Ethiraj College for Women

Infertility in males accounts for a significant proportion of cases from around the world, impaired sperm motility being one of the most common etiologies. Sperm motility is an extremely coordinated biological process that involves the integrity of flagellar architecture, energy metabolism, ion channel signaling, hormonal control, and genetic control with high precision during spermatogenesis. Genetic alteration of these processes can disrupt sperm locomotion and decrease male reproductive potential.The present narrative review integrates current knowledge on genes that regulate sperm motility by incorporating structural, energetic, regulatory, and developmental mechanisms. It reviews the molecular roles and corresponding motility characteristics of genes associated with axonemal structure, calcium channel signaling, mitochondrial function, and hormone pathways. Furthermore, an increasing amount of data indicates the significance of epigenetic regulation and the integrity of the mitochondrial genome in sperm motility abnormalities. In order to help identify genetic disorders related to motility, this study also looks at advancements in laboratory and genetic diagnostic techniques, including imaging modalities, functional assays, and high-throughput genomic technologies.By offering a mechanistic framework that connects genetic variation to cellular pathways and clinical symptoms, this study highlights the translational value of genetic findings toward the better diagnosis and treatment of male infertility. The integrative perspective offered here focuses on a future path for research toward tailored diagnostic techniques and focused therapy therapies for sperm motility abnormalities.

The plant parts of Pongamia pinnata (also known as Milletia pinnata) like pods, leaves, seeds and bark, are recognized as excellent adsorbents for the removal of heavy metal ions and dyes from aqueous solutions. Pongamia has been classified as an excluder and also as a hyperaccumulator depending on the contaminant. It shows greater efficiencies for the removal of heavy metals like Ni(II), Co(II), Cr(VI), Zn(II), and a wide variety of dyes like Methylene Blue, Malachite green, Grey BL, Rhodamine B. This review paper aims to analyse and summarize the existing research works on heavy metal-bearing pollutants uptake by raw, chemically modified, activated and carbonised Pongamia pinnata (PP) plant parts. Reports reveal that uniform particle sizes of the adsorbent were achieved using mesh sizes ranging from less than 2 mm to 200 mm,100–300 μm and 75–150 nm, depending on the need and availability. Many researchers have reported the use of chemical reagents like H2O2, H2SO4. H3PO4, HCl, KOH, NaOH, and ZnCl2 for the surface activation of pulverised plant parts. The characterization studies of the PP adsorbent were carried out through pore structure analysis, Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy. Batch adsorption process was the widely used technique among all the other available methods, as it is simple and economically viable, as reported by researchers. The effect of various controlling parameters like pH, dosage variation, effect of contact time, and initial concentration was studied using this batch adsorption technique. As reported by the various researchers, from the Batch adsorption studies the removal efficiency of heavy metals and dyes exceeds 90

This research aims to assess and compare the effectiveness of XGBoost and Multi-Layer Perceptron (MLP) neural network models, implemented with TensorFlow/Keras, in predicting stress levels from various physiological and psychological indicators. The study focuses on determining which model delivers superior performance in terms of accuracy, precision, recall, and overall prediction efficiency for stress classification. This research employed the Student Stress Monitoring dataset obtained from Kaggle, containing 843 anonymized survey responses from college students aged 18-21, encompassing psychological, physiological, environmental, academic, and social stress factors. The analysis involved applying two predictive modeling approaches-XGBoost and a Multi-Layer Perceptron (MLP) neural network developed using TensorFlow/Keras. The results of the research suggest that Deep Learning algorithms possess the ability to offer accurate forecastsStudents Stress level. The significance level of this study is indicated by $\mathbf{p}=\mathbf{0. 0 4 2}$, where $\mathbf{p}<\mathbf{0. 0 5}$. This indicates that there is a statistical significance observed between the two algorithms. Findings reveal that the MLP model outperformed XGBoost with a higher accuracy of 89% versus 86.82%, along with improved precision, recall, and F1-scores for most categories. Although XGBoost offered quicker training and prediction times, the MLP model delivered better overall effectiveness in classifying stress levels.

Microbial identification aids in understanding microbial diversity and ecological roles. Characterization techniques rely mainly on the DNA or RNA composition of bacteria and biochemical parameters tested in laboratories. Bacterial fatty acid composition is a reliable biochemical fingerprint that supports accurate bacterial identification, classification, and understanding of evolutionary relationships. Bacteria contain straight and branched-chain fatty acids, unusual fatty acids, hydroxy acids, internally branched fatty acids, ω-cyclic fatty acids, hydroxy and cyclopropane fatty acids, dicarboxylic fatty acids, ladderane fatty acids. The fatty acid profile of an organism is a stable chemotaxonomic marker because it generates species specific profiles that can differentiate species and genera. It also reflects the genetic makeup of an organism since fatty acid biosynthesis pathways are genetically encoded thus aiding phylogenetic inference. Determination of fatty acid profile of an organism can also indicate its ecological adaptations. Techniques like gas chromatography–fatty acid methyl ester (GC-FAME) analysis allow for rapid and reproducible profiling for confirming bacterial identity from environmental and clinical samples. It can complement other molecular identification methods by providing phenotypic data to resolve ambiguities if any that may arise in genetic classification. Fatty acid composition can be compared with standard fatty acid databases using the Sherlock Microbial Identification System (MIS) a system that generates a Similarity Index and expresses the relative distance between the unknown entry profile and the closest library match. This review highlights GC-FAME analysis for microbial identification and discusses the characterization of the paddy rhizosphere bacterium, Brevibacillus centrosporus , through GC-FAME analysis, which demonstrated potential bioinoculant activity by enhancing seed germination and shoot growth in Vigna radiata.

Mullite-type bismuth ferrite (BFO) nanoparticles were synthesized using a combustion technique. Various analytical techniques, including X-Ray diffraction (XRD) spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray (EDS) spectroscopy with elemental mapping, Vibrating Sample Magnetometry (VSM), Fourier Transform Infrared Spectroscopy (FTIR), and ultraviolet (UV) spectroscopy, were utilized to analyze the powder samples. XRD examination revealed that BFO samples were entirely orthorhombic following calcination at 750°C. Agglomeration, associated with combustion, was observed in FESEM micrographs. Energy Dispersive X-ray and mapping investigation verified BFO nanoparticles. UV-Vis examination of BFO nanoparticles revealed band gap energy (Eg) of 1.92 eV. The Fe–O–Fe bending motions connected to iron atoms in tetrahedral coordination are the source of the vibrational peak that appears at about 618 cm–1. The formation of mullite-type Bi2Fe4O9 has been proven by the FTIR spectra, which demonstrate distinctive Fe–O stretching along with Fe–O–Fe and Bi–O vibrational modes. The M-H curves of our powders were antiferromagnetic. The elemental constituents and their corresponding oxidation states in the BFO nanomaterials were identified through analysis of the XPS peak regions. The BET surface area of BFO nanoparticles was 2.98 m2/g. As-fabricated BFO nanoparticles demonstrated over 96