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Introduction: Bacterial vaginosis (BV) is one of the most common genital tract infections among women of reproductive age. The existence of BV among pregnant women has momentously attracted the attention of both clinicians and the scientific community due to its potential link with adverse clinical outcomes in pregnancy. Methods: To evaluate the prevalence, risk factors, and adverse outcomes of bacterial vaginosis among pregnant women, a comprehensive systematic review was conducted based on the preferred reporting items for systematic review and meta-analyses (PRISMA) criteria. PubMed, ScienceDirect, ClinicalTrials.gov and Cochrane database searches were conducted independently by two authors until May 13th, 2023. Results: The search strategies yielded a total of 2237 records; among them, 12 studies met the inclusion criteria and were included in the qualitative synthesis. Majority of the included studies demonstrated a high prevalence of BV among African women. The risk of developing BV during pregnancy was highest among women with multiple sexual partners. Additionally, factors including age, socioeconomic status, unhygienic practices, ethnicity, 2nd trimester, spontaneous abortion, vaginal douching, symptoms, and history of sexually transmitted infections (STIs) were also associated with a higher prevalence of BV. Overall, 7 studies reported adverse outcomes during pregnancy which was directly associated with BV. Based on the review, it was found that PROM, PTB, and LBW were the most frequently reported adverse outcomes in pregnant women with BV. Conclusion: In summary, the high prevalence of bacterial vaginosis necessitates a global surveillance approach to delineate the health risks imposed on both mother and child, and promote cost-effective strategic measures to alleviate the undesired consequences of BV during pregnancy. [ABSTRACT FROM AUTHOR]

Background/Objectives: The emergence and dissemination of carbapenem-resistant organisms, particularly Acinetobacter baumannii and Klebsiella pneumoniae, pose a significant threat to healthcare systems worldwide. This retrospective study aims to characterise carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Klebsiella pneumoniae (CRKP) strains in a teaching hospital and to determine the risk factors associated with patients' in-hospital mortality. Methods: A total of 90 CRAB and 63 CRKP were included in this study. Carbapenemase genes and MLST types of CRAB and CRKP were determined using specific primers. Risk factors associated with in-hospital mortality were analysed with collected data. Results: All the CRAB strains consisted of OXA carbapenemase genes, with 98% of the strains co-harbouring blaOXA-23-like and blaOXA-51-like carbapenemase genes. Conversely, blaNDM is the predominant carbapenemase gene in CRKP, followed by blaOXA-48-like carbapenemase genes. ST2 and ST20 are the dominant MLST types in CRAB and CRKP, respectively. In CRAB, multivariate analysis identified age, ethnicity, the presence of a mechanical ventilator, and patients who experienced previous exposure to clindamycin in the last 90 days as associated with an increased risk of in-hospital mortality. In contrast, older age, male, ICU admission, and the presence of an indwelling urinary catheter were significantly associated with an increased risk of mortality for patients with CRKP. Conclusions: Both CRAB and CRKP lead to high rates of mortality. The MLST profile showed that the genomic patterns of CRKP were highly diverse, whereas CRAB strains had low genetic diversity. To tackle these challenging pathogens, robust surveillance and an in-depth understanding of molecular epidemiology and genomics studies are needed to tailor infection control strategies and individualise treatment approaches. [ABSTRACT FROM AUTHOR]

The growing prevalence of commensal antibiotic resistant Escherichia coli poses a significant concern for the global spread of antibiotic resistance. Stool samples (n = 35) from a seminomadic indigenous community in Malaysia, the Jehai, were screened for multidrug‐resistant bacteria, specifically the extended‐spectrum β‐lactamase (ESBL) producers. Subsequently, whole‐genome sequencing was used to provide genomic insights into eight ESBL‐producing E. coli that colonised eight individuals. The ESBL E. coli isolates carry resistance genes from various antibiotic classes such as the β‐lactams (blaTEM, blaCTX-M−15 and blaCTX-M−55), quinolones (gyrA, qnrS and qnrS1) and aminoglycosides (aph(3′)-Ia, aph(6)-Id and aac(3)-IId). Three concerning convergence of ESBL, colistin and metal resistance determinants, with three plasmids from H‐type lineage harbouring blaCTX-M and mcr-1.1 genes were identified. Using the Oxford Nanopore Technology (ONT) Native Barcoding Kit (SQK‐NBD114.24) in conjunction with the R10.4.1 flow cell, which achieved an average read accuracy (Q > 10) of 99.84%, we further characterised the mcr-1.1‐bearing plasmids, ranging in size from 25 to 28 kb, from three strains of E. coli. This report represents the first whole genome analysis of multidrug‐resistant bacteria, specifically those resistant to colistin, found within the indigenous population in Malaysia. It strongly indicates that the pertinent issue of colistin resistance in the country is far more significant than previously estimated. [ABSTRACT FROM AUTHOR]

This project employs artificial intelligence, including machine learning and deep learning, to assess COVID-19 readmission risk in Malaysia. It offers tools to mitigate healthcare resource strain and enhance patient outcomes. This study outlines a methodology for classifying COVID-19 readmissions. It starts with dataset description and pre-processing, while the data balancing was computed through Random Oversampling, Borderline SMOTE, and Adaptive Synthetic Sampling. Nine machine learning and ten deep learning techniques are applied, with five-fold cross-validation for evaluation. Optuna is used for hyperparameter selection, while the consistency in training hyperparameters is maintained. Evaluation metrics encompass accuracy, AUC, and training/inference times. Results were based on stratified five-fold cross-validation and different data-balancing methods. Notably, CatBoost consistently excelled in accuracy and AUC across all tables. Using ROS, CatBoost achieved the highest accuracy (0.9882 ± 0.0020) with an AUC of 1.0000 ± 0.0000. CatBoost maintained its superiority in BSMOTE and ADASYN as well. Deep learning approaches performed well, with SAINT leading in ROS and TabNet leading in BSMOTE and ADASYN. Decision Tree ensembles like Random Forest and XGBoost consistently showed strong performance. [ABSTRACT FROM AUTHOR]