Background: Type 2 diabetes mellitus (T2DM) is a major cardiovascular risk factor, yet echocardiographic data from the Jazan region of Saudi Arabia are scarce. Methods: This retrospective case-control study included Adults 168 patients with T2DM and 160 non-diabetic controls, (age- and gender are matched), who underwent transthoracic echocardiography at two hospitals in Jazan in period from (October 2024 to April 2026). Clinical, demographic, and echocardiographic data were retrieved from electronic medical records. Results: Echocardiographic abnormalities were detected in 85.1% of diabetic patients. The most common findings were mitral regurgitation (40.5%), left ventricular hypertrophy (30.4%), and tricuspid regurgitation (27.4%). Diabetes duration ≥10 years was a significant risk factor (OR 8.6, p=0.041). BMI <25 kg/m² showed a protective effect in logistic regression (p=0.011), though abnormalities were prevalent across all BMI categories. Compared with controls, diabetic patients had significantly higher LVIDS, LVIDD, ESV, and aortic root area (all p<0.05), with reduced fractional shortening. Conclusion: In Jazan, echocardiographic abnormalities are common, especially among T2DM patients, where significant risk factors include diabetes duration and BMI. Cardiac screening should be performed routinely, regardless of BMI, as it helps identify and manage cardiac abnormalities.

This study explored the digital entrepreneurship in the informal economy: adoption, monetization, and profitability among open market traders in Warri Metropolis, Nigeria. The objectives of the study focused on exploring the adoption, modernization, profitability, barriers, and drivers of digital platforms use among open market traders in Warri, Nigeria. Employing a mixed methods design, survey data (200) were complemented with qualitative interviews to capture both statistical trends and lived experiences. Results show moderate adoption (mean – 3.05), with traders relying more on informal platforms such as WhatsApp and Facebook than on formal e-commerce system. Monetization remains limited (mean = 2.98), with indirect benefits, such as; boosting physical purchases than online income. Profitability perceptions are moderate (mean = 3,25), with digital marketing expanding customer reach but traditional walk-in customers remaining dominant, Barriers are significant (mean = 3.48), particularly unstable electricity and poor internet connectivity, while drivers such as education and social influence (mean = 3.23) encourage adoption. Correlation analysis revealed a positive and significant relationship between adoption and profitability (r = 0.414, p < 0.01), while regression analysis confirmed monetization as the strongest predictor of profitability (β = 3.636, p < 0.001). Qualitative findings reinforced these results, highlight infrastructural frustrations, trust concerns in online payments, and the role of younger relatives in facilitating digital engagement. Conclusively, this study demonstrate that adoption alone does not guarantee profitability; rather, effective monetization strategies are critical. The study then recommends that the constraints be addressed in order to achieve sustainable profitability.

Energy drinks are increasingly consumed due to their perceived ability to enhance physical and mental performance. However, concerns remain regarding their stimulant composition and acidic nature. This study quantitatively determined the concentrations of caffeine, taurine, and titratable acidity in six commercially available energy drink brands sold in Abuja Nigeria namely Predator, Fearless, Climax, Monster, Red Bull, and Power Horse. Caffeine and taurine were determined using High-Performance Liquid Chromatography coupled with Ultraviolet detection (HPLC-UV), while titratable acidity was determined using standard acid–base titration methods. The results showed that caffeine concentrations ranged from 129.14 ± 0.74 to 2186.66 ± 5.95 mg/L, with Climax recording the lowest level, while Power horse had the highest. Taurine concentrations varied between 59.16 ± 0.94 and 378.75 ± 0.83 mg/L, with Fearless exhibiting the highest taurine content and Climax showing the lowest concentration. Titratable acidity values ranged from 5.24 ± 0.20 to 9.77 ± 0.56 g/100 mL, indicating varying degrees of acidity among the samples, with Power Horse and Monster showing relatively higher acidity levels. The low standard deviation values recorded demonstrate the precision and reliability of the analytical methods employed and the observed variations in caffeine, taurine, and acidity among the energy drinks highlight the need for continuous quality assessment and regulatory monitoring to ensure consumer safety. This study provides baseline scientific data on the chemical characteristics of energy drinks and supports the need for stricter regulatory oversight, improved labeling, and increased public awareness regarding energy drink consumption.

