Guru Ghasidas Vishwavidyalaya

Design, synthesis, SAR, in silico and in vitro evaluation of 1, 3, 4-oxadiazole derivatives against breast cancer

The 1,3,4-oxadiazole scaffold is well recognized for its anticancer potential. Based on our previous work, we designed and synthesized nine 1,3,4-oxadiazole derivatives (R1–R9) via Schiff base formation followed by cyclization. The compounds were characterized by FT-IR, ¹H NMR, a...

Inflammation biomarkers in cancer: mechanistic drivers, diagnostic innovations, and future clinical horizons

Chronic inflammation is a universally acknowledged characteristic of cancer, playing the role of a vital catalyst in cancer initiation and progression, and a chief predictor of the success or failure of cancer therapies. About 20% of all cancer cases are associated with states of...

Photonic Hardware and Network Architectures for the Quantum Internet: From Entanglement Generation to Global Connectivity

ABSTRACT The rapid growth of global data traffic and the increasing demand for secure and scalable communication are driving the need for networking paradigms beyond the classical internet. The quantum internet has emerged as a promising solution, leveraging the principles of qua...

A critical synthesis of forest carbon dynamics in central india over two decades

India harbours one of the largest extents of tropical forest in the world, and these forests are central to the country''s Nationally Determined Contributions (NDCs) and national climate policy. While global reviews (e.g. [11, 77, 124]) have assessed pantropical carbon balances,...

Deep Learning-Based Potato Leaf Disease Detection Using Transfer Learning and Explainable AI Techniques

Objectives: To create a multi-class image classification system to automate the detection of potato crop diseases using deep learning algorithms to classify images of potato leaves. Method: This study involves an implementing and comparing of six deep learning models to classify...

Hierarchical federated edge learning with criticality-aware aggregation for privacy-preserving fetal health classification

The automated classification of fetal health from cardiotocographic (CTG) recordings is a crucial machine learning application in obstetric diagnostics. Although centralized predictive models have advanced significantly, their large-scale clinical deployment remains structurally...