OBJECTIVE: Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (1 H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by 1 H-NMR-based metabolomics.
RESULTS: Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC50 value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC50 value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities.
CONCLUSION: The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.
PURPOSE: To examine relationship between ulam consumption and the working memory and cognitive flexibility among aging adults from low-income households who are more susceptible to cognitive decline.
STUDY TYPE: Cross-sectional.
POPULATION/SUBJECTS: Thirty-two aging adults (45-75 years old).
FIELD STRENGTH/SEQUENCE: Task-based fMRI, 3.0T, T1 -weighted anatomical images, T2 *-weighted imaging data.
ASSESSMENT: The dietary and ulam consumption were assessed using the respective validated Dietary History and semiquantitative Food Frequency questionnaires. Working memory and cognitive flexibility were evaluated by using neuropsychological batteries (ie, mini-mental state examination [MMSE], Digit Span, and Rey auditory verbal learning test [RAVLT]) and task-based fMRI (N-back and Stroop Color Word Test [SCWT]). Brodmann's areas 9 and 46 were the regions of interest (ROIs) of DLPFC activation.
STATISTICAL TESTS: Multiple linear regression used to understand the relationship between ulam consumption and the working memory and cognitive flexibility, while analysis of covariance (ANCOVA) was used to compare the difference of working memory and cognitive flexibility among four percentiles of ulam consumption, after age, gender, and education years adjustments. Significance was decided by two-sided, P
OBJECTIVE: This proposed study aims to evaluate the effectiveness of the Stroke Riskometer™ app in improving stroke awareness and stroke risk probability amongst the adult population in Malaysia.
METHODS: A non-blinded, parallel-group cluster-randomized controlled trial with a 1:1 allocation ratio will be implemented in Kelantan, Malaysia. Two groups with a sample size of 66 in each group will be recruited. The intervention group will be equipped with the Stroke Riskometer™ app and informational leaflets, while the control group will be provided with standard management, including information leaflets only. The Stroke Riskometer™ app was developed according to the self-management model of chronic diseases based on self-regulation and social cognitive theories. Data collection will be conducted at baseline and on the third week, sixth week, and sixth month follow-up via telephone interview or online questionnaire survey. The primary outcome measure is stroke risk awareness, including the domains of knowledge, perception, and intention to change. The secondary outcome measure is stroke risk probability within 5 and 10 years adjusted to each participant's socio-demographic and/or socio-economic status. An intention-to-treat approach will be used to evaluate these measures. Pearson's χ2 or independent t test will be used to examine differences between the intervention and control groups. The generalized estimating equation and the linear mixed-effects model will be employed to test the overall effectiveness of the intervention.
CONCLUSION: This study will evaluate the effect of Stroke Riskometer™ app on stroke awareness and stroke probability and briefly evaluate participant engagement to a pre-specified trial protocol. The findings from this will inform physicians and public health professionals of the benefit of mobile technology intervention and encourage more active mobile phone-based disease prevention apps.
TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT04529681.