Articles: disease.
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There is still a scarcity of data on hair loss caused by coronavirus disease 2019 (COVID-19) infection. This study aims to determine the characteristics of hair loss in Thai individuals after COVID-19 infection and to identify associated factors. From March to June 2022, a retrospective review of medical records and telephone interviews was conducted to determine the details of hair loss, the severity of infection, and the associated treatments of patients with an abrupt onset of hair loss after the diagnosis of COVID-19 infection at Siriraj Hospital in Bangkok, Thailand. ⋯ Patients with a history of androgenetic alopecia tended to have a lower hair shedding scale (adjusted odd ratio 0.03, 95% CI 0.01-0.38). This study reviewed the characteristics of hair loss after COVID-19 infection during Omicron outbreaks in Thailand. The COVID-19-associated telogen effluvium, which is the primary cause in our patients, manifested with earlier onset at approximately 30 days.
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Working with 2019 coronavirus disease (COVID-19) patients is currently considered one of the main fears and challenges that face healthcare workers (HCWs), especially nurses. This challenge can jeopardize the quality of health care services for those patients and cause a serious mental burden to HCWs. To understand and estimate the risk of COVID-19 infection among HCWs who directly serve COVID-19 patients. ⋯ The relative risk of getting COVID-19 infection among HCWs who worked in the COVID-19 wards was reduced to about half in comparison to other HCWs who worked in the non-COVID wards (RR = 0.469). HCWs should not fear working with COVID-19 patients. Considering appropriate personal protective measures and infection control standards, the risk of infection transmission from the community is higher than that of COVID-19 patients, if any.
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This study explores the role of combining the controlling nutritional status (CONUT) score and the carcinoembryonic antigen (CEA) level on predicting tumor stage and prognosis in gastric cancer (GC) patients. A total of 682 GC patients were included in this retrospective study. CONUT scores and CEA levels were combined to establish a new scoring system: CONUT-CEA score. cutoff values for distinguishing patients between stage IV and non-stage IV were established by receiver operating characteristic curves. cutoff values for predicting prognosis were determined by maximum χ2 method. ⋯ Among non-stage IV patients, CONUT and CEA cutoff values of 2.0 and 9.50 ng/mL predicted overall survival (OS), respectively. The Cox proportional risk model revealed that high CONUT-CEA score was notable related to decreased OS (2 vs 0: hazard ratios (HR) = 2.358, 95% confidence intervals (CI) = 1.412-3.940, P = .001) and decreased disease-free survival (2 vs 0: HR = 1.980, 95% CI = 1.072-3.656, P = .003). The CONUT-CEA score may be a good biomarker for predicting tumor stage and prognosis in GC patients.
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Myocardial infarction (MI) is a major cause of death and disability worldwide, but current treatments are limited by their invasiveness, side effects, and lack of efficacy. Novel drug targets for MI prevention are urgently needed. In this study, we used Mendelian randomization to identify potential therapeutic targets for MI using plasma protein quantitative trait loci as exposure variables and MI as the outcome variable. ⋯ We also found 4 other proteins, IL1RN, FN1, NT5C, and SEMA3C, that may have potential as drug targets but require further confirmation. Our study demonstrates the utility of Mendelian randomization and protein quantitative trait loci in discovering novel drug targets for complex diseases such as MI. It provides insights into the underlying mechanisms of MI pathology and treatment.
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The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. ⋯ Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.