Pub Kamrup College, established in 1972, is a general degree college situated at Baihata Chariali in Kamrup district, Assam. This college is affiliated with the Gauhati University. The college fraternity as well as the locality at large is thankful to the concern doyens for their able guidance and leadership in the act of initiation of the college. Started with Arts stream, there are a total of 59 regular teachers being engaged in all the three faculties- Arts, Science and Vocational Course, and in a few professional courses in the college at a present..
Honey is an ancient natural sweetener and a traditional remedy for many illnesses. It has also been the subject of scientific interest as a functional food with diverse therapeutic uses. This review summarizes and critically evaluates the current knowledge on four widely studied medicinal honeys of diverse botanical origins, namely Manuka, Acacia, Tualang and Gelam, focusing on the relationship between their composition, biological functions and biomedical importance. The first part provides a comparative analysis of the physicochemical characteristics, nutrition and major bioactivity of these honeys, including sugars, enzymes, phenolic compounds and some specific biomarkers such as methylglyoxal. The next section summarizes in vitro and in vivo studies that report their antioxidant, antimicrobial, anti-inflammatory, wound repair and anticancer effects, along with the underlying molecular pathways. Finally, it discusses their potential applications in patents and highlights existing limitations, such as variation in composition, need for standardization and lack of rigorously designed clinical trials, which remain barriers to clinical translation. This review builds on the currently scattered literature and outlines a pathway for future research imperative to the development of honey-based therapeutic and nutraceutical applications.
Bioconvective magnetohydrodynamic (MHD) flows with varying transport characteristics are of significant interest due to their applications in energy management, biological engineering, and the environment. The objective of this work is to explore MHD boundary layer flow through a Darcy-Forchheimer permeable medium across a moving horizontal surface involving gyrotactic microorganisms. The novelty of this work accounts for the combined effects of variable viscosity, variable thermal conductivity, nonuniform heat source, Joule heating, and activation energy. The flow is supposed to be steady, laminar, incompressible, and electrically conducting, determined by Brownian motion and thermophoresis with the Buongiorno model. To simplify the governing equations, suitable similarity variables are used to convert them to a structure of ordinary least squares. The changed equations are numerically cracked using MATLAB's built-in solver BVP5C. After verifying the computational frame, numerical replications are run to investigate the fluctuation in velocity, temperature, concentration, and density of microbes, as well as numerous other important physical factors. The impacts of major factors are methodically examined and displayed using 2D and 3D graphical representations, as well as tabular data. The results show that increasing the temperature-dependent viscosity and thermal conductivity parameters reduces the velocity and temperature gradient, respectively. Also, the activation energy amplifies the concentration profile. Our study is consistent with the previously published work. This work enhances our understanding of the composite interaction among flow mechanism, particle movement, and microbial metabolism, which may have significant implications for an extensive variety of technical and biological applications.
Phytochemicals like alkaloids, flavonoids, terpenoids, and others have many health benefits, but their safety can be a concern due to high doses, structural modifications, or interactions. However, traditional tests using cells, animals, or organ-like models give useful results but are costly, time-consuming, and raise ethical issues. Artificial intelligence (AI) and machine learning (ML) provide faster ways to predict toxicity from chemical and biological data. Methods such as QSAR, random forests, and deep learning can detect organ-specific risks, and combining them with omics data makes predictions more accurate. Integrating lab tests with AI predictions gives a balanced approach that confirms results, guides experiments, and reduces animal use. This helps in discovering safer drugs, supporting regulations, and developing plant-based medicines more quickly, reliably, and ethically discovery, regulatory evaluation, and the development of plant-based medicines in a faster, more reliable, and ethical way.
