METHODS: Sprague-Dawley rats were divided into four groups: Sham, AMI, AMI treated with PBS (AMI-PBS), and AMI treated with pirfenidone (AMI-PFD) (n=12 each). AMI was induced via coronary artery ligation. The AMI-PFD and AMI-PBS groups received pirfenidone and PBS for 14 days, respectively. Cardiac function, fibrosis, serum cytokines, collagen and elastin content, and their ratios were assessed. Cardiac fibroblasts (CFs) from neonatal rats were categorized into control, hypoxia-induced (LO), LO+PBS, and LO+PFD groups. ELISA measured inflammatory factors, and RT-PCR analyzed collagen and elastin gene expression.
RESULTS: The AMI-PFD group showed improved cardiac function and reduced serum interleukin-1β (IL-1β), IL-6, and transforming growth factor-β (TGF-β). Type I and III collagen decreased by 22.6 % (P=0.0441) and 34.4 % (P=0.0427), respectively, while elastin content increased by 79.4 % (P=0.0126). E/COLI and E/COLIII ratios rose by 81.1 % (P=0.0026) and 88.1 % (P=0.0006). CFs in the LO+PFD group exhibited decreased IL-1β, IL-6, TGF-β, type I and III collagen, with increased elastin mRNA, enhancing the elastin/collagen ratio.
CONCLUSION: Pirfenidone enhances cardiac function by augmenting the early elastin/collagen ratio post-AMI.
DESIGN: A secondary data analysis of the 2023 Registry of Occupational Disease Screening (RODS) was performed. The RODS survey tool, which included the Nordic Occupational Skin Questionnaire, a symptoms checklist and items on work-relatedness, was used to screen for OSDs. Logistic regression analyses were performed to identify associated factors.
SETTING AND PARTICIPANTS: Restaurant workers (n=300) registered in RODS from February 2023 to April 2023, aged 18 years and above and working in restaurants across Selangor, Melaka and Pahang for more than 1 year, were included in the study, whereas workers who had pre-existing skin diseases were excluded.
RESULTS: The prevalence of suspected OSDs among study participants was 12.3%. Higher odds of suspected OSDs among study participants were observed among those exposed to wet work (adjusted OR (AOR) 22.74, 95% CI 9.63 to 53.68) and moderate to high job stress levels (AOR 4.33, 95% CI 1.80 to 10.43).
CONCLUSIONS: These findings suggest that OSDs are a significant occupational health problem among restaurant workers. Interventions targeting job content and wet work may be vital in reducing OSDs among this group of workers.
METHODS: This study used secondary data from a population-based health survey in Malaysia, namely the National Health Morbidity Survey (NHMS) 2018: Elderly Health. The analysis included 926 community-dwelling geriatric population aged 60 and above with low social support. The primary data collection was from August to October 2018, using face-to-face interviews. This paper reported the analysis of depression as the dependent variable, while various biological, psychological and social factors, guided by established biopsychosocial models, were the independent variables. Multiple logistic regression was applied to identify the factors. Analysis was performed using the complex sampling module in the IBM SPSS version 29.
RESULTS: The weighted prevalence of depression among the community-dwelling geriatric population aged 60 and above with low social support was 22.5% (95% CI: 17.3-28.7). This was significantly higher than depression among the general geriatric Malaysian population. The factors associated with depression were being single, as compared to those married (aOR 2.010, 95% CI: 1.063-3.803, p: 0.031), having dementia, as opposed to the absence of the disease (aOR 3.717, 95% CI: 1.544-8.888, p: 0.003), and having a visual disability, as compared to regular visions (aOR 3.462, 95% CI: 1.504-7.972, p: 0.004). The analysis also revealed that a one-unit increase in control in life and self-realisation scores were associated with a 32.6% (aOR: 0.674, 95% CI: 0.599-0.759, p
METHODS: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted by searching four electronic databases (PubMed, CENTRAL, Scopus, and Science Direct) through December 2023. The risk of bias was assessed using the PEDro tool, and the study outcomes were expressed as standard mean difference at 95% CI.
RESULTS: Out of 1838 yielded results, eight RCTs involving 623 participants with a mean age of 56.96 ± 4.89 met the prespecified eligibility criteria. The pooled results showed a statistically significant and beneficial effect of MBIs on CAD patients' mental health status in regards to anxiety (SMD = -0.83; 95% CI [-1.19, -0.46], p
METHODS: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies.
RESULTS: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively.
CONCLUSIONS: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.