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  1. Effect of Vitamin C supplements on respiratory tract infections: A systematic review and meta-analysis
    Keya TA, Leela A, Fernandez K, Habib N, Rashid M
    Curr Rev Clin Exp Pharmacol, 2021 Dec 30.
    PMID: 34967304 DOI: 10.2174/2772432817666211230100723
    BACKGROUND: Respiratory tract infections are a primary cause of illness and mortality over the world.

    OBJECTIVE: This study was aimed to investigate the effectiveness of vitamin C supplementation in preventing and treating respiratory tract infections.

    METHODS: We used the Cochrane, PubMed, and MEDLINE Ovid databases to conduct our search. The inclusion criteria were placebo-controlled trials. Random effects meta-analyses were performed to measure the pooled effects of vitamin C supplementation on the incidence, severity, and duration of respiratory illness.

    RESULTS: We found ten studies that met our inclusion criteria out of a total of 2758. The pooled risk ratio (RR) of developing respiratory illness when taking vitamin C regularly across the study period was 0⸳94 (with a 95% confidence interval of 0⸳87 to 1⸳01) which found that supplementing with vitamin C lowers the occurrence of illness. This effect, however, was statistically insignificant (P= 0⸳09). This study showed that vitamin C supplementation had no consistent effect on the severity of respiratory illness (SMD 0⸳14, 95% CI -0⸳02 to 0⸳30: I2 = 22%, P=0⸳09). However, our study revealed that vitamin C group had a considerably shorter duration of respiratory infection (SMD -0⸳36, 95% CI -0⸳62 to -0⸳09, P = 0⸳01).

    CONCLUSION: Benefits of normal vitamin C supplementation for reducing the duration of respiratory tract illness were supported by our meta-analysis findings. Since few trials have examined the effects of therapeutic supplementation, further research is needed in this area.

  2. Enhancing precision flood mapping: Pahang's vulnerability unveiled
    Keya TA, Balakrishnan SS, Solayappan M, Dheena Dhayalan SS, Subramaniam S, An LJ, et al.
    PLoS One, 2024;19(11):e0310435.
    PMID: 39509412 DOI: 10.1371/journal.pone.0310435
    Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of 'very high' and 'high' class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.
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