children can grasp. Using concepts from developmental psychology, I focused on how children at different ages understand death, and how visual storytelling, character reactions, and symbolic moments in animation can teach them some emotional learning. My main conclusion is that children's cartoons do far more emotional work than people realize. When watched thoughtfully, they give children safe ways to experience sadness, ask questions, and understand that grief is a natural part of life.

I chose this topic because I took the Death and Dying course with Professor Weiser over the spring semester, and the class completely opened my eyes to how rarely we talk about death, even though it affects everyone. I was initially very nervous to take this class but I thought I'd take the challenge. We discussed how difficult it is for adults to have conversations about loss, especially with children. The class made me think about how kids learn about death at all, and whether the media they watch might be doing some of the teaching. From there, I noticed more children's shows were including emotional episodes, and I wanted to understand the psychology behind it. I wanted to combine what I learned in Professor Weiser's class with my interest in psychology and storytelling.

I expected the capstone to be a long research paper, but I didn't expect how much time I would spend rewatching scenes, analyzing emotional cues, and connecting them with theory. I do love watching movies and shows but having to rewatch them over and over just to look back to see anything I missed was a bit tedious. I thought it would be more straightforward, but I found myself going even deeper into the films than I expected. The work was definitely challenging because I needed to also balance academic research with emotional content. Writing about grief requires a lot of sensitivity and I found it emotionally draining at times. The main goal I had was to make sure I represented both the psychology and the storytelling accurately. The easiest part was writing about the films themselves. Animation especially is something I've always enjoyed, so analyzing those scenes led me to catch something new whenever I replayed them. What surprised me most was how intentional children's media really is. I noticed these emotional moments were not just written for only the plot but with the true purpose of teaching about grief.

If I had more time to expand this project, I would explore how different cultures teach children about death through media. My favorite movie to write about on this list was Coco. Coco showed me how powerful cultural traditions can be when explaining loss, and I would be interested in comparing international films or shows to see how different societies support children emotionally. Another direction would be studying how parents use these films in real-life conversations, whether watching them leads to discussions at home, and how children respond afterward.

The biggest thing I'm taking away from this experience is confidence in my ability to handle a sensitive topic academically. I learned how to build a research question, gather sources, and create a structured argument, but I also learned how important emotional education is. Children's cartoons may seem simple, but they teach lessons that stay with us for life. I feel that till this day we still reference most of these movies which reflects the impact they have on us watching them. This project reminded me that the media we show children matters, and that emotional development should be taken seriously. Completing this capstone also made me appreciate the value of the Death and Dying class even more and it shaped my thinking, pushed me out of my comfort zone, and ultimately inspired a topic that helped me grow both academically and personally.

The Dolphin Finder

Ahmad Alrafati, Verrazzano Class of 2026, completed major in Computer Science 

During my time at the College of Staten Island, I noticed that because CSI is largely a commuter campus, students often have limited opportunities to connect beyond their classes. Everyone has different schedules, responsibilities, and travel times, which can make it harder to meet new people or form project groups outside the classroom. This makes it difficult to find others with similar academic interests, especially when it comes to group projects and long-term ideas. That is what motivated me to create Dolphin Finder, a platform designed to help CSI students meet, collaborate, and build project teams more easily. The app can help students across many majors: a business major could find a computer science student to help build an app idea, an art major could find a marketing major to promote a creative project, or a biology student could find a software developer to build a visualization tool. Dolphin Finder gives students one place to share ideas, discuss interests, and connect with people they might never meet in person.

I originally expected the capstone to be mostly about programming, but I quickly learned there was a lot more involved. I had to plan features, consider user experience, build the structure of the system, document my work, and test every piece carefully. At times, even small errors took hours to fix, and I often had to pause, rethink, and try multiple solutions. These challenges taught me patience and forced me to improve my problem-solving skills. Even though the process was demanding, seeing the app finally work the way I imagined was extremely rewarding, and it showed me how technology can solve real issues in a community when built with purpose and intention.

This capstone helped me grow both technically and personally. I learned how to take an idea from concept all the way to a full working system, and I became more confident in building full-stack software independently. More importantly, this experience reminded me why I majored in computer science, to build tools that can make people’s lives easier and more connected. In the future, I hope to expand Dolphin Finder by eventually making it available to all CSI students and even outside CSI. This project represents my journey at CSI, and it showed me that with commitment, planning, and creativity, a simple idea can become something meaningful, impactful, and useful for the community.