Stella Maris College

Scrub typhus is a mite-borne, re-emerging public health problem in India, particularly in Tamil Nadu, South India. More than 40 serotypes of Orientia tsutsugamushi have been documented worldwide. However, the information on the circulation of its molecular sub-types in India is scanty. A retrospective study was conducted among serologically confirmed cases of scrub typhus. DNA isolated from blood was screened by a nested polymerase chain reaction (nPCR) targeting the GroEL and the 56 kDa type-specific antigen (TSA) genes. Out of 59 samples, 14 partial fragments of GroEL and the twelve 56 kDa genes were PCR-amplified and DNA-sequenced. The neighbor-joining (NJ) analysis indicated three distinct phylogenetic clades, including a novel genotype designated as Ot-Thanjavur-Tamil Nadu (Ot-TJTN, 9 nos. 64.3%); Karp-like (4 nos. 28.6%); and Kuroki-Gilliam type (1 no. 7.1%). Also, phylogenetic analysis of twelve 56 kDa variable domains (VDΙ-ΙΙΙ) of TSA gene sequences revealed a distinctive new genotypic cluster of eight samples (66.6%), and the remaining four (33.4%) were Karp-like genotypes. The Simplot analysis for the similarity and event of recombination testing elucidated the existence of the new genotype of the Ot-TJTN cluster, which was undescribed so far, in the Kato and TA716 lineages. The significant findings recommend further studies to understand the ongoing transmission dynamics of different O. tsutsugamushi strains in vector mites, rodent hosts, and humans in this region.

Increasing reliance on renewable energy sources (RES) within smart grid systems, ensuring power balance amid fluctuations in energy production and load demand presents a significant challenge. This study proposes a novel hybrid approach, termed the GJO-THDCNN technique, which integrates Golden Jackal Optimization (GJO) with a Tree Hierarchical Deep Convolutional Neural Network (THDCNN) to address this issue effectively. The proposed approach uses advanced controllers and power electronic converters to improve overall performance while integrating battery storage with solar and wind energy conversion systems. GJO generates optimized control signals, while the THDCNN enhances prediction accuracy by considering power demand, state-of-charge (SoC), and RES availability. Implemented in MATLAB, the model showcases superior performance compared to existing methods, achieving a remarkable 20% improvement in power output stability and a 30% reduction in response time to load variations. These findings underscore the GJO-THDCNN technique's potential for advancing energy management strategies in smart grids.

Machine Learning (ML) algorithms have procured a profound position in healthcare sectors, especially in diagnosis, treatments, and recommendation systems. The ML is evolving as an aiding tool for medical practitioners in disease diagnosis. Also, the feature selection reveals the latent relationships among the features, which emerge significant scope for clinical research. In the proposed study, a cognitive ensemble model (CEM) was developed to predict the probability of stroke among various subjects using highly raw clinical data. The optimal base learners are made in such a way that each of them complements one another. The proposed CEM is tested on a real-world dataset on important classification metrics. The results indicate that the CEM deployed in the healthcare sector forewarns patients regarding the probability of stroke.

Socially responsible investing (SRI), also referred to as social investment, involves making investments in businesses that are deemed socially responsible based on their operations (James, 2021). The act of engaging in the financial market by supporting companies and funds with positive social impacts is termed socially responsible investment (SRI), and it has seen a rise in popularity in recent times. It is essential for investors to recognize that socially responsible investments, though aligned with ethical considerations, remain financial undertakings. Therefore, investors should assess the potential for profitability when making investment decisions. Socially responsible investment aims to achieve two primary objectives: making a positive social impact and deriving financial benefits. However, it is important to note that these objectives are not necessarily intertwined. Just because an investment is labeled as socially responsible does not guarantee a high financial return, and the promise of a lucrative return does not guarantee the company's commitment to social consciousness. In evaluating the social worth of an investment, investors must also scrutinize its financial prospects. The study aims to present the antecedents of Socially Responsible Investing behaviour using TPB approach. The study is descriptive in nature using the primary data collected from individuals on their investment behaviour. Correlation analysis produced significant r values and it could be taken support to the hypotheses that attitude, subjective norms and perceived behavioural control are positively related to behavioural intention towards SRI. Behavioural intention has been found to be promising though not really higher values. The positive spirit towards the antecedents and usage intention is expected to persist and contribute towards the widespread and growth in Socially Responsible Investing.

Extremophiles are fascinating organisms that flourish in hostile niches like extremes of temperatures, pH, salinity, radiation and high pressure. Through evolution, these microorganisms have developed and honed their abilities to live in hostile environments through evolution. To improve the resilience of their lipids, proteins, nucleic acids and cell membranes, they rely on exclusive metabolic pathways. Of particular interest are the physiological, genetic and structural characteristics of extremophilic microbes, which allow them to withstand exceptionally particular ecological environments. They adjust to the alterations in their environment and surroundings by stabilizing their homeostasis. Evolutionary diversity enhanced enzymatic function, resistance to abiotic and biotic stresses, amino acids production, resistance to cell death, nuclear factor activation, heat shock protein (HSP) usage and cellular compartmentalization all contribute to the conservation of genes in extremophiles. These distinctive modified systems and biological processes play a vital role in various biotechnological applications. Extremophiles constitute a vital field of study for many different fields, spanning from the exploration of adaptations and survival strategies to hostile environments to proving that extremophilic products can benefit sustainable agriculture and understanding these mechanisms may lead to solutions for improvements in soil health.

