METHODS: Focus group discussions using the phenomenology approach was conducted involving 72 respondents in Selangor and Kelantan. Data were examined using content analysis.
RESULTS: Respondents perceived leptospirosis infection as severe due to its poor disease prognosis and complications. However, some rated it less severe when compared with other chronic diseases such as cancer and AIDS. Their perceptions were influenced by their knowledge about the disease, media portrayal and frequency of health campaigns by the government. All respondents believed they were not susceptible to the disease.
CONCLUSION: The low perceived susceptibility of leptospirosis infection is a matter of concern as it may contribute to respondents' lack of motivation towards preventing the disease. The study findings may provide the basis for health promotional activities designed to heighten public perceived threat towards leptospirosis infection and thereby improving preventive health behaviors for avoiding leptospirosis.
METHOD: This cross-sectional study involved eighty-three (n=83) adults attending a health screening program at Universiti Putra Malaysia (UPM). Demographic data, anthropometric measurements and blood samples for fasting blood glucose (FBG), fasting lipid profile (FSL), glycated haemoglobin (HbA1c) and hsCRP were taken. Respondents were grouped according to FRS and the Joint Interim Statement into 10-year CVD risk categories (low, intermediate and high) and MetS, respectively.
RESULTS: hsCRP was significantly increased in patients with high body mass index (BMI) (p=0.001), at-risk waist circumference (WC) (p=0.001) and MetS (p=0.009). Spearman's correlation coefficient showed a significant positive correlation between hsCRP level and total FRS score (r=0.26, p<0.05) and HDL-C score (r=0.22, p<0.05).
CONCLUSION: The significant difference of hsCRP levels across obesity levels and MetS with its modest correlation with FRS scores supported the adjunctive role of hsCRP in CVD risk prediction, most likely capturing the inflammatory pathological aspect and thus partly accounting for the residual CVD risk.
MATERIALS AND METHODS: This cross-sectional study included 150 subjects aged 30 years and above who attended a health screening in a Malaysian tertiary institution. Sociodemographics, clinical characteristics and laboratory parameters (lipids, glucose, and sdLDL) were obtained. Lipoprotein subfraction was analysed using the polyacrylamide gel electrophoresis method.
RESULTS: Malays and females made up the majority of subjects and the median age was 37 years. Normolipidaemic Pattern B was significantly higher in women (p=0.008). Significant independent predictors of Pattern B were gender (p=0.02), race (p=0.01), body mass index (BMI) [p=0.02] and lipid status (p=0.01). Triglyceride was the only independent predictor of sdLDL (p=0.001).
CONCLUSION: The prevalence of Pattern B of 33% in this study was comparatively high, of which 6.7% were normolipidaemic. Chinese males with dyslipidaemia and increased BMI independently predicted Pattern B. Differences in triglyceride levels alone among these ethnic groups do not fully explain the differences in the prevalence of Pattern B although it was the only lipid parameter to independently predict sdLDL. Individuals with atherogenic normolipidaemia are at greater risk for a CVD event as they are not included in the protective measures of primary CVD prevention.
CONTENT: Databases search of Scopus, ScienceDirect, PubMed, Directory of Open Access Journals (DOAJ), Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, MyJournal, Biblioteca Regional de Medicina (BIREME), BioMed Central (BMC) Public Health, Medline, Commonwealth Agricultural Bureaux (CAB), EMBASE (Excerpta Medica dataBASE) OVID, and Web of Science (WoS) was performed, which include the article from 1st January 2008 until 31st August 2018 using medical subject heading (MeSH). Articles initially identified were screened for relevance.
SUMMARY: Out of 744 papers screened, nine eligible studies did meet our inclusion criteria. Prison and housing environments were evaluated for TB transmission in living environment, while the other factor was urbanization. However, not all association for these factors were statistically significant, thus assumed to be conflicting or weak to end up with a strong conclusion.
OUTLOOK: Unsustainable indoor environment in high congregate setting and overcrowding remained as a challenge for TB infection in Malaysia. Risk factors for transmission of TB, specifically in high risk areas, should focus on the implementation of specialized program. Further research on health care environment, weather variability, and air pollution are urgently needed to improve the management of TB transmission.
Methods: The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.
Results: The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R2) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO2, SO2, PM10, rainfall, temperature, and atmospheric pressure, with the highest adjusted R2 value of 0.47, errors below 6, and accuracies above 96%.
Conclusions: It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.
OBJECTIVES: The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated.
METHODS: Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs.
RESULTS: Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs.
CONCLUSIONS: The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.