Triamcinolone Acetonide, Amlexanox and Tacrolimus as topical application for oral erosive lichen planus: Clinical effectiveness and evaluation

Aim: Oral Lichen Planus (OLP), first described by Wilson E. in 1869, is a prevalent oral mucosal disease affecting the global population. The study evaluates and compares the clinical efficacy of various topical applications: Triamcinolone Acetonide 0.1% (TA1), Amlexanox 5% (TA2) and Tacrolimus 0.03% (TA3) in the treatment of OLP.Materials and Methods: A study involving 90 patients aged 18-70 years enrolled in a randomized, single-blind, placebo-controlled clinical design enrolled them in three groups using three oral creams. Patients were monitored for allergic reactions and evaluated weekly for effectiveness and safety.Results: Relative to baseline data, the acute sensation diminished by 31% after the first week, 57% after the second week, and 74% after the third week. The intra-group comparison of burning sensation scores was performed using the Wilcoxon matched-pairs signed rank test, a non-parametric statistical method. Compared to BL, there was a substantial decrease in burning sensation (P<0.001) during the first, second, and third weeks.Conclusions: The research demonstrates that Amlexanox 5% oral paste, when used topically, markedly alleviates symptoms of OLP, diminishing the size of the erosive region and VAS scores within 28 days

IMPACT OF HUMOUR ADVERTISING ON CONSUMER BUYING BEHAVIOUR IN INDIA’S METRO POPULATION

This study examines the impact of humor-based advertising on consumer buying behavior in India’s metropolitan areas. It explores humor as a marketing tool to enhance brand likability, loyalty, and consumer engagement. By analyzing emotional connections and humorous elements, the research highlights how humor can drive consumer preferences, foster brand reputation, and increase purchase intent. Using a mixed-method approach, data was collected from major Indian cities and analyzed to understand humor\u27s influence on advertising effectiveness. While humor enhances brand recall and consumer satisfaction, challenges such as cultural sensitivity and ethical considerations necessitate careful implementation for sustained success

Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific threat data recovered from the publicly available data sets CICIDS2017 and IoT-23. Classification of network anomalies and feature extraction are carried out with the help of deep learning models such as CNN and LSTM. This paper’s proposed system complies with IEEE standards like IEEE 802.15.4 for secure IoT transmission and IEEE P2413 for architecture. A testbed is developed in order to use the model and assess its effectiveness in terms of overall accuracy, detection ratio, and time to detect an event. The findings of the study prove that threat intelligence systems built with deep learning provide explicit security to IoT networks when they are designed as per the IEEE guidelines. The proposed model retains a high detection rate, is scalable, and is useful in protecting against new forms of attacks. This research develops an approach to provide standard-compliant cybersecurity solutions to enable trust and reliability in the IoT applications across the industrial sectors. More future research can be devoted to the implementation of this system within the context of the newest advancements in technologies, such as edge computing

AI IMPLEMENTATION IN DIGITAL PAYMENT: AN EMPIRICAL ANALYSIS ON BANKING SECTOR IN INDIA

To study AI Implementation in Digital Payment, as an empirical analysis on Banking Sector in India. Questionnaires comprised of closed-ended questions were given out in order to accomplish these objectives. The elements of the proposed model were evaluated using the validity, correlation, and reliability analyses. Regression analysis was used to confirm the proposed model and test the hypothesis further. Findings shows Thus, with a R square of 0.813, all variables account for 90% of the digital payment system. The regression model\u27s ANOVA values show validation with a 95% confidence level. In the coefficient summary, the beta values of every factor are 0.902, which is a fair depiction of their impact on Digital Payment system. Practitioners and marketers in the Indian banking industry might benefit from the insights this research offers. We encourage banking professionals to think about improving their usage of AI in the credit scoring, analysis, and granting processes in order to decrease risk, save costs, and enhance customer experience. This will help with the deployment of AI-based decision-making. In addition to enabling banks to automate their knowledge workers, artificial intelligence (AI) will make automation more intelligent overall, eliminating competitiveness and security dangers. Banks will be able to provide individualized services and increase operational and financial savings by making the most use of human and machine capabilities thanks to AI. As AI is used more widely in banking, authorities working to maintain financial stability while improving consumer protection and fostering innovation will face both new opportunities and difficulties. It offers the banking industry fantastic chances to improve customer experience democratize financial services, bolster risk management, and better cyber-security and consumer protection