Pharmaceutical supplementation and dietary fortification are the most common approaches to reducing vitamin deficits. To improve the health and nutritional value of crops, agronomic biofortification necessitates the direct application of nutrients. Producers using micronutrient fertilizers to increase the fortification of crops are essential to the success of biofortification. Overthrow malnutrition using biofortified millets notwithstanding their challenges. Millets stressors have been demonstrated to be reduced by artificial nanoparticles recently. Engineered nanoparticles (ENPs) have had their properties and functions has been reported recently. Several genes that are involved in maintaining an equilibrium of iron and zinc are genetically regulated in millet with nanoparticle formulations, resulting in even greater nutrient-by-default and stress-resilience. Millet, according to the study, is a micronutrient powerhouse because priming controls cereal iron and zinc absorption and enrichment even in the face of nutritional deficiency. This review examines millet, its health advantages, nano fertilizers, and initiatives to improve the crop production.

The anti-viral drug hydroxychloroquine (HCQ) has captivated significant interest in the pharmaceutical field, as it is a quinolone derivative. Its unrestrained occurrence causes prominent health hazards owing to its persistent, carcinogenic, recalcitrant, and teratogenic nature. Herein, in this work, an experimental investigation was carried out toward the photocatalytic degradation of HCQ drug using magnesium zirconate (MgZrO3) nanoparticles as an effective photocatalyst. A comprehensive characterizations of the as-synthesized material was carried out. The photocatalytic degradation of the HCQ drug was examined with various sources of light energies. The obtained outcomes indicated that ±85% of HCQ was degraded using a MgZrO3 photocatalyst within 30 min of the reaction time under UV–visible (ultraviolet) light irradiation. Further, other significant operational parameters such as various catalyst dosages, HCQ concentrations, pH, scavengers, and salts were examined. The degradation studies revealed that the reaction followed pseudo-first-order kinetics. Hence, this perovskite-type MgZrO3 has grasped profound attention in environmental remediation, significantly in photocatalytic degradation of HCQ drug. This comprehensive research offers green synthesis strategy as a substantial framework for providing effective photocatalyst that addresses contemporary water pollution issues linked to notable results. This aids in targeting era-driven advancements toward a clean and safe future environment.

The advancement of wearable supercapacitors (SCs) has recently garnered a lot of attention owing to their ease of fabrication into textiles, low cost, long cycle life, fast charging and discharging, high efficiency, and ability to bridge the energy and power gap between conventional capacitors and batteries. The present study focuses on the development of wearable textile-based SC electrodes using green-synthesised manganese oxide nanoparticles functionalised on poly(o-phenylenediamine) reinforced to a polymer nanocomposite. The prepared nanocomposite was characterized using spectroscopic techniques such as UV-visible spectroscopy, Fourier transform infrared spectroscopy, x-ray diffraction studies, and scanning electron microscopy to validate the incorporation of metal oxide nanoparticles into the polymer matrix. The thermal properties were studied using thermogravimetric analysis and differential scanning calorimetry. The electrochemical performance of the bare polymer and the nanocomposite was evaluated using cyclic voltammetry, galvanostatic charge-discharge, and impedance spectroscopy techniques. An impressive specific capacitance of 213 Fg⁻¹ was achieved at a current density of 1 Ag⁻¹ for the polymer nanocomposite and even after 1000 cycles a capacitance retention of 89% was observed. Enhanced antimicrobial activity was also observed for the nanocomposite against both gram-negative and gram-positive bacteria. Based on these attributes, the fabricated device can be used as an efficient antimicrobial wearable SC.

Nocardiopsis sp. strain LC-9 was isolated from freshwater sediments and explored for its varied bioactive traits. Initially, ethyl acetate extract of strain LC-9 at varied concentrations showed pronounced antibacterial activities. After column chromatography, fraction F2 and F3 of the extract were identified as prominent fractions in terms of antimicrobial activities with low minimum inhibitory concentration values. Antioxidant activities of fraction F2 and F3 revealed remarkable scavenging of free radicals with low IC50 values (DPPH – 417.86 ± 0.24 μg/mL, ABTS – 431.6 ± 0.90 μg/mL, and FRAP – 404.36 ± 0.18 μg/mL). Fractions F2 and F3 were further characterized by UV spectrum, Fourier transform infrared spectroscopy, nuclear magnetic resonance, and liquid chromatography-mass spectrometry, and were identified as Antimycin A and 4-hydroxybenzoic acid. The compounds were further tested for anticancer activity against MCF-7 cells. The MTT assay showed reduced viability of MCF-7 cells with an increase in concentration of compounds. The IC50 values for Antimycin A and 4-hydroxybenzoic acid were 9.6 ± 0.7 μg/mL and 20.8 ± 0.4 μg/mL, respectively. Staining techniques confirmed the apoptosis mechanism. Finally, molecular docking (against targeted proteins of bacteria, fungus, and cancer cells) and molecular dynamics confirmed the pharmaceutical efficacy of the purified compounds.

