Methods: The questionnaire was developed from the literature together with outcomes from focus group discussions. It was divided into two domains which are knowledge on ME and attitude towards ME reporting. Content validity index (I-CVI), exploratory factor analysis (EFA), Cronbach alpha and intraclass correlation coefficient (ICC) to assess test-retest reliability were obtained during the validation process.

Results: Overall Cronbach alpha for internal consistency was good (0.742), where subscale of the questionnaire demonstrated adequate internal consistency, with Cronbach alpha value 0.83 for knowledge and 0.70 for reporting behaviour attitude. The I-CVI showed good scores (knowledge=0.88) and (attitude=0.81), while ICC was moderately accepted with a value of 0.77. Two factors were extracted from the 16 items in EFA.

Objective: To evaluate the knowledge and attitude of university students regarding antibiotics.

Methods: A cross-sectional study was conducted on university students at a private university in Malaysia in 2018, using a self-administered validated questionnaire.

Results: Of the 424 respondents included in this study, 25.7% had taken antibiotics in the previous month while 22.6% had good knowledge about antibiotics. The faculty to which the respondents were attached was the only determinant associated with the respondents' knowledge level on antibiotics. The odds of respondents from other faculties having poor knowledge level on antibiotics were significantly higher than those from the Faculty of Health and Medical Sciences (P<0.001). The study found that 77.8% of the respondents knew the roles of antibiotics in treating bacterial infections, but only 27.4% knew that antibiotics were not for viral infections. However, 46.5% of the respondents recognized that penicillin is an antibiotic, but only 35.4% and 9.2% knew that aspirin and diphenhydramine, respectively, are not antibiotics. More than 70% of the respondents were aware that antibiotics have side effects and that overuse of antibiotics could lead to resistance. However, 35.6% expected antibiotics to be prescribed for common colds and 33% would not complete their course of antibiotics.

Methods: The current study was cross-sectional population-based and used a convenient sampling technique. Moreover, Lorenz's formula was used to calculate the sample size and the required sample size was 480. Data were collected through face-to-face interviews with a pre-validated questionnaire and the study was conducted in Kuala Lumpur (Malaysia). Descriptive statistics, cross-tabulation, and logistic regression were executed by using SPSS version 24.

Results: Out of 480 participants, 45.8% were polled male and the prevalence of SMA in this study was found to be 15.1%. The majority of participants 23.1% indicated that they practiced antibiotics at least once in the last six months. The commonly self-medicated antibiotics were amoxicillin-clavulanate 20.6%, ampicillin/cloxacillin 14.2%, and levofloxacin 8.3%. Moreover, 64.8% of participants indicated that they bought their antibiotics from pharmacies. Whereas, most of the participants practice antibiotics to save money 19.2% and time 23.1%. Findings from multivariate logistic regressions showed that predictors of SMA were male gender, (95% CI: 0.300-0.877) occupation (95% CI: 0.122-10.797), health insurance (95% CI: 0.025-0.472), and education (95% CI: 0.084-0.800).

METHODS: AI-based chatbots (ie, ChatGPT-3.5, ChatGPT-4, Microsoft Bing AI, and Google Bard) were compared for their abilities to detect clinically relevant DDIs for 255 drug pairs. Descriptive statistics, such as specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), were calculated for each tool.

RESULTS: When a subscription tool was used as a reference, the specificity ranged from a low of 0.372 (ChatGPT-3.5) to a high of 0.769 (Microsoft Bing AI). Also, Microsoft Bing AI had the highest performance with an accuracy score of 0.788, with ChatGPT-3.5 having the lowest accuracy rate of 0.469. There was an overall improvement in performance for all the programs when the reference tool switched to a free DDI source, but still, ChatGPT-3.5 had the lowest specificity (0.392) and accuracy (0.525), and Microsoft Bing AI demonstrated the highest specificity (0.892) and accuracy (0.890). When assessing the consistency of accuracy across two different drug classes, ChatGPT-3.5 and ChatGPT-4 showed the highest ‎variability in accuracy. In addition, ChatGPT-3.5, ChatGPT-4, and Bard exhibited the highest ‎fluctuations in specificity when analyzing two medications belonging to the same drug class.

AIM: This study described characteristics of adulterated THM products in Malaysia and aimed to quantify THM products' safety signals of adverse reactions (ARs).

METHODS: THM products that were seized by Pharmacy Enforcement Division between 2008 and 2014 were extracted and analysed for 59,440 THM products. Of these, only 6452 THM products with complete information were included in the final analyses. Safety signalling tools were used to measure AR signals from AR reports obtained from the National Pharmaceutical Regulatory Agency Adverse Drug Reaction Database.

RESULTS: More than half of adulterated THM products originated from countries outside of Malaysia, with the majority were from Indonesia. The most common claimed indication of adulterated THM products was for pain and fever relief, while steroids were the most common adulterant. AR signals were generated for cough and cold products for respiratory and thoracic disorders, weight-loss products for cardiac disorders, and women's health products for reproductive and breast disorders.