Enhancing the clinical skills of undergraduate medical students through simulation-based training using indigenous subcutaneous swelling models

Simulation-based learning is a novel teaching approach that involves fabricating clinical situations to immerse students in realistic scenarios, that aid in acquiring new skills without involving real patients prioritizing patient safety. It serves as an opportunity to combine theoretical knowledge with practical application providing valuable hands-on experience before students venture out to practice medicine preparing them to handle complex situations without anxiety. The integration of Simulation-based training into the medical curriculum has proven to be efficient as evidenced by multiple articles related to the use of simulation in education that were used as references. This was further substantiated by the overwhelming response obtained from this study which was conducted to explore the efficiency of simulation models in teaching the clinical examination of subcutaneous swellings among undergraduate medical students, who expressed that these models helped them in better understanding and retaining of the concept acquired

Statistical and Bioinformatics Framework for Evaluating TERT Variants: Implications in T elomere Biology and Oncogenesis

Background:Telomerase reverse transcriptase [ TERT] is a catalytic subunit of the telomerase enzyme complex that maintains genomic stability by elongating telomeres. While TERT is silenced in most somatic cells, its dysregulation is implicated in cancers, telomere syndromes, and age-related diseases. Beyond telomere maintenance, TERT participates in chromatin remodeling and cellular signaling. Understanding the functional and disease-related implications of TERT variants is essential for therapeutic advancements. The study integrates statistical rigor and bioinformatics, emphasizing the application of computational models in genomics to explore TERT’s multifaceted roles in genomic stability and cancer.Methods:We analyzed 1510 TERT variants from the ENSEMBL database using computational tools, including SIFT, PolyPhen, and CADD, to predict pathogenicity. Variants were prioritized based on thresholds [ SIFT < 0.05, PolyPhen > 0.9, CADD > 20], and clustering algorithms [ K-Means, MCL, DBSCAN] were applied to group functionally related proteins. Gene Ontology [ GO] and KEGG pathway enrichment analyses were performed using g:Profiler and DAVID to elucidate biological roles. Disease associations were explored via ClinVar, COSMIC, and literature mining.Results:266 prioritized variants showed high pathogenic potential based on functional scores. K-means clustering revealed three distinct groups, linking TERT to telomere maintenance, Wnt signaling, and DNA repair. Functional enrichment highlighted TERT’s involvement in telomerase RNA binding and telomere elongation. Disease association studies identified links to cancer [ e.g., hepatocellular carcinoma] and telomere syndromes [ e.g., dyskeratosis congenita]. Network metrics confirmed a cohesive protein interaction network, with a clustering coefficient of 0.828 and a PPI enrichment p-value of 0.000332.Conclusion:This comprehensive analysis underscores TERT’s multifaceted roles in cellular biology and its association with genomic stability and disease. By integrating clustering, enrichment, and disease association analyses, the study provides a robust framework for understanding TERT’s therapeutic potential in cancer and age-related disorders

INTERVENTIONS TO REDUCE CAESAREAN SECTION UTILIZATION RATE: A LITERATURE REVIEW

Objective: This paper aims to review and synthetize findings of interventions or policies to reduce caesarean section utilization rates.Methods: A literature review was conducted by searching database with a systematic approach from two electronic databases (PubMed and ScienceDirect) starting January 2010 up to 31 December 2022.Results: 1313 records were screened and 50 articles were selected to be reviewed, 1263 articles were excluded after screening for duplication and reviewing the title or abstract of the articles. All the 50 articles included in the review will be analyzed to study further about the intervention in reducing caesarean sections. Conclusions: There are clinical or non-clinical interventions to reduce caesarean sections rate that already been done in many countries. All interventions need multi-stakeholder strong commitment and participations to ensure the success of each intervention

Executive Presence Among Young Management and Law Students of SPMVV: An Interventional Study

