Artificial Intelligence (AI) has emerged as a transformative force in education, particularly in the field of language learning and academic writing. AI-powered writing tools such as grammar checkers, paraphrasing applications, automated feedback systems, and generative writing assistants have significantly altered how students learn and practice English writing skills. These technologies provide instant feedback, language correction, vocabulary enhancement, and writing suggestions, enabling learners to improve their linguistic competence and writing efficiency. However, the increasing dependence on AI-generated content raises concerns regarding originality, critical thinking, academic integrity, and the development of independent writing abilities. This study explores the impact of AI writing tools on English language learning and academic writing practices. It examines both the benefits and challenges associated with AI-assisted writing and discusses their implications for learners, educators, and academic institutions.
The growing demand for environmentally friXc007A CVHB endly materials has driven research into innovative strategies for recycling and improving the mechanical performance of composites. This study investigates a novel sandwich composite comprising a core of recycled Polyethylene terephthalate (PET) bottle waste and kenaf fiber-reinforced vinyl ester face sheets, with a particular focus on the effects of silane coupling grafting. The objective is to develop a high-strength, lightweight composite suitable for load-bearing applications. Two composite formulations were fabricated and subjected to various aging conditions to evaluate their performance under different environmental scenarios. The synergistic combination of silane treatment, PET core, and kenaf fiber significantly influences the composites’ mechanical, flammability, and thermal properties, enhancing their versatility across potential applications. Based on measured properties, KPR1 () emerged as the best-performing composite, exhibiting consistently high values across all mechanical tests: tensile strength of 42 MPa, flexural strength of 79 MPa, impact energy of 3.5 J, and interlaminar shear strength (ILSS) of 16 MPa. In contrast, KPH2 exhibited the highest thermal conductivity of 2.00 W/m · K, which further increased with silane treatment, highlighting its potential for efficient heat dissipation. Notably, this composite also demonstrated the slowest flammability propagation rate at 13.3 mm/min. Preliminary findings indicate that silane coupling grafting substantially enhances the mechanical performance of these sandwich composites. The modified formulations exhibit superior resilience to environmental stressors, increased rigidity, and improved load-bearing capacity. These results underscore the potential of such composites for structural applications where a balance of strength, weight, and environmental sustainability is essential, advancing the development of high-performance, eco-friendly materials.
Urolithiasis is a condition where kidney stones exit through the renal pelvis, causing chronic pain. Kidney stones are usually formed due to the deposition of calcium and oxalate crystals. It is known to be a prevalent health condition that affects a large portion of the global human population. Inopportunely, no medications that show effective prevention of urolithiasis are currently available. Extensive research has highlighted the significant role of commensal microbes in regulating host oxalate homeostasis and oxalate-associated pathological conditions. However, limited knowledge of the pathophysiology of urolithiasis poses difficulties in designing target-based therapeutics. Growing evidence suggests the role of gut microbiota and probiotics in helping reduce the disease burden. Understanding the intricate relationship between gut-associated microbiota and its host symbiosis reveals the therapeutic potential of specific bacteria to prevent and/or treat such metabolic diseases. Oxalobacter formigenes, a bacterium, is considered crucial for degrading dietary oxalates through the oxalyl-CoA decarboxylase enzyme. The absence of this enzyme leads to hyperoxaluria and calcium oxalate urolithiasis, underscoring the impact of microbiota on kidney stone formation. Studies on the urinary microbiome, including those focusing on Oxalobacter formigenes, Lactobacillus, Bifidobacterium, Eubacterium lentum, Enterococcus faecalis, and Escherichia coli, elucidate the metabolism of dietary oxalates, providing a novel approach to kidney stone management. This review aims to consolidate the present information on the urinary microbiome, aetiology, pathogenesis, and disease prevention.
Incretin hormones secreted by intestinal endocrine cells cause 60% of the postprandial insulin release whose modulation helps in glucose-lowering actions in blood. DPP-4, a serine exopeptidase, breaks dipeptides from polypeptides like incretin hormones, using proline or alanine amino acid at the N-terminal of the peptide chain, which makes DPP-4 helpful in glucose homeostasis, immunology, blood pressure and neurogenic inflammations averting hypoglycaemia and weight gain in comparison to rest of the antidiabetic agents. Thiosemicarbazone derivatives inhibit α-glucosidase, which is responsible for transformation of carbohydrates into glucose and are helpful in reducing postprandial hyperglycemia with simultaneous inhibition of the action of enzymes viz glycogen phosphorylase, aldose reductase (ALR2), α-amylase, and dipeptidyl peptidase-4 (DPP-4) and thus act as multifaced molecule in preventing the sugar formation in the blood. Pyrazole moiety, having a natural origin, is biocompatible, inhibitors of DPP-4 and found in Teneligliptin, which has been rendered approval in Japan for treating type 2 diabetes. The present research reports the incorporation of pyrazole moiety into thiosemicarbazones, which have shown promising in vitro antidiabetic activities through their inhibition to DPP-4 at 100 µM. Among the synthesized compounds 4d, 4f, 4a, 4b exhibited >70% inhibition to DPP-4. Compound 4d showed 98.0% inhibition of DPP-4 and found it to be better than Sitagliptin (96.5% inhibition). The synthesized compounds exhibited a promising safety profile to normal cells with lower toxicity to L929 cells (IC50 > 600 µM). The antidiabetic profile of the synthesized compounds was further fortified by the in-silico analysis including ADMET profiling, pharmacokinetic properties, bioavailability, and docking studies.
Diabetes is a chronic disorder that affects how many people around the world process their metabolism. The prevalence of diabetes is increasing alarmingly every year. Diabetes can harm various essential organs in the body if it is not managed well. Therefore, it is very important to identify diabetes early and initiate treatment as soon as possible to avoid the disease from causing such complications. This research applied five different techniques in WEKA tools to forecast diabetes based on the input attributes of the dataset. This research used 17 attributes, such as Age, Sex, Polyuria, Polydipsia and other medical terms to assess the likelihood of a patient developing disease. Nowadays Filtering techniques are crucial in various fields and applications, serving to extract relevant information, enhance data quality, and improve overall system performance. The filters—Resample, Obfuscate, and Discretizeare important for preprocessing data for machine learning algorithms. They help to overcome challenges related to data imbalance, privacy protection, and data representation, resulting in enhanced model performance and data usability. These filters are vital for achieving high-quality data preprocessing, feature engineering, and privacy preservation in machine learning workflows. Preprocessed data applied to the classification techniques, namely JRip, SMO, KNN, Logistic and SGD used to examine the diabetes. The performance measurement for this study are the mean absolute error and kappa statistics and the accuracy of correct classification, of the classifier. The results show that Overall, employing these filters in the preprocessing stage assists to optimize data preparation and establishes the basis for successful machine learning outcomes
