Published Research

Abnormal amyloid precursor protein (APP) processing, specifically cleavage through the amyloidogenic pathway creates amyloid-beta proteins that when oligomerized, causes brain damage and is a hallmark of Alzheimer's disease. Here, we examine how AICD and Arc expression affect APP localization and processing in Drosophila. Immunohistochemistry revealed that AICD overexpression induces Arc expression and leads to APP clustering and accumulation, which are not present in controls. Western blot analysis showed that Arc-GFP expression generates additional APP-related species, possibly by-products of APP proteolysis. This includes AICD, which drives the feed-forward loop in APP cleavage and Alzheimer’s Disease. Furthermore, long-term memory (LTM) training increased APP cleavage in MBON-5β neurons, which in turn causes increased AICD and sAPP levels. These findings suggest that Arc and AICD are the main components in APP proteolysis, and memory related activity further drives the feed-forward loop in vivo.

Personalized medicine (PM) has increasingly become a central theme in contemporary healthcare and biomedical research, promising more effective and efficient care through tailored interventions. Although widely discussed, the term remains loosely defined, encompassing both holistic, patient-centered approaches and targeted treatments for stratified subgroups, such as those informed by pharmacogenetics. Despite its modern framing, personalization in medicine has deep historical roots. Since Hippocratic times, physicians have sought to adapt treatments to individual patients, even as the tools and scientific understanding behind these efforts have evolved dramatically. Over centuries, medical practice has progressed from metaphysical explanations of disease to physical, cellular, and molecular models, ultimately leading to advanced diagnostics and precision therapies driven by genetics and data science. The underlying goal—to optimize treatment for each patient—has remained constant, even as methods have transitioned from the ancient theory of four humors to the molecular logic of DNA. This review traces the historical evolution of personalized medicine, illustrating how technological and scientific advancements have continuously refined our ability to diagnose, classify, and treat disease at increasingly precise levels.

This research proposes an AI-based predictive maintenance (PdM) system with real-time operation in mechanical systems. It offers a neuromorphic computing, machine learning, and explainability from large language models-based hybrid architecture to reduce downtime by over 70% and increase diagnostic readability and trustworthiness. The system is running on edge devices like Raspberry Pi and Intel Loihi with sub-5ms latency and power consumption less than 50mW, making it possible for real-time monitoring without cloud dependency. Domain testing by domain experts confirms both its human usability and technical performance and, therefore, is the ideal solution for the industrial environment today.

This research paper examines how test framing and peer presence influence academic performance. It combines psychological theory with real-world classroom application to highlight how subtle environmental factors affect student outcomes.

This research explores how the lack of climate awareness among rural and peri-urban populations contributes to the worsening of global climate change. It identifies major gaps in knowledge and access to environmental information and analyzes the impact of digital exclusion. The study proposes community-based, culturally relevant awareness strategies—such as local campaigns, storytelling, and low-tech educational tools—to foster environmental responsibility. By highlighting these overlooked communities, the paper emphasizes the importance of inclusive climate communication for global sustainability.