Plastic waste, considered a great threat to the environment, requires an effective treatment process. The ability of the microbes to oxidize the polymeric chain (C–C bonds), hydrolyze and produce carbon dioxide and water as final products of degradation was studied. The process becomes complicated due to structural complexity of the polymer. The present study is the continuation of the LDPE degradation using the Winogradsky Column. The determination of metabolites formed on degradation is discussed. The FTIR analysis indicated the reduction in the intensity of the C-H, confirming the cleavage of the alkane chains in LDPE. The metabolites produced during the degradation resulted in the formation of smaller alkanes, which contain C32, C22, C16, C18 and aromatic compounds such as phenols and benzene dicarboxylic acid. The occurrence of terminal oxidation of the polymeric chain, cleavage, fragmentation and cyclization of the alkanes confirm the biodegradation process. The current research also focuses on the biodegradation of LDPE using bacterial strains isolated from dumpsite soil samples. The degraded LDPE was analyzed for its metabolite production using GC–MS. It enabled us to understand and hypothesize an overview pathway of LDPE degradation by bacterial strains. The hypothesized pathway indicated that bacterial strains performed fragmentation and cyclization of the long polymeric chain, followed by hydrogenation and oxidation, resulting in the formation of alcohols, aldehydes, ketones and carboxylic acid compounds leading to ester formation. The esters are then understood to enter the ꞵ-oxidation pathway or TCA cycle, producing carbon dioxide and water molecules.

microRNA (miRNA)–messenger RNA (mRNA or gene) interactions are pivotal in various biological processes, including the regulation of gene expression, cellular differentiation, proliferation, apoptosis, and development, as well as the maintenance of cellular homeostasis and pathogenesis of numerous diseases, such as cancer, cardiovascular diseases, neurological disorders, and metabolic conditions. Understanding the mechanisms of miRNA–mRNA interactions can provide insights into disease mechanisms and potential therapeutic targets. However, extracting these interactions efficiently from a huge collection of published articles in PubMed is challenging. In the current study, we annotated a miRNA–mRNA Interaction Corpus (MMIC) and used it for evaluating the performance of a variety of machine learning (ML) models, deep learning-based transformer (DLT) models, and large language models (LLMs) in extracting the miRNA–mRNA interactions mentioned in PubMed. We used the genomics approaches for validating the extracted miRNA–mRNA interactions. Among the ML, DLT, and LLM models, PubMedBERT showed the highest precision, recall, and F-score, with all equal to 0.783. Among the LLM models, the performance of Llama-2 is better when compared to others. Llama 2 achieved 0.56 precision, 0.86 recall, and 0.68 F-score in a zero-shot experiment and 0.56 precision, 0.87 recall, and 0.68 F-score in a three-shot experiment. Our study shows that Llama 2 achieves better recall than ML and DLT models and leaves space for further improvement in terms of precision and F-score.

A new paradigm in education is being ushered in by online learning. In the aftermath of the pandemic, online learning has become increasingly available in both academic and professional spheres. Despite difficulties, teachers strive to improve online learning effectiveness. Further emphasis is placed on learning characteristics, readiness, environment, design, and e-learning mode. Whether synchronous or asynchronous learning modes are used will determine the efficacy of the learner. In this study, researchers analyzed the influence of learning qualities on satisfaction and effectiveness, exploring the moderating function of learning mode with 545 online learners’ results. According to this study, the characteristics of online learning significantly impact effectiveness. It also supports the idea that the modality of learning plays a moderating effect in online education. These findings have important practical ramifications for structuring the learning environment and adjusting relevant factors to the satisfaction of students, increasing e-learning efficiency.

The glasses of the composition 30PbO–60B2O3–5.5SiO2–(4.5 − x) Al2O3–xHo2O3 (with x varied from 0 and 2 mol%) were prepared. Density (ρ), refractive index (n), and average molecular weight (M) were calculated from experimental measurements of these glasses. The spectra of optical absorption besides the photoluminescence were recorded at room temperature in the wavelength range of 300–2200 nm and 450–2200 nm, respectively. Using the optical absorption spectra the oscillator strengths were estimated and found to be in good agreement with the theoretical values evaluated using optical absorption spectra. Glasses doped with 0.5 mol% holmium had the highest bonding parameter, while those doped with 2.0 mol% holmium had the lowest. From this observation, it was concluded that holmium ions are in a more covalent environment in H20 glasses. The overall analysis of the luminescence spectral results indicated maximal luminescence efficiency of the glasses with 0.5 mol%.