Despite publishing and securing research grants being obligatory in research universities, the literature on the factors influencing academic productivity is relatively scarce. Thus, in this study, we aimed to determine the personal and behavioural-related factors that influence the culture of publishing and securing research grants among academicians with lower research-related performance. This cross-sectional study was conducted among 49 academic staff members of Universiti Kebangsaan Malaysia (UKM). A self-administered questionnaire consisting of personal, attitude and behavioural (barriers, perceived stress scale, work extrinsic and intrinsic motivation scale, psychological well-being scale, and basic needs satisfaction scale) questions were distributed during a workshop and online. Simple linear regression (SLR) analyses were performed for each variable, followed by multiple linear regression (MLR) to identify the associated factors of research output. After adjusting for covariates, having a doctoral degree (β=0.396, 95% CI=0.221-2.146, p<0.05) and integrated regulation (β=0.574, 95% CI=0.036-3.612, p<0.05) were found to be associated with research grant acquisition (R2=0.273). Moreover, increasing age (β=0.426, 95% CI=0.088-0.397, p<0.05), living alone (β=0.331, 95% CI=0.944-6.626, p<0.05), having a doctoral degree (β=0.248, 95% CI=0.174-6.747, p<0.05), environmental mastery (β=0.318, 95% CI=0.013-0.347, p<0.05), self-acceptance (β=0.284, 95% CI=0.010-0.242, p<0.05), satisfaction incompetence (β=0.273, 95% CI=0.001-0.200, p<0.05) and relatedness (β=0.280, 95% CI=0.001-0.116, p<0.05) were found to be the factors that influence the publications produced among participants (R2 =0.423). The findings of this study could be used by management to formulate effective strategies to increase the productivity of academics in their research-related performance.

Objective: This study aimed to evaluate the effects of combination of curcumin and piperine supplementation on Fasting Plasma Glucose (FPG), Homeostatic Model of Insulin Resistance (HOMA-IR), Body Mass Index (BMI) in patients with prediabetes and type 2 Diabetes Mellitus (T2DM). This review was done to identify potential herbal remedies that may help improve glycemic parameters, leading to better health outcomes in combination with current antidiabetic treatment. Methodology: This systematic review was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It was conducted in 2023 with sources and databases from MEDLINE, EBSCO-Host, ScienceDirect and ProQuest. This paper included randomized-controlled trials exploring the effects of the combination of curcumin and piperine on patients with prediabetes and T2DM. Systematic reviews, observational studies, case reports, case series, conference abstracts, book sections, commentaries/editorials, non-human studies and articles with unavailable full-text and written in non-English language, were excluded. The key terms for the literature search were “curcumin,” “piperine,” “prediabetes” and “Type 2 Diabetes Mellitus.” We use Cochrane Risk of Bias (RoB) 2 for quality assessment of the included studies and Review Manager (RevMan) 5.4 to do the meta-analysis. Results: A total of three studies were included in this systematic review. Two studies from Neta et al., and Cicero et al., showed no significant difference in HOMA-IR, BMI and FPG levels between the curcumin, piperine and placebo groups. One study from Panahi et al. demonstrated a significant difference in BMI levels between the curcumin and piperine and placebo groups (p <0.01). The meta-analysis showed that FPG levels, HOMA-IR and BMI improved among patients with diabetes given in curcumin and piperine with reported mean differences (MD) of = -7.61, 95% CI [-15.26, 0.03], p = 0.05, MD = -0.36, 95% CI [-0.77 to 0.05], p = 0.09, and MD = -0.41, 95% CI [-0.85 to 0.03], p = 0.07, respectively). Conclusions The supplementation of curcumin and piperine showed a numerical reduction in FPG, HOMA-IR and BMI, but were not statistically significant. Further research is needed as there is a paucity of studies included in the review.
Curcumin ; Prediabetic State ; Diabetes Mellitus, Type 2

