Polycystic ovarian syndrome (PCOS) is one of the known causes of anovulatory fertility in the world. Previous research has linked oxidative stress could contribute to PCOS, and vanillic acid has shown antioxidant potential. Hence, the present study evaluated the effect of vanillic acid on letrozole-induced polycystic ovarian syndrome in female rats. PCOS was induced in Wistar female rats with letrozole (1 mg/kg, orally) in carboxymethoxycellulose (1 % w/v), administered for 21 days. After induction, the standard group received clomiphene citrate (1 mg/kg, orally) while other treatment groups were administered with vanillic acid at doses 25, 50, and 100 mg/kg, orally for 15 days, and without treatment was considered a negative control group. Different parameters studied were body weight, ovary weight, blood glucose, lipid profile, hormonal levels [luteinizing hormone (LH), follicle-stimulating hormone (FSH), and testosterone], markers for oxidative stress (superoxide dismutase, reduced glutathione, catalase, and malonaldehyde), and histopathology of the ovary. Statistical analysis was done for the results and p < 0.05 was considered to indicate the significance. Vanillic acid-treated animals showed a concentration-dependent activity on the tested parameters. The highest tested dose (100 mg/kg) produced a more prominent effect in significantly (P < 0.001) decreasing the body weight, and ovary weight and improving the hormonal imbalance. Also, vanillic acid significantly (P < 0.01) reduced elevated blood sugar and lipid levels. Additionally, vanillic acid reduced oxidative stress significantly (P < 0.001) in the ovaries of female rats. Histopathological reports showed a reduction in cystic follicles and appearance of normal healthy follicles at different stages of development after the administration of vanillic acid. Furthermore, these effects were observed to be comparable with those recorded for standard drug, clomiphene. The current study data suggests that vanillic acid has protected the letrozole-induced polycystic ovarian syndrome. In the event of several side effects associated with conventional treatments used for PCOS, the findings of this study suggest the promising role of vanillic acid. More research in this direction might identify the true potency of vanillic acid in the treatment of PCOS.

Generic medications are cost-effective without compromising therapeutic outcomes. Therefore, the goal of this study was to investigate, using a cross-sectional study design, the factors influencing Saudi Arabian consumers' preferences between innovator and generic medications. This cross-sectional study was carried out in Saudi Arabia using a Google survey form. For data collection, a simple random sampling strategy was used. The recruited participants were surveyed using a validated questionnaire that focused on six influencing domains: physician, pharmacist, perceived effectiveness, price, information availability, and confidence based on prior experience. The obtained data was used to analyze factors that have an association with any of the six domains using multinomial regression analysis. A correlation analysis was performed to examine the relationship between domains. The 317 participants included 64.4 % females, 52 % aged ≥ 26, and a large proportion of Saudi nationals (82.6 %) and university graduates (78.9 %). Being employed (OR:3.029; P = 0.006; CI: 6.715–1.366), a healthcare providers (OR:2.298; P = 0.043; CI: 5.151–1.025), and having insurance coverage (OR:1.908; P = 0.017; CI: 3.245–1.122) had a greater influence on medication selection. Participants with linguistic and business educational backgrounds (OR:3.443; P = 0.022; CI: 9.950–1.191), those living in the northern region of Saudi Arabia (OR:3.174; P = 0.009; CI: 7.585–1.328), having chronic ailments (OR:3.863; P = 0.013; CI: 11.274–1.324), and possess insurance (OR:1.748; P = 0.039; CI: 2.971–1.028) get readily influenced by pharmacist. People who were married and lived in Saudi Arabia's southern region were influenced by perceived effectiveness when choosing medicine. Participants from the northern region were found to be influenced by the price of the medicines, information about the medicines, and confidence based on previous experience. The price of medicines has a significant impact on those suffering from chronic diseases. At a significant level of P = 0.01, all six influencing domains were found to be positively correlated with each other. The study shows that healthcare providers, drug prices, perceived efficacy, and information availability all have a big influence on the Saudi Arabian population's choice of medications. Educational background, location, and chronic disease status are associated with several influencing domains. Aside from public awareness campaigns, healthcare professionals should be involved in the implementation of the generic medication policy.