Introduction: Streblus asper Lour. (Moraceae), known locally as Sheora or Khoi, has been traditionally used across South and Southeast Asian medicine systems including Ayurveda and folk practices in Assam, India for the treatment of intestinal worm infestations, filariasis, and gastrointestinal disorders. Despite this well-documented ethnopharmacological background, systematic in vitro evaluation of its anthelmintic potential using standardized bioassay models remains inadequate in the published literature. Aim of the study: To evaluate the in vitro anthelmintic activity of successive Soxhlet-derived chloroform, ethyl acetate, and hydroalcoholic (70% ethanol) leaf extracts of S. asper against Pheretima posthuma, using albendazole as a positive control, and to characterize the phytochemical profile of each extract. Materials and methods: Dried leaf powder (100 g) of S. asper, authenticated by voucher specimen (SA/BOT/2026/01), was subjected to successive Soxhlet extraction with chloroform, ethyl acetate, and 70% ethanol. Each extract was characterized by qualitative phytochemical screening. Anthelmintic activity was assessed using adult P. posthuma earthworms (n = 6 per group) at concentrations of 10, 20, and 40 mg/mL by recording time to paralysis (TP) and time to death (TD) at 37 ± 0.5°C. Data were analysed by one-way ANOVA with Tukey's post hoc test (p < 0.05). Results: Extract yields were 3.12% (chloroform), 4.56% (ethyl acetate), and 8.84% (ethanolic) w/w. The ethanolic extract tested strongly positive for tannins, saponins, flavonoids, alkaloids, and cardiac glycosides. All three extracts produced dose-dependent anthelmintic activity (p < 0.001 vs. negative control). At 40 mg/mL, the ethanolic extract produced paralysis in 23.40 ± 0.82 min and death in 39.60 ± 0.98 min, compared to albendazole at 16.00 ± 0.58 min and 27.80 ± 0.74 min, respectively. Potency ranking at all doses: albendazole > ethanolic > ethyl acetate > chloroform extract. Conclusions: The hydroalcoholic leaf extract of S. asper exhibits significant anthelmintic activity attributable to the synergistic action of tannins, saponins, flavonoids, and cardiac glycosides. These findings provide rigorous pharmacological substantiation for the ethnomedicinal use of this plant as an anthelmintic and identify it as a promising candidate for further bioactivity-guided fractionation and in vivo validation.

General higher education plays a critical role in human capital formation, economic development, and social mobility. In India, state-level higher education systems display significant variation in institutional capacity, enrollment growth, and resource allocation. Rajasthan has experienced rapid extension in general higher education institutions over the past few decades; however, the relationship between educational inputs and outputs remains deficiently studied. This study models the production function of general higher education in Rajasthan using the Autoregressive Distributed Lag (ARDL) approach. The study examines the impression of key inputs such as the number of institutions, faculty strength, government expenditure, and infrastructure capacity on educational output measured through student enrollment and graduates. The ARDL bounds testing framework is in work to analyze both short-run dynamics and long-run equilibrium relationships among variables. The findings points that faculty strength and government expenditure significantly power higher education output in the long run, while infrastructure capacity subscribe to short-run adjustments. The study finds that effective resource allocation and institutional strengthening are important to improve the productivity and efficiency of general higher education in Rajasthan.

In In the era of cyber threats evolving at lightning speed, the multinational companies (MNCs) must also incorporate an AI-driven cybersecurity framework to detect the threat, prevent intrusion, and manage the data security to continue to stay afloat. Using federated learning-based security models combined with ABSorbed ML, ABSorbed DL, and ABSorbed NLP, the AI-powered three-phase cybersecurity architecture is presented in this research for data management, intrusion detection, and real-time threat intelligence. In addition to the NSL, CICIDS, and UNSW-NB15 datasets, several AIs are used to train the AI using the AI, viz., Random Forest, XGBoost, CNN_LSTM Hybrid, Autoencoders, and Federated Learning AI in order to experiment with the effectiveness of intrusion detection. Federated Learning greatly outperformed standard security protocols: they found that Federated Learning had a collection of values of 99.0 percent accuracy and a minimum false positive rate. Few algorithms employing the use of NLP and AI for automated threat analysis had enabled proactive security intelligence, reduced detection reaction time by orders of magnitude, and enhanced IDS for intrusion detection systems. In addition, federation encryption methods also reduced the cost of computation by 2.5% and ensured high-performance data protection with homomorphic encryption and zero trust architecture (ZTA). Even in learning cybersecurity using AI-based frameworks, the adversarial attacks had suffered strong resistance, and through the usage of federated learning, the attack success rate under PGD attacks was lowest, with just a success rate of 8.5%. There are, however, several important subjects related to AI related to ethical issues, regulatory compliance, and responsibility. It leads research aimed at enhancing improved AI governance models, explainable AI (XAI), and adversarial AI defensive mechanisms for strengthening cybersecurity infrastructures in multinational corporations. After all, if used well, an AI-integrated cybersecurity framework can be utilized by MNCs to create scalable, flexible, and resilient security architecture with solid cyberthreat prevention and safe data management capabilities. Future research can also encompass a study on the federated AI cybersecurity protocols, quantum-safe cryptographic AI models, and improvements in the real-time monitoring tools in order to boost the performance of AI-driven cybersecurity defenses.

This research investigates how artificial intelligence (AI) might aid data security protocols in custodian banks. The paper evaluates custodian bankers' preparedness to adopt AI-based security solutions and the role that AI can play in securing data. To collect quantitative information from attitudes, difficulties, and readiness to integrate AI, sixty-two custodian bankers were asked to answer a structured survey. AI significantly increases the data security in risk management and fraud detection, and the majority of respondents (86.67%) agreed with this finding. It is proven that organizational readiness and financial limits have a large influence on the adoption of AI. Respondents reported being moderately to well prepared for AI, although the greatest obstacle to its deployment was budgetary restrictions. Using t-tests to test hypotheses, we were able to find that using AI actually helped data security with a mean score of 4.25 out of 5. In regression analysis, the impact of institutional readiness and budgetary limits on opinions concerning AI's ability to attract investments was identified. Cluster analysis identified three separate custodian bank groups that had different financial capabilities and preparedness. Overall, the results suggest that custodian banking needs particular tactics focused on overcoming financial obstacles and making organizations AI-ready to promote adoption of AI.