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A Scoping Review of the Current Knowledge of the Social Determinants of Health and Infectious Diseases (Specifically COVID-19, Tuberculosis, and H1N1 Influenza) in Canadian Arctic Indigenous Communities.
Int J Environ Res Public Health
December 2024
Indigenous and Global Health Research Group, Department of Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, 1-126 8602 112 Street, Edmonton, AB T6G 2E1, Canada.
Social determinants of health (SDHs) and the impact of colonization can make Canadian Arctic Indigenous communities susceptible to infectious diseases, including the coronavirus disease 2019 (COVID-19). This scoping review followed the PRISMA guidelines for scoping reviews and studied what is known about selected pandemics (COVID-19, tuberculosis, and H1N1 influenza) and SDHs (healthcare accessibility, food insecurity, mental health, cultural continuity, housing, community infrastructure, and socioeconomic status (SES)) for Canadian Arctic Indigenous communities. Original studies published in English and French up to October 2024 were located in databases (PubMed, Medline, and CINAHL), , and through reference tracking.
View Article and Find Full Text PDFPrediction of mortality and prioritisation to tertiary care using the 'OUR-ARCad' risk score gleaned from the second wave of COVID-19 pandemic-A retrospective cohort study from South India.
PLoS One
January 2025
NIE-Indian Council of Medical Research-National Institute of Epidemiology, Chennai, India.
Background: Judicious utilisation of tertiary care facilities through appropriate risk stratification assumes priority, in a raging pandemic, of the nature of delta variant-predominated second wave of COVID-19 pandemic in India. Prioritisation of tertiary care, through a scientifically validated risk score, would maximise recovery without compromising individual safety, but importantly without straining the health system.
Methods: De-identified data of COVID-19 confirmed patients admitted to a tertiary care hospital in South India, between April 1, 2021 and July 31, 2021, corresponding to the peak of COVID-19 second wave, were analysed after segregating into 'survivors' or 'non-survivors' to evaluate the risk factors for COVID-19 mortality at admission and formulate a risk score with easily obtainable but clinically relevant parameters for accurate patient triaging.
Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method.
J Glob Health
January 2025
Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic may inadvertently influence the epidemiology of tuberculosis (TB). (TB). However, few studies have explored how NPIs impact the long-term epidemiological trends of TB.
View Article and Find Full Text PDFBibliometric analysis of metformin as an immunomodulator (2013-2024).
Front Immunol
January 2025
School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China.
Background: Metformin, the frontline treatment for diabetes, has considerable potential as an immunomodulator; however, detailed bibliometric analyses on this subject are limited.
Methods: This study extracted 640 relevant articles from the Web of Science (WOS) Core Collection and conducted visual analyses using Microsoft Excel, VOSviewer, and CiteSpace.
Results: The findings showed that research on the immunomodulatory function of metformin has grown steadily since 2017, with China and the United States being the leading contributors.
Revolutionizing diagnosis of pulmonary based on CT: a systematic review of imaging analysis through deep learning.
Front Microbiol
January 2025
Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, China.
Introduction: The mortality rate associated with (MTB) has seen a significant rise in regions heavily affected by the disease over the past few decades. The traditional methods for diagnosing and differentiating tuberculosis (TB) remain thorny issues, particularly in areas with a high TB epidemic and inadequate resources. Processing numerous images can be time-consuming and tedious.
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