2025 Publications

Three-dimensional (3D) bioprinting represents one of the most developing fields of biomedical engineering and regenerative medicine. 3D bioprinting integrates the use of additive manufacturing used to create devices with the biological understanding of human and animal physiology to create complex tissues and organs from scratch using various bioinks and biological scaffolds. The engineering perspective comes in 3D printing through the lens of biomechanics to implement the material, innovative bioink properties, scaffold designs, and printing techniques for precise layering. However, scientific persuasion must still overcome issues with cell health post printing, construction vascularization & functionality, and generated tissue durability. Through the lens of a medical approach, 3D printing holds a great deal of value for regenerative medicine such as skin grafts, craniofacial surgery, orthopedic procedures, dental solutions, and organs for transplant. There are some clinical limitations which can be overseen through regulatory concerns, biocompatibility challenges, and ethical issues . Finally, from a business standpoint, this market has significant potential. 3D bioprinting affects new market generation from pharmaceutical testing in 3D bioprinted lungs and tumor constructs to expansion in regenerative patches with future goals for anatomical perfection. Bioprinting is already commercially effective in small markets, but larger endeavors are limited due to scalable and reproducible inadequacies in research materials along with high research costs. The market for this is mostly in research and development (R&D) stages and is expected to gradually increase in demand over time in effective pharmaceutical testing and academic solutions, but no solid predictions for widespread clinical applicability for decades. This paper assesses 3D organ bioprinting through the lenses of engineering, medicine, and business to determine the realities of viability, practicality, and ethics in new interdisciplinary integration. Without a multidisciplinary approach gained through an understanding of all three perspectives, 3D bioprinting cannot achieve its full potential to change the world.

Cancer treatments like chemotherapy, though effective, often end up damaging healthy cells along with cancer cells, with fatigue, hair loss, and cognitive impairment being just a few of many negative side effects caused by chemotherapy. Targeted drug delivery aims to minimize, if not eliminate, the undesirable aspects of chemotherapy by directly delivering treatment to cancer cells. Smart nanoparticles are engineered to carry drugs directly to affected sites by responding to specific stimuli, allowing them to provide targeted treatment through either a change in chemical structure, solubility, or a release mechanism linked to a particular type of stimulus. These nanoparticles minimize damage to healthy tissue and increase the therapeutic outcome of treatments. There are many current cases of smart
nanoparticles being used in the field of oncology for cancers such as breast cancer, lung cancer, prostate cancer, and brain cancer. Additionally, nanoparticles have shown promising results when used in diagnostics. While smart nanoparticles are promising for cancer drug delivery, drawbacks such as potential toxicity, difficulty achieving targeted delivery, and challenges in scaling up production leave room for more research focused on improving the efficiency of nanoparticles. Smart nanoparticles are an innovative form of drug delivery that, with time, can go on to expand their reach beyond oncology and positively impact the medical field as a whole.

Spinal cord injury (SCI) causes severe motor impairments that significantly reduce patient independence, but cortical networks often remain intact. Non-invasive brain-computer interfaces (BCIs) hold promise for restoring motor control and facilitating rehabilitation after SCI. We conducted a systematic literature review of 100 recent studies on non-invasive BCIs for SCI motor recovery. Our analysis revealed that EEG-based motor-imagery (MI) BCIs paired with functional electrical stimulation (FES) were the predominant approach. These systems often incorporated rich multimodal feedback: many protocols combined visual cues, tactile sensations, and robotic assistance to reinforce the intended movement. We found that providing high density EEG recordings and personalized classifier calibration markedly
improved decoding accuracy and clinical outcomes. Key implementation challenges included unstable FES electrode interfaces, user fatigue during extended training, and high system costs. Additionally, most studies tested only small patient cohorts, making it difficult to generalize results; patients with complete neural degeneration cannot benefit from conventional EEG-BCIs, indicating a need for alternative strategies. We highlight advanced adaptive techniques such as deep-learning decoders and transfer learning that have shown promise in recent studies. Overall, aligning neural intent detection with timely stimulation or feedback appears critical for driving neuroplasticity and enhancing motor recovery.

  Betul Ozgen and Munevver Coskuner                                                                                                                                              Abstract: Modern neuroscience has found that the human brain is dynamic and prone to...

Mechatronics has revolutionized mechanical engineering by fusing computer science, electronics, and mechanics, presently with artificial intelligence as another powerful driving force. The key purpose of this paper is to explore how AI is redefining mechatronic systems to be more intelligent, flexible, and efficient. From smart factories and driverless cars to surgical robots and advanced prosthetic limbs, AI-driven mechatronics has introduced increased precision, decision-making, and reliability. Based on existing studies and applications, this paper demonstrates how AI is being utilized in sensor fusion, adaptive control, and predictive
maintenance, propelling mechatronics to become a key element and foundational technology for Industry 5.0’s future