BackgroundThe emergency medical services (EMS) sector presents substantial hurdles in providing excellent treatment while operating within limited resources. Artificial intelligence (AI) has significant potential in prehospital EMS, where rapid and accurate decision-making is essential. However, a comprehensive synthesis of AI’s effectiveness in this setting remains limited. The objective of this systematic review and meta-analysis was to evaluate AI’s predictive accuracy, compare its performance with human-based triage, and assess its feasibility in prehospital care. MethodsFollowing PRISMA guidelines, PubMed, Cochrane Library, and Web of Science were searched up to February 20, 2025. Fourteen peer-reviewed studies reporting quantitative outcomes were included. Study quality was assessed using the Newcastle-Ottawa Scale (median score = 7). Random-effects meta-analyses in R were conducted to synthesize area under the curve (AUC), AI-human AUC differences, and feasibility outcomes. ResultsAcross 14 studies involving 9,107,906 patients, AI demonstrated strong predictive performance with a pooled AUC of 0.874 (95% CI: 0.843–0.905). Individual study AUCs ranged from 0.805 to 0.997, with moderate heterogeneity (I²=52.3%). In eight studies, AI significantly outperformed human triage, showing a pooled AUC improvement of 0.074 (95% CI: 0.045–0.103; p<0.001), despite high heterogeneity due to varying human benchmarks. Feasibility analysis showed high clinical utility, with a pooled proportion of 0.929 and no heterogeneity (I²=0%). Funnel plots suggested slight asymmetry, but Egger’s tests indicated no significant publication bias. ConclusionArtificial intelligence demonstrated high predictive accuracy, slight superiority over human triage, and strong feasibility in prehospital care, supporting its integration into EMS with further validation and standardized reporting.

Background: Medication adherence is critical for achieving optimal glycemic control and preventing diabetes-related complications. However, poor adherence remains a major barrier to effective diabetes management worldwide. Evidence regarding factors associated with medication adherence among adults with diabetes in Saudi Arabia remains limited. This study aimed to assess medication adherence among adults with diabetes and examine associations of sociodemographic, clinical, pharmacist-related, and digital health factors with adherence levels. Methods: A cross-sectional study was conducted among adults with type 1 or type 2 diabetes attending a specialized diabetes center in Jazan, Saudi Arabia, during February–March 2025. Participants were recruited using non-probability convenience sampling. Of 489 patients approached, 388 completed the questionnaire, yielding a response rate of 79.3%. Data were collected using a bilingual (Arabic/English) electronic questionnaire. Medication adherence was assessed using the 8-item Morisky Medication Adherence Scale (MMAS-8). Descriptive statistics, bivariate analyses, correlation analysis, and multinomial logistic regression were performed using IBM SPSS Statistics version 27. Results: Overall, 77.1% of participants demonstrated low medication adherence, while 16.5% and 6.4% demonstrated moderate and high adherence, respectively. In the adjusted multinomial logistic regression model, higher satisfaction with the current diabetes management plan was significantly associated with greater odds of moderate adherence (OR=1.650, 95% CI: 1.212– 2.245, P=0.001) and high adherence (OR=2.038, 95% CI: 1.165– 3.565, P=0.013) compared with low adherence. Insulin use was associated with moderate vs low adherence (OR=2.175, 95% CI: 1.008– 4.692, P=0.048), but not with high adherence. Participants who did not use mobile applications or digital health tools had higher odds of high vs low adherence than those who used such tools (OR=2.685, 95% CI: 1.007– 7.154, P=0.048). Age and comorbid conditions were not significantly associated with adherence level in the adjusted model. Frequency of pharmacist consultation was significant overall (P=0.001), although individual category comparisons did not demonstrate a consistent pattern across adherence levels. Multicollinearity diagnostics indicated no problematic collinearity (VIF=1.04– 1.34; tolerance ≥ 0.75). Conclusion: Medication adherence was predominantly low among study participants. Higher satisfaction with diabetes management was consistently associated with greater adherence, while insulin use and digital health tool use showed category-specific associations. The unexpected association between digital-tool non-use and high adherence should be interpreted cautiously and does not establish a causal effect of digital technology on adherence. Further longitudinal and prospective studies are warranted to clarify these associations and determine how structured pharmacist engagement and personalized digital-health strategies can be optimally incorporated into patient-centered diabetes care.