Exploring the Gut Microbiota-Physical Activity Nexus: A Multidisciplinary Approach toward Sustainable Health Education in the Indian Context
Studies on Mechanical, Microstructural, Morphological and Thermogravimetric Characterization of Bio-Composite Based on Poly Lactic Acid Reinforced with Banana Fibre for its Multifaceted Engineering Applications
mRNA–Lipid Hybrid Nanovaccines: A Next-Generation Strategy for Broad-Spectrum Viral Immunity
Image-Based Lumpy Skin Disease Diagnosis: A Comprehensive Review of Deep Learning Models
Network Biology Approaches for Functional Gene Module Discovery: Tools, Techniques, and Applications in Functional Genomics
AI Driven Biotechnology for Climate Resilient Agriculture, Healthcare and Food System
Biochemical Test for Identification of Proteins by Biuret Reagent using Microscale Methods
Green Marketing for Environmental Sustainability: A Critical Review
Development of an Edu-Blog in Biology for Class X Equivalency Programme Learners under the Kerala State Literacy Mission Authority
Sustainable Engineering Approaches using Quantum Algorithms to Predict Oceanic Plastic Drift Patterns
The Role of Awareness in Reducing Antinutrients for Enhanced Nutrient Absorption: A Review
Diversity Assessment of Insect Species in the Kariavattom Campus, University of Kerala
Exploring the Gut Microbiota-Physical Activity Nexus: A Multidisciplinary Approach toward Sustainable Health Education in the Indian Context
Studies on Mechanical, Microstructural, Morphological and Thermogravimetric Characterization of Bio-Composite Based on Poly Lactic Acid Reinforced with Banana Fibre for its Multifaceted Engineering Applications
Network Biology Approaches for Functional Gene Module Discovery: Tools, Techniques, and Applications in Functional Genomics
Neem Root Mediated-Biosynthesis of Silver Nanoparticles: Antimicrobial Activities and Application as Detergent Additive
The human gut microbiota plays a foundational role in health, particularly influencing physical performance, metabolism, and cognitive well-being. This paper explores how insights from gut microbiota science can be integrated with physical education, especially in the Indian context, where nutrition, hygiene, and exercise patterns vary widely across regions. With rising rates of non-communicable diseases (NCDs) and mental health challenges among Indian youth, a multidisciplinary model linking microbiome awareness with physical education is both timely and transformative. The study also emphasizes the role of AI, digital health platforms, and indigenous health knowledge systems in promoting sustainable well-being, aligned with India's National Education Policy (NEP 2020) and the UN Sustainable Development Goals (SDGs).
The present work elaborates the ongoing work in banana fiber reinforced in bioplastic for the making of green and sustainable composites. Non-woven banana fiber mats were incorporated as reinforcement into polylactic acid (PLA) and processed using a compression-molding hydraulic press. Fiber loadings of 20, 25, 30, 35, and 40% were investigated. The resulting composites exhibited notable improvements in tensile, impact, and flexural properties up to 35% fiber content, with marginal gains at 40%. Maximum tensile strength (64 MPa), flexural strength (49 MPa), and impact energy (2.2 J) were all achieved at 35% reinforcement. Hardness decreased progressively with increasing fiber content. Thermal analysis indicated that adding banana fiber reduced the onset degradation temperature of PLA, leading to earlier composite degradation. Overall, the findings support the potential of banana fiber–reinforced PLA composites as low-cost, eco-friendly materials suitable for everyday products such as basins, baskets, and household accessories, contributing to sustainable material development.
Messenger RNA (mRNA)–based vaccines have revolutionized modern immunization by offering a flexible and rapid platform for combating infectious diseases. When combined with lipid nanoparticles (LNPs), these vaccines gain enhanced stability, targeted delivery, and efficient cellular uptake. The integration of mRNA technology with lipid-based nanocarriers has opened new possibilities for developing broad-spectrum vaccines capable of inducing strong and durable immune responses against multiple viral pathogens. This innovative hybrid approach enables the delivery of multiple antigen-encoding mRNAs within a single formulation, promoting both humoral and cellular immune responses. Moreover, lipid nanoparticles protect the fragile mRNA from enzymatic degradation and facilitate endosomal escape, ensuring efficient protein expression in host cells. Such designs can be fine-tuned to address emerging viral variants, including influenza, coronavirus, and other zoonotic threats. Beyond their immediate role in pandemic preparedness, mRNA–lipid hybrid nanovaccines represent a transformative step toward personalized immunization and universal antiviral defense. Continued research into optimizing lipid composition, immune adjuvants, and storage stability will be critical to realizing their full potential in global public health.
Lumpy Skin Disease (LSD) is a viral infection that impacts cattle. This may result in financial setbacks in the dairy and livestock sectors. Timely identification of the illness is essential for improved treatment and for halting its transmission. Conventional diagnostic approaches, including clinical assessments and lab examinations, require considerable time and resources. Recent advancements in artificial intelligence, particularly in image processing through machine learning, offer efficient methods for automated LSD detection. This evaluation provides an examination of existing techniques, contrasting their advantages and disadvantages. Key obstacles in practical implementation are examined, and avenues for future studies are proposed to enhance the precision and effectiveness of LSD detection systems.
Functional genomics aims to understand the dynamic aspects of gene expression and function at a systems level. Network biology offers a powerful framework to uncover functionally coherent gene modules by integrating various types of biological data. This review summarizes the current tools and computational techniques used for functional gene module discovery, highlighting their theoretical foundations, strengths, and limitations. Recent advances in integrating multi-omics data, single-cell analyses, and the application of machine learning are also discussed. Emphasis is placed on the biological relevance and translational potential of identified gene modules in areas such as disease mechanism elucidation, biomarker discovery, and therapeutic target identification.
Artificial intelligence is emerging as a game-changer for farmers coping with the escalating challenges of climate change, as AI models can predict and mitigate its wide-ranging impacts on agriculture while providing advanced decision-support tools. As environmental issues intensify, artificial intelligence integration is shifting the landscape toward climate-resilient agriculture. To address the complexities of climate unpredictability, this overview discusses how artificial intelligence assists farmers in making adaptive decisions. The advantages of artificial intelligence and climate research working together to identify climate-related risks—such as extreme weather, altered precipitation patterns, and emerging pest threats—are examined, along with its impact on smallholder and rural farmers to enhance overall resilience. A thorough analysis is conducted on the potential benefits and challenges of widespread artificial intelligence adoption across diverse agricultural contexts. Artificial intelligence-powered technologies combining computer vision, deep learning, reinforcement learning, and predictive analytics enable accurate climate forecasting, early disease detection, and efficient resource utilization. Furthermore, reinforcement learning and Internet of Things (IoT) integration support smart irrigation systems and adaptive decision-making under unpredictable climate conditions. This overview provides a comprehensive analysis of artificial intelligence and machine learning applications in precision agriculture, climate-smart farming, and sustainable land management.