Executive presence (EP) is a critical attribute for success in leadership roles, combining confidence, communication skills, and the ability to inspire trust. This interventional study explores the impact of targeted EP training on management and law students at Sri Padmavati Mahila Visvavidyalayam (SPMVV). A total of 100 students participated. They underwent a structured EP enhancement program comprising workshop on nonverbal communication, public speaking, and self-awareness. Pre- and post-intervention assessments measured changes in key EP dimensions, including selfconfidence, body language, and verbal clarity. Results revealed a significant improvement in the group, highlighting the effectiveness of the intervention in equipping future leaders with essential executive competencies. This study underscores the importance of integrating EP development into higher education curricula, particularly for management and law disciplines

Age-Stratified Safety And Effectiveness Of Streptokinase In Acute Myocardial Infarction: A Retrospective Cohort Study From Sudan

Background: Streptokinase (SK) remains widely used for thrombolysis in acute myocardial infarction (AMI) across low-resource settings. Despite its affordability and accessibility, concerns persist regarding its efficacy in elderly patients, those with delayed presentation, and individuals with prior SK exposure. Objective: To evaluate the safety and effectiveness of SK in AMI management in Sudan, with emphasis on age-related outcomes, aspirin co-administration, and comorbidities such as diabetes mellitus. Methods: A retrospective analysis was conducted on 100 AMI patients treated with SK at a tertiary hospital in Sudan. Data were stratified by age, time-to-presentation, prior SK exposure, and comorbid conditions. Outcomes included symptom resolution, adverse drug reactions (ADRs), and in-hospital mortality. Results: SK demonstrated consistent efficacy across age groups, with no significant reduction in therapeutic response among elderly patients (OR = 1.12, p = 0.81) or those with delayed presentation. Aspirin co-administration significantly improved outcomes and reduced ADR incidence (OR = 3.21, p < 0.001). Prior SK exposure did not compromise efficacy. ADRs were observed in 51% of patients, with hypotension (21%), arrhythmias (7%), and fever (6%) being the most common. Diabetic patients exhibited higher complication rates and suboptimal thrombolytic response (OR = 0.48, p = 0.032), highlighting the need for tailored adjunctive therapies. Conclusion: SK remains a safe, effective, and accessible thrombolytic agent for AMI management in Sudan. Its performance is enhanced by aspirin and unaffected by age or prior exposure. Diabetes mellitus presents ongoing challenges, warranting further research into personalized treatment strategies. These findings support revisions to national AMI guidelines and underscore the importance of context-specific thrombolytic protocols in resource-constrained environments

Impact of Different Waves of the COVID-19 Pandemic on Fetomaternal Outcome-A Medical University Experience in Bangladesh

Background: The COVID-19 pandemic disrupted maternal healthcare services in Bangladesh, with significant declines in antenatal care coverage and regional disparities in access. The purpose of this study was to analyze the effects of various COVID-19 pandemic waves on maternal and fetal health outcomes. Aim of the study: The aim of the study was to analyze the effects of various COVID-19 pandemic waves on maternal and fetal health outcomes of covid negative pregnant women. Methods: This retrospective study at the Department of Obstetrics and Gynecology, Bangabandhu Sheikh Mujib Medical University, Dhaka (March 2020–December 2022), included 2,159 covid negative pregnant women in five pandemic waves. Data on demographics, clinical presentation, and maternal-fetal outcomes were analyzed using chi-square and t-tests (p<0.05) in SPSS v25.0. Results: The mean maternal age was 27.5 ± 4.91 years, with the highest gestational age in the second wave (37.83 ± 2.26 weeks). Delivery complications peaked at 32–40 weeks (91.8%), with intrapartum bleeding most common >40 weeks and meconium-stained liquor at 28–30 weeks (p = 0.001). The second wave showed the highest rate of multiple risk factors (44.7%), while the fourth wave had the most LUCS (41.1%), NVD (34.0%), and NICU admissions (44.9%) (p = 0.001). Fetal deaths were highest in the second wave (35.3%), whereas live births peaked in the fourth wave (40.2%) (p = 0.001). Conclusion: The severity of COVID-19 and its outcomes varied across waves, with worse effects during the 2nd (Delta) wave, compared to 4th wave. This variation likely due to mass vaccination, and further research is needed to understand its impact on pregnancy