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The impact associated with mix body's genes about cancer base tissues and also medicine weight.
Background The most common pre-existing liver disease, the metabolic dysfunction-associated fatty liver disease (MAFLD) formerly named as non-alcoholic fatty liver disease (NAFLD), may have a negative impact on the severity of COVID-19. This meta-analysis aimed to evaluate if MAFLD or NAFLD are associated with a more severe disease course of COVID-19. Methods A systematic search was performed in five databases for studies comparing severity, the rate of intensive care unit (ICU) admission, and mortality of COVID-19 patients with and without MAFLD or NAFLD. In meta-analysis, pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results Altogether, we included nine studies in our quantitative and qualitative synthesis. MAFLD was associated with an increased risk of severe COVID-19 compared to the non-MAFLD group (28 vs. 13%, respectively; OR = 2.61, CI 1.75-3.91). Similarly, in the NAFLD vs. non-NAFLD comparison, NAFLD proved to be a risk factor as well (36 vs. 12%, respectively; OR = 5.22, CI 1.94-14.03). On the other hand, NAFLD was not associated with an increased risk of ICU admission (24 vs. 7%, respectively; OR = 2.29, CI 0.79-6.63). We were unable to perform meta-analysis to investigate the association of MAFLD with the rate of ICU admission and with mortality. Conclusion In conclusion, patients with MAFLD and NAFLD showed a more severe clinical picture in COVID-19. Our results support the importance of close monitoring of COVID-19 patients with MAFLD. Further research is needed to explore the cause of increased severity of COVID-19 in MAFLD.Objectives The main purpose of this retrospective cohort study was to provide an evaluation of Ankylosing spondylitis (AS) patients' fibromyalgia risk in different age and sex subgroups by analyzing large study samples. Methods Datasets from the National Taiwan Insurance Research Database (NHIRD) were retrieved in this retrospective cohort study. This study was approved by the Institutional Review Board of Chung Shan Medical University (IRB permit number CS15134). Within the Longitudinal Health Insurance Database (LHID), and the subset of NHIRD, we identified AS patients to explore the risk of further fibromyalgia. The exposure cohort included patients with newly-diagnosed AS (ICD-9-CM720.0) during 2000-2013. After 14 age-sex matching and 12 propensity score matching, and adjusting potential confounders, individuals without AS were identified as a comparison cohort. The adjusted hazard ratio of subsequent development of fibromyalgia in people with AS was evaluated. Further stratification analyses of different ages and genders were then undertaken to validate the results. Results In total, 17 088 individuals were included in the present study, including 5,696 patients with AS and 11,392 individuals without AS. Respective incidence rates (per 1,000 person-months) of fibromyalgia was 0.52 (95% CI, 0.46-0.59) in the AS cohort and 0.39 (95% CI, 0.35-0.44) in the non-AS cohort. Compared with the non-AS cohort, aHR of developing fibromyalgia was 1.32 (95% CI, 1.12-1.55) in people with AS. selleck inhibitor This association was consistent in both statistical models of 14 age-sex matching and 12 propensity score matching. Conclusion Patients with AS were associated with a higher risk of fibromyalgia, especially those over 65 years old. In managing patients with AS, clinicians should be aware of this association, which could impact diagnosis, disease activity evaluation, and treatment.Purpose Acute respiratory distress syndrome (ARDS) is common in critically ill patients and linked with serious consequences. A manual chart review for ARDS diagnosis could be laborious and time-consuming. We developed an automated search strategy to retrospectively identify ARDS patients using the Berlin definition to allow for timely and accurate ARDS detection. Methods The automated search strategy was created through sequential steps, with keywords applied to an institutional electronic medical records (EMRs) database. We included all adult patients admitted to the intensive care unit (ICU) at the Mayo Clinic (Rochester, MN) from January 1, 2009 to December 31, 2017. We selected 100 patients at random to be divided into two derivation cohorts and identified 50 patients at random for the validation cohort. The sensitivity and specificity of the automated search strategy were compared with a manual medical record review (gold standard) for data extraction of ARDS patients per Berlin definition. Results On the first derivation cohort, the automated search strategy achieved a sensitivity of 91.3%, specificity of 100%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 93.1%. On the second derivation cohort, it reached the sensitivity of 90.9%, specificity of 100%, PPV of 100%, and NPV of 93.3%. The strategy performance in the validation cohort had a sensitivity of 94.4%, specificity of 96.9%, PPV of 94.4%, and NPV of 96.9%. Conclusions This automated search strategy for ARDS with the Berlin definition is reliable and accurate, and can serve as an efficient alternative to time-consuming manual data review.The SARS-CoV-2 (SARS2) is the cause of the coronavirus disease 2019 (COVID-19) pandemic. One unique structural feature of the SARS2 spike protein is the presence of a furin-like cleavage site (FLC) which is associated with both viral pathogenesis and host tropism. Specifically, SARS2 spike protein binds to the host ACE-2 receptor which in-turn is cleaved by furin proteases at the FLC site, suggesting that SARS2 FLC structural variations may have an impact on viral infectivity. However, this has not yet been fully elucidated. This study designed and analyzed a COVID-19 genomic epidemiology network for December 2019 to July 2020, and subsequently generated and analyzed representative SARS2 spike protein models from significant node clusters within the network. To distinguish possible structural variations, a model quality assessment was performed before further protein model analyses and superimposition of the protein models, particularly in both the receptor-binding domain (RBD) and FLC. Mutant spike models were generated with the unique 681PRRA684 amino acid sequence found within the deleted FLC.
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