The increasing global demand for mental health services has highlighted many shortcomings in access, affordability, and timeliness of care. In response, new applications powered by artificial intelligence (AI) in the form of chatbots have been developed to provide increased, scalable access to emotional support, cognitive behavioral techniques, and self-help resources. In this paper, we review the potential for the use of AI chatbots in mental health support and interventions through a focused analysis of applications, clinical evaluations, and user impressions. Moreover, key examples of applications, for example, Woebot, Wysa, and Youper, yielded some promising results for individuals who experience symptoms of anxiety and/or depression, especially individuals seeking low-barrier, less stigmatizing access to support. Several studies also found benefits in mood charting, facilitating emotional expression, and self-reflection. However, operational usability of AI applications often depends on design efficacy, adherence to evidence-informed therapeutic models, and active user participation. Indeed, while chatbots are cost-effective and scalable, there are some limitations. Examples of limitations include limited efficacy for some emotional crises that more complex, personalized interventions may deem necessary, insufficient personalization, and lack of true empathy. Additionally, clinical implications cannot progress without further addressing ethical concerns
regarding data privacy, informed consent from users, and algorithmic bias in AI responses. While AI chatbots are not substitutes for licensed mental health professionals, they represent a growing complement in the continuum of care. This paper concludes by emphasizing the need for hybrid human-AI models and stronger regulatory oversight to ensure responsible, safe, and effective deployment.

Mental disorders are significant sources of global disease but are diagnosed inadequately using techniques often grounded on subjective judgment and lacking biological validation. Prevention tools such as cognitive-behavioral therapy, school programs, and mobile health apps are promising entry points for early intervention but are limited by constraints of scalability, long- term effectiveness, and access, particularly in low- and middle-income countries. Recently developed breakthroughs using artificial intelligence (AI) and physiological monitoring offer a promising complement. Electroencephalography (EEG) and Galvanic Skin Response (GSR), when supplemented with AI, can discern complex physiology patterns associated with stress, anxiety, and depression. While EEG is afflicted by issues of noise, lack of spatial resolution, and variability when used individually, multimodal combinations of EEG and other biosignals and GSR have shown rich promise of diagnosis. Reports detail machine learning models founded on these signals that achieve 79–95% accuracy rates for distinguishing between and predicting affective states and clinical populations, and predictive models permit pre-warning of risks of mental health issues before their onset. These findings describe the possibility of AI-driven multimodal physiological monitoring overcoming the problems of conventional diagnosis with scalable, objective tools with worldwide promise.

New advancements in medical technology are making it possible to treat conditions that were previously difficult or even impossible to manage. Among the most promising of these technologies are nano-drugs specifically engineered to enter the body and target disease at the cellular level. What made nano-drugs so promising was that they often come with minimal side effects. However, because each individual has unique genetic and biological characteristics, a one-size-fits-all approach to nano-drugs is no longer ideal. This calls for a way to tailor these technologies to each and every person who relies on them. Personalized medicine has become
more and more popular over the years, ultimately transforming healthcare. By creating treatment plans using individual patients’ genetic, biological, and lifestyle data, the healthcare system can drastically improve treatment efficiency. This paper explores the potential benefits that come with integrating personalized medicine with nano-drug technologies. Additionally, our focus is not just on the healthcare benefits, as we dive into how this works from a business standpoint. Using
research from recent literature in biomedical engineering and healthcare marketing, we examine just how nano-drugs work to treat patients while also discussing the potential economic and ethical implications. This paper also focuses on the challenges of implementing this sort of system into healthcare, such as cost, accessibility, and data/privacy concerns. Overall, this paper dives into the possibilities of personalized nano-drug treatment for both patients and the healthcare system, while tracking the risks that come along with it.

Social-emotional learning (SEL) has become a predominant tool throughout early educational
contexts to inform and prepare students to convey their own emotions, as well as interpret
others. This developed understanding is difficult to attain generally, but even more so for
students with intellectual and learning disabilities. Peers with ADHD and ASD have lower awareness and perceptiveness of emotions, especially when applying them within social environments, which can provide a barrier to lasting relationships and effective
communication. Throughout this paper, one will thoroughly discern the hindrances that
learning disabilities present in social forums and emotional discrepancies, as well as the great
promise that SEL provides in bridging the gap in not only communication but also
opportunity. This study has found that the usage of SEL within classrooms provides an
essential application for those with learning disabilities to orient themselves in the
social-cultural foundation. SEL allows an ease of communication that is often not afforded to
ADHD and ASD students, especially in presenting their emotions. This has resulted in
heightened participation in social activities and more comprehensive reciprocation of
common social practices. With “soft skills” being in increasing demand on the job market,
and academic spaces observing the necessity of these skills for healthy professional dialogue,
SEL proves a significant prospect for those with learning disabilities for increasing social
awareness and vocational development. With the growing prevalence of diagnoses every
year, establishing the presence of youth with ADHD and ASD further, SEL programs are
vital to the success of students with learning disabilities. This paints a hopeful abstract for the
future of these students. In particular, paired with the potential and efficiency of AI, SEL
programs can combat the everyday challenges that permanently impair these students’ ability
to prosper.