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Background Alopecia areata (AA) is a chronic autoimmune disease that leads to a high psychiatric, economic and systemic disease burden. A comprehensive understanding of AA epidemiology is essential for evaluating healthcare source utilization; however, a systematic approach to summarizing epidemiological data on AA is lacking. Objectives To investigate systematically the global, regional and national incidence and prevalence of AA. Methods A structured search was conducted using the databases MEDLINE, Embase, Cochrane Library, Web of Science, SciELO and Korean Journal Database from their date of inception to 4 October 2023. Studies that reported the prevalence or incidence of AA were included. We used a Bayesian hierarchical linear mixed model to analyse prevalence estimates. The primary outcomes of our study were the global, regional and national prevalence of physician-diagnosed AA for the overall population, for adults and for children. The incidence data were summarized descriptively. Results In total, 88 studies from 28 countries were included in the analysis. The reported incidence of AA tended to be higher in adults aged 19–50 years, and this trend was consistent with its estimated prevalence. The reported prevalence in overall populations tended to be higher in men vs. women. The estimated lifetime prevalence rate of AA was 0.10% [95% credible interval (CrI) 0.03–0.39] in the general population worldwide, 0.12% (95% CrI 0.02–0.52) in adults and 0.03% (95% CrI 0.01–0.12) in children. The estimated prevalence of AA was highest in the Asian region and lowest in the African region. Conclusions In this study, 48% of the Global Burden of Disease regions had insufficient data on the prevalence or incidence of AA. Further studies are needed to provide epidemiological information on middle- and low-income countries. Our study may serve as a crucial reference in terms of healthcare policy decisions. [ABSTRACT FROM AUTHOR]

Carbon monoxide poisoning is associated with severe damage to various organs. In this study, we aimed to determine if previous carbon monoxide poisoning was associated with an increased risk of lung diseases. The study population was derived from the National Health Insurance Service database of Korea between 1 January 2002 and 31 December 2021. Adults with carbon monoxide poisoning, with at least one visit to medical facilities between 2002 and 2021, were included. For comparison, an equal number of matched controls with the same index date were selected from the database. A total of 28,618 patients with carbon monoxide poisoning and 28,618 matched controls were included in this study. Approximately 42.8 per cent of the patient and control groups were female, with a mean age of 51.3 years. In patients with carbon monoxide poisoning, there was a significant increase in the risk of lung cancer (adjusted hazard ratio, 1.84; 95 per cent confidence interval, 1.42–2.39; P < 0.001), chronic obstructive pulmonary disease (adjusted hazard ratio, 1.60; 95 per cent confidence interval, 1.36–1.89; P < 0.001), pulmonary tuberculosis (adjusted hazard ratio, 1.46; 95 per cent confidence interval, 1.13–1.88; P = 0.003), and non-tuberculous mycobacterial infection (adjusted hazard ratio, 1.54; 95 per cent confidence interval, 1.01–2.36; P = 0.047). In this retrospective cohort study, previous carbon monoxide poisoning was associated with an increased risk of lung cancer, chronic obstructive pulmonary disease, pulmonary tuberculosis, and non-tuberculous mycobacterial infection. Further studies are needed to confirm such an association in other populations and the risk of lung diseases due to the toxic effect of carbon monoxide from different sources. Previous carbon monoxide poisoning was associated with an increased risk of lung diseases, but the relative importance of the causes and sources of exposure was not known. The long-term management of survivors of acute carbon monoxide poisoning should include monitoring for lung cancer, chronic obstructive pulmonary disease, pulmonary tuberculosis, and non-tuberculous mycobacterial infection. [ABSTRACT FROM AUTHOR]

Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical, and non-invasive method for screening in primary care. However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m2 vs. ≥132 g/m2, <109 g/m2 vs. ≥109 g/m2). Six types of input were used for the classification tasks. We attempted to determine whether electrocardiography had predictive power for left ventricular hypertrophy diagnosis. For the entire dataset, the model achieved an area under the receiver operating characteristic (AUROC) curve of 0.836 (95% CI, 0.833–838) with a sensitivity of 78.37% (95% CI, 76.79–79.95). For the male dataset, the AUROC was 0.826 (95% CI, 0.822–830) with a sensitivity of 76.73% (95% CI, 75.14–78.33). For the female dataset, the AUROC was 0.772 (95% CI, 0.769–775) with a sensitivity of 72.90% (95% CI, 70.33–75.46). Our model confirmed that left ventricular hypertrophy can be classified to some extent using electrocardiography, demographics, and electrocardiography features. In particular, a learning environment that considered gender differences was constructed. Consequently, the difference in diagnostic power between men and women was confirmed. Our model will help patients with suspected left ventricular hypertrophy to undergo screening tests at a low cost. In addition, our research and attempts will show the expected effect that gender-consideration approaches can help with various currently proposed diagnostic methods. [ABSTRACT FROM AUTHOR]

Byline: Solam. Lee, Hanil. Lee, Chung. Lee, Won-Soo. Lee Sir, Androgenetic alopecia (AGA) and alopecia areata (AA) are the commonest causes of hair loss. Despite the availability of several therapeutic [...]