Dr Manjula R, Assistant Professor, Department of Computer Science and Engineering, and Dr Anirbhan Ghosh, Assistant Professor, Department of Electronics and Communication Engineering, has recently had their patent published titled “A System for Analyzing Electromagnetic Wave Scattering Path Loss in a Tissue and a Method Thereof” with Application no: 202541001730.

The faculty duo has revolutionised cardiac diagnostics and real-time health monitoring through their invention. This innovative system analyses electromagnetic wave scattering in biological tissues, using terahertz (THz) frequencies to optimise nanosensor communication and path loss analysis. By leveraging cutting-edge technology, it enables advanced biomedical devices for precise physiological monitoring and safer, more reliable in-vivo communication systems. A step forward for heart health and medical breakthroughs, this invention bridges the gap between technology and life-saving healthcare solutions.

Dr Abhijit Dasgupta, Assistant Professor from the Department of Computer Science and Engineering, has published his groundbreaking research on using deep proteomics to analyse Alzheimer’s disease (AD) models in mice and comparison with human AD protein alterations. He has published the paper titled “Human-mouse proteomics reveals the shared pathways in Alzheimer’s disease and delayed protein turnover in the amyloidome” as one of the first authors in the Nature Index journal Nature Communications, having an impact factor of 14.7.

Abstract

Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by amyloid-beta (Aβ) plaques and tau tangles. This study presents a comprehensive proteomic and phosphoproteomic analysis of multiple AD mouse models, comparing their molecular pathways with human AD data to evaluate their translational relevance. Using deep proteomics, the study identifies shared and distinct protein alterations across amyloidosis models (5xFAD, APP-KI), tauopathy models (3xTG), and splicing dysfunction models (BiG). While these models collectively replicate 42% of human AD protein alterations, amyloid formation significantly delays protein turnover in the amyloidome, contributing to proteome-transcriptome discrepancies. Proteomic and turnover analysis highlight the accumulation of proteins such as ApoE, CLU, and HTRA1, implicating lysosomal and autophagic dysfunction. These findings underscore the importance of protein homeostasis in AD pathology and provide a multi-omics resource for selecting appropriate mouse models for specific disease mechanisms.

Explanation of the Research in Layperson’s Terms

Alzheimer’s disease (AD) is a brain disorder that affects memory and thinking ability. Scientists know that AD is caused by harmful clumps of proteins—called amyloid-beta (Aβ) plaques—that build up in the brain. However, studying AD in humans is challenging, so researchers often use mice with genetic modifications that mimic the disease.

This study looks at different types of AD mouse models and compares their brain protein changes to those seen in humans with Alzheimer’s. The researchers analysed thousands of proteins to understand how the disease progresses, how mouse models reflect human AD, and what biological processes might be involved.

One key finding is that in both mice and humans, the buildup of Aβ plaques slows down the normal breakdown and recycling of certain proteins. This means that some proteins accumulate in the brain not just because they are being produced in excess but also because they are not being cleared efficiently. This could explain why some aspects of AD develop over time.

The study also shows that while current mouse models capture some aspects of human AD, none of them fully replicate the disease. However, when different models are combined, they represent a larger portion of the changes seen in human AD. This research provides valuable insights into how AD develops and helps scientists choose the right mouse models for studying different parts of the disease. It also highlights potential targets for future treatments focused on restoring protein balance in the brain.

Practical Implementation/Social Implications of the Research

Practical Implementation

This research helps improve Alzheimer’s disease (AD) drug development by identifying key proteins involved in disease progression. It aids in selecting better animal models for testing treatments, enhances early diagnosis through biomarkers, and supports AI-driven modelling of disease progression. The findings could lead to therapies that improve protein clearance, slowing AD progression.

Social Implications

With AD cases rising globally, this study has significant public health and economic impacts. It could reduce caregiver burden, lower healthcare costs, and improve the quality of life for ageing populations. Additionally, it encourages ethical advancements in research by promoting better human-relevant models and minimising reliance on animal testing.

Collaborations

• St. Jude Children’s Research Hospital, Memphis, TN, USA.
• University of Tennessee Health Science Center, Memphis, TN, USA
• Yale University School of Medicine, New Haven, CT, USA

Future Research Plans

The future focus will be on AI-driven modelling of temporal proteomics to understand Alzheimer’s Disease (AD) progression and identify therapeutic targets. By integrating mass spectrometry-based proteomics, machine learning, and protein turnover analysis, the goal is to bridge transcriptome-proteome discrepancies and uncover key regulatory pathways.

The research aims to develop cost-effective AI-based diagnostics for AD and cardiomyopathies, utilizing multi-omics integration for early detection and personalized treatments. This approach will contribute to precision medicine and scalable healthcare solutions for a broader impact.

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