Frontiers in Life Sciences Research

Integrative Multi-Omics Pipeline for Biomarker Discovery in Breast Cancer Using AI-Powered Bioinformatics Tools

Saravanakumar Veerappan — Wed, 19 Mar 2025 00:00:00 +0300

As breast cancer is a heterogeneous disease with diverse molecular subtypes/disease behaviors and outcomes, it is a major challenge to identify early detection and personalized treatment. Based on the above, we describe in this study an integrative pipeline based on AI that can be used to identify the robust Clinically relevant biomarkers for Breast cancer. Using advanced bioinformatics tools and machine learning algorithms (e.g. random forest, LASSO regression, support vector machines, SVMs) together with genomic, transcriptomic and proteomic datasets obtained from the TCGA and CPTAC, the pipeline is used. It integrates feature selection, data normalization, cross platform harmonization and predictive modeling to discover major features that predict disease progression and prognosis of the patients. The selected biomarkers are validated by function enrichment analysis and protein protein interaction (PPI) network. Using this, we produce a multi-omics signature with the ability to classify tasks (AUC > 0.90) and correlates strongly with clinical outcomes. Potentially, this integrative framework demonstrates the role of the AI approach in the search for biomarkers to use for such actionable diagnostic approaches for personalized therapeutic strategies in breast cancer management.

Single-Cell Transcriptomic Analysis Reveals Novel Cell Signaling Networks in Human Embryonic Development

N. Arvinth — Sat, 22 Mar 2025 00:00:00 +0300

Even in the realm of human embryonic development, there is a fundamental hope to comprehend the complicated, cellular and molecular mechanisms that underlie this process. Here we report a comprehensive single cell transcriptomic analysis of early human embryos to build a map of the early signaling networks and dictate how different cell types develop. We profiled over 50,000 cells across critical time points in early embryogenesis, with high throughput single cell RNA sequencing (scRNA-seq), revealing transcriptomic signatures of ectoderm, mesoderm, endoderm, and extraembryonic lineages. These trajectories and the associated transitions were clustered and the transitions subsequently inferred. Interestingly, we observed novel signalling interaction including unique ligand receptor pair and transcriptional regulators active in niche specific way. They were next functionally enriched and pathway coactivated in cell fate commitment, tissue morphogenesis and interlineage communication. These findings demonstrate how human embryonic signaling dynamics are spatial and temporal, and set a priceless resource for regenerative medicine and developmental biology.

Preclinical Evaluation of Targeted Nanoparticle-Based Drug Delivery in Triple-Negative Breast Cancer

T M Sathish Kumar — Thu, 27 Mar 2025 00:00:00 +0300

Triple negative breast cancer (TNBC) is a special subtype of breast cancer that is currently not novelized, i.e. has no estrogen, progesterone and HER2 receptors, and therefore is difficult to be treated as well as has poor clinical outcomes. A preclinical evaluation with such a targeted nanoparticle based drug delivery system is presented in this study to enhance the therapeutic efficacy of TNBC with minimal systemic toxicity. Physicochemical properties of ligand functionalized nanoparticles with the encapsulation of doxorubicin were developed and evaluated for drug release kinetics and cellular uptake are presented. Targeted nanoparticles enhanced killing of TNBC cells versus free drug and non targeted controls in cytotoxicity assays of TNBC cell lines (MDA MB 231). These studies performed in TNBC xenograft mouse models show in vivo biodistribution and therapeutic efficacy with preferntial tumor accumulation and tumor volume reduction. Off target organ toxicity was confirmed and reduced by histopathological analysis. The implications of these findings are the potential of targeted nanoparticle platform for enhancing the therapeutic index of chemotherapeutic agents in TNBC and their potential clinical translation.

Impact of Climate-Induced Habitat Fragmentation on Pollinator Diversity in Tropical Forest Ecosystems

J. Muralidharan — Sat, 29 Mar 2025 00:00:00 +0300

A substantial proportion of global biodiversity lives in tropical forests, which are also highly important for the diversity of pollinator communities. Nevertheless, ongoing climate change has caused habitat fragmentation to proceed to the point of structure and ecological integrity of these ecosystems being altered spatially. This paper investigates how habitat fragmentation caused by climatic changes affects pollinator diversity in tropical forest landscapes, combining metrics of landscape ecology from the field, with survey of pollinator diversity, and with space for assessing climatic variability. There is a strong association between decreased pollinator species richness and abundance in forest patches with very high fragmentation and losses of specialist pollinators, including stingless bees and orchid bees. Out of these, fragment breakdown, reduced floral resource availability, and fluctuations of microclimatic conditions resulted as key drivers of decline in pollinators. Plant–pollinator interactions and mutualistic networks are severely threatened by the disruption of these plant–pollinator interactions, and their associated mutualistic networks. These results underscore the need for climate adaptive planning for biodiversity conservation in tropical ecosystems involving pollinator corridors and forest connectivity restoration.

Next-Generation mRNA Vaccines: Immunological Mechanisms and Challenges in Broad-Spectrum Viral Protection

Shaik Sadulla — Sat, 29 Mar 2025 00:00:00 +0300

The mRNA vaccine platform has dramatically changed the domain of vaccinology by providing unparalleled speed, flexibility, and scale to respond to viral outbreaks. First generation mRNA vaccines were efficacious against SARS-CoV-2, however, next generation mRNA vaccines seek to confer broad spectrum protection against multiple,if not all, virus families, variants and zoonotic threats. In this study, the principles of mRNA vaccine−induced immunology are explored, including antigen presentation, innate immune activation, and B and T cell responses orchestration. In addition, it discusses important challenges encountered in antigenic variability, delivery system optimization, immunodominance, and durability of immune memory. Potential enhancements by means of improvements in self amplifying mRNA, thermostable formulations, and multivalent vaccine design are evaluated for enhancement of cross protection and global distribution. In this paper, we integrate recent preclinical and clinical data in a comprehensive overview of the next frontier in mRNA vaccine research and its ramifications for pandemic preparedness and universal vaccine strategies.

Machine Learning-Based EEG Analysis for Early Detection of Alzheimer’s Disease in Aging Populations

K P Uvarajan — Mon, 31 Mar 2025 00:00:00 +0300

The assessment of Alzheimer’s disease (AD), the most common form of dementia, which is especially prevalent in aging populations, is addressed. However, current diagnostic tools are either expensive, invasive and/or not sensitive enough for preclinical stages and early diagnosis is critical for timely intervention. In this work, we propose a machine learning based framework based on electroencephalography (EEG) data for early detection of Alzheimer's disease. EEG provides an inexpensive, noninvasive means of recording neurophysiological changes related to cognitive decline. Feature extraction methods like power spectral density (PSD), entropy measures and connectivity metrics, together with supervised learning models such as support vector machines (SVM), random forests and deep neural networks are incorporated in the study. Classification accuracies of over 90% are achieved on benchmark EEG datasets, and the strong potential for clinical deployment of the EEG deep attention network is demonstrated via cross validation. The work describes a scalable approach for non-invasively screening for Alzheimer’s in the elderly and could help accelerate AI-driven precision neurology.