METHODS: The reporting of this systematic review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We carried out a literature search through three databases (Scopus, PubMed, Web of Science) and targeted original article published in English between 2012 and 2021. Quality appraisal of the eligible articles was conducted using the Mixed Methods Appraisal Tool. Findings were synthesized using content analysis.
RESULTS: A total of 86 studies were included. We found a variety of questionnaires assessing risk perception of NCDs, with many differences in their development, domains, items and validity. We also identified several personal, sociopsychological and structural factors associated with risk perception of NCDs.
LIMITATIONS: Most of the included studies were of cross-sectional design, and therefore the quality of evidence was considered low and exhibit a high risk of bias. The role of publication bias within this systematic review should be acknowledged as we did not include grey literature. Additionally, language bias must be considered as we only included English-language publications.
CONCLUSION: Further development and testing of available questionnaire is warranted to ensure their robustness and validity in measuring risk perception of NCDs. All the identified factors deserve further exploration in longitudinal and experimental studies.
RESULTS: Fifty-six H. pylori isolate from Bangladeshi patients were included in this cross-sectional study. Crystal violet assay was used to quantify biofilm amount, and the strains were classified into high- and low-biofilm formers As a result, strains were classified as 19.6% high- and 81.4% low-biofilm formers. These phenotypes were not related to specific clades in the phylogenetic analysis. The accessories genes associated with biofilm from whole-genome sequences were extracted and analysed, and SNPs among the previously reported biofilm-related genes were analysed. Biofilm formation was significantly associated with SNPs of alpA, alpB, cagE, cgt, csd4, csd5, futB, gluP, homD, and murF (P
CONCLUSION: The identification of a core microbiome furthers our understanding of mangrove microbial biodiversity, particularly in Southeast Asia where studies such as this are rare. The identification of significantly different microbial communities between sampling sites suggests environmental filtering is occurring, with hosts selecting for a microbial consortia most suitable for survival in their immediate environment. As climate change advances, many of these microbial communities are predicted to change, however, without knowing what is currently there, it is impossible to determine the magnitude of any deviations. This work provides an important baseline against which change in microbial community can be measured.
METHODS AND RESULTS: The 'COlchicine for the Prevention of Perioperative Atrial Fibrillation' (COP-AF) trial is an international, blinded, randomized trial that compares colchicine to placebo in patients aged at least 55 years and undergoing major noncardiac thoracic surgery with general anesthesia. Exclusion criteria include a history of AF and a contraindication to colchicine (eg, severe renal dysfunction). Oral colchicine at a dose of 0.5 mg or matching placebo is given within 4 hours before surgery. Thereafter, patients receive colchicine 0.5 mg or placebo twice daily for a total of 10 days. The 2 independent co-primary outcomes are clinically important perioperative AF (including atrial flutter) and MINS during 14 days of follow-up. The main safety outcomes are sepsis or infection and non-infectious diarrhea. We aim to enroll 3,200 patients from approximately 40 sites across 11 countries to have at least 80% power for the independent evaluation of the 2 co-primary outcomes. The COP-AF main results are expected in 2023.
CONCLUSIONS: COP-AF is a large randomized and blinded trial designed to determine whether colchicine reduces the risk of perioperative AF or MINS in patients who have major noncardiac thoracic surgery.
METHODS: Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.
RESULTS: Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).
CONCLUSIONS: Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.
METHODS: We followed the Joanna Briggs Institute guideline for the conduct of this scoping review. We searched MEDLINE, Embase, LILACS and study registers from inception to 14 March 2022. We included cross-sectional and cohort studies in populations representing a geographically-defined unit (urban or rural) in LMICs, and with data on CVH metrics i.e. all health or clinical factors (cholesterol, blood pressure, glycemia and body mass index) and at least one health behavior (smoking, diet or physical activity). We report findings following the PRISMA-Scr extension for scoping reviews.
RESULTS: We included 251 studies; 85% were cross-sectional. Most studies (70.9%) came from just ten countries. Only 6.8% included children younger than 12 years old. Only 34.7% reported seven metrics; 25.1%, six. Health behaviors were mostly self-reported; 45.0% of studies assessed diet, 58.6% physical activity, and 90.0% smoking status.
CONCLUSIONS: We identified a substantial and heterogeneous body of research presenting CVH metrics in LMICs. Few studies assessed all components of CVH, especially in children and in low-income settings. This review will facilitate the design of future studies to bridge the evidence gap. This scoping review protocol was previously registered on OSF: https://osf.io/sajnh.
OBJECTIVE: This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model.
METHODS: This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model.
RESULTS: This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level.
CONCLUSIONS: This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43712.