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions. Methods: This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk. Results: Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02–1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42– 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07–2.69), travel history (aOR, 2.30; 95% CI, 1.13–4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72–0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16– 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97–0.99) were significant determinants. Conclusion: This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions. Methods: This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk. Results: Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02–1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42– 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07–2.69), travel history (aOR, 2.30; 95% CI, 1.13–4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72–0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16– 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97–0.99) were significant determinants. Conclusion: This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions. Methods: This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk. Results: Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02–1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42– 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07–2.69), travel history (aOR, 2.30; 95% CI, 1.13–4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72–0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16– 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97–0.99) were significant determinants. Conclusion: This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions. Methods: This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk. Results: Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02–1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42– 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07–2.69), travel history (aOR, 2.30; 95% CI, 1.13–4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72–0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16– 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97–0.99) were significant determinants. Conclusion: This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions. Methods: This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk. Results: Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02–1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42– 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07–2.69), travel history (aOR, 2.30; 95% CI, 1.13–4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72–0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16– 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97–0.99) were significant determinants. Conclusion: This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection. Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. Results: The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks. Conclusions The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Background: Acute coronary syndrome (ACS) is a major cardiovascular problem due to its high hospitalization and mortality rates. One of the risk factors for atherosclerosis that leads to ACS is insulin resistance (IR) which plays a role in the pathogenesis and development of cardiovascular events. This study aims to determine the relationship between IR and in-hospital outcomes in non-diabetic patients with ACS. Methodology: This was a cohort study conducted from January-June 2021. Insulin resistance was assessed using the Admission insulin resistance index (AIRI). This measurement was performed once during the patient's admission, and then the outcome was observed during hospitalization. The observed in-hospital outcomes were composite outcomes; namely, heart failure, arrhythmia, cardiogenic shock, and death. The statistical tests used were ANOVA, independent T and Chi-Square tests. Statistical test results were considered significant if p<0.05. Results: This study included 60 subjects (51 males and 9 females). Analysis revealed that AIRI was higher in patients with composite outcomes (mean 9.97 ± 4.08) than in patients without composite outcomes (mean 7.71 ± 4.06) (p<0.05); AIRI was higher in patients with heart failure (mean 10.72 ± 3.83) than in patients without heart failure (mean 7.25 ± 3.84) (p<0.001). Patients with IR had a higher rate of heart failure complications [OR 5.5 95% CI (1.56-19.38) (p=0.005)]. Conclusion There is an association between AIRI and composite outcomes. Patients with IR have 5.5 times the risk of developing heart failure.
insulin resistance ; acute coronary syndrome

Introduction: Previous studies on the degree of leg length discrepancy that causes limb biomechanical problems did not differentiate between adults and children. We conducted this study to determine the effects of simulated leg length discrepancy on vertical ground reaction force in children and adults to enable decision-making for intervention in patients with leg length discrepancy for different age groups or heights. Materials and methods: This cross-sectional study involved male volunteers of children 150cm and adults with 170cm in height. Vertical ground reaction force was measured using a gait analysis study. The first measurement was taken without any leg length discrepancy as a baseline. Subsequently, different amounts of leg length discrepancy were simulated on the left leg with shoe lifts of 2, 3, and 4cm. The measurements were repeated on each volunteer with similar shoe lifts on the right leg. Therefore, 14 volunteers provided simulations of 28 leg length discrepancies for each group. The first and second peaks of vertical ground reaction force were separately analysed. The vertical GRF of a simulated leg length discrepancy was compared with the baseline. Repeated measurement of analysis of variance (ANOVA) within each group was done. Results: In both groups, the second peak of vertical ground reaction force in the longer leg reduced gradually as the shoe lift increased sequentially from 2 to 3cm and then to 4cm. A discrepancy of 3cm and above was statistically significant to cause a reduction in the vertical GRF on the longer limb in both height groups. Conclusion: The degree of leg length discrepancy that caused significant changes in second peak ground reaction force in children with 150 and adults with 170cm height population was similar at 3cm. Therefore, the cut-off point for intervention for both groups are similar with additional consideration of future growth in children.