Prevalence and Associated Factors of Depression and Anxiety Among Healthcare Workers During the Coronavirus Disease 2019 Pandemic: A Nationwide Study in Korea.

BACKGROUND: A healthcare system's collapse due to a pandemic, such as the coronavirus disease 2019 (COVID-19), can expose healthcare workers (HCWs) to various mental health problems. This study aimed to investigate the impact of the COVID-19 pandemic on the depression and anxiety of HCWs. METHODS: A nationwide questionnaire-based survey was conducted on HCWs who worked in healthcare facilities and public health centers in Korea in December 2020. Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to measure depression and anxiety. To investigate factors associated with depression and anxiety, stepwise multiple logistic regression analysis was performed. RESULTS: A total of 1,425 participating HCWs were included. The mean depression score (PHQ-9) of HCWs before and after COVID-19 increased from 2.37 to 5.39, and the mean anxiety score (GAD-7) increased from 1.41 to 3.41. The proportion of HCWs with moderate to severe depression (PHQ-9 ≥ 10) increased from 3.8% before COVID-19 to 19.5% after COVID-19, whereas that of HCWs with moderate to severe anxiety (GAD-7 ≥ 10) increased from 2.0% to 10.1%. In our study, insomnia, chronic fatigue symptoms and physical symptoms after COVID-19, anxiety score (GAD-7) after COVID-19, living alone, and exhaustion were positively correlated with depression. Furthermore, post-traumatic stress symptoms, stress score (Global Assessment of Recent Stress), depression score (PHQ-9) after COVID-19, and exhaustion were positively correlated with anxiety. CONCLUSION: In Korea, during the COVID-19 pandemic, HCWs commonly suffered from mental health problems, including depression and anxiety. Regularly checking the physical and mental health problems of HCWs during the COVID-19 pandemic is crucial, and social support and strategy are needed to reduce the heavy workload and psychological distress of HCWs.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

Prediction Model for Chronological Weight Loss After Bariatric Surgery in Korean Patients.

Purpose: This study aimed to develop a predictive model for monitoring chronological weight loss during the early postoperative period following bariatric surgery in Korean patients with morbid obesity. Materials and Methods: The baseline characteristics and postoperative weight loss outcomes were collected for up to 24 months after surgery in patients who underwent sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB). The factors influencing weight loss outcomes were analyzed, and longitudinal percentile charts were plotted using quantile regression models adjusted for the identified independent factors. Results: The analysis included 491 and 274 patients who underwent SG and RYGB, respectively, of whom 225 (29.4%) were men. A positive association was found between the maximum percentage of total weight loss (%TWL) and female sex, body mass index (BMI) ≥40, and age <40 years. Among patients who reached nadir BMI or had at least 12 months of follow-up data (n=304), 7.6% exhibited inadequate weight loss (TWL <20%). The predictors of insufficient weight loss were older age (>40 years), male sex, and psychological problems. Centile charts were generated for the entire cohort, incorporating age, sex, and the type of procedure as covariates. Conclusion: The percentile charts proposed in the present study can assist surgeons and healthcare providers in gauging patients' progress toward their weight loss goals and determining the timing of adjunctive intervention in poor responders during early postoperative follow-up.

mTOR Inhibition Prolongs Survival and Has Beneficial Effects on Heart Function After Onset of Lamin A/C Gene Mutation Cardiomyopathy in Mice.

BACKGROUND: Mutations in LMNA encoding nuclear envelope proteins lamin A/C cause dilated cardiomyopathy. Activation of the AKT/mTOR (RAC-α serine/threonine-protein kinase/mammalian target of rapamycin) pathway is implicated as a potential pathophysiologic mechanism. The aim of this study was to assess whether pharmacological inhibition of mTOR signaling has beneficial effects on heart function and prolongs survival in a mouse model of the disease, after onset of heart failure. METHODS: We treated male LmnaH222P/H222P mice, after the onset of heart failure, with placebo or either of 2 orally bioavailable mTOR inhibitors: everolimus or NV-20494, a rapamycin analog highly selective against mTORC1. We examined left ventricular remodeling, and the cell biological, biochemical, and histopathologic features of cardiomyopathy, potential drug toxicity, and survival. RESULTS: Everolimus treatment (n=17) significantly reduced left ventricular dilatation and increased contractility on echocardiography, with a 7% (P=0.018) reduction in left ventricular end-diastolic diameter and a 39% (P=0.0159) increase fractional shortening compared with placebo (n=17) after 6 weeks of treatment. NV-20494 treatment (n=15) yielded similar but more modest and nonsignificant changes. Neither drug prevented the development of cardiac fibrosis. Drug treatment reactivated suppressed autophagy and inhibited mTORC1 signaling in the heart, although everolimus was more potent. With regards to drug toxicity, everolimus alone led to a modest degree of glucose intolerance during glucose challenge. Everolimus (n=20) and NV-20494 (n=20) significantly prolonged median survival in LmnaH222P/H222P mice, by 9% (P=0.0348) and 11% (P=0.0206), respectively, compared with placebo (n=20). CONCLUSIONS: These results suggest that mTOR inhibitors may be beneficial in patients with cardiomyopathy caused by LMNA mutations and that further study is warranted.

Does preoperative bone marrow edema affect clinical outcomes after medial open-wedge high tibial osteotomy?

PURPOSE: The aim of this study is to evaluate the results of high tibial osteotomy (HTO) in patients with bone marrow edema (BME) and assess the effect of factors on the subsequent results. METHODS: A total of 138 patients who underwent medial open-wedge HTO using locking plate were included in this study. BME was observed in 108 patients using preoperative magnetic resonance imaging. Clinical results were evaluated before HTO and postoperatively at 12 months. Moreover, we evaluated the factors affecting the clinical results. RESULTS: The clinical scores were all significantly improved regardless of the presence of BME. There were no differences in improvement of clinical scores between patients with BME and without BME. Patients with BME showed higher postoperative Western Ontario and McMaster Universities Arthritis Index (WOMAC) stiffness scores than patients without BME. Furthermore, patients with BME on both the femur and tibia showed lower Knee Society function scores than patients with BME on either the femur or the tibia. Patients with large BME lesions exhibited a lower Hospital for Special Surgery score and WOMAC pain scores, postoperatively. In patients with BME, patients with undercorrection showed significantly lower improvement in WOMAC pain scores compared with patients with acceptable correction. CONCLUSIONS: The clinical improvement after HTO in patients with varus and medial osteoarthritis was not different regardless of the presence or absence of BME. However, accurate alignment should be considered essential for achieving better clinical outcomes in patients with preoperative BME.

Dynamic regulation of hepatic lipid metabolism by torsinA and its activators.

Depletion of torsinA from hepatocytes leads to reduced liver triglyceride secretion and marked hepatic steatosis. TorsinA is an atypical ATPase that lacks intrinsic activity unless it is bound to its activator, lamina-associated polypeptide 1 (LAP1) or luminal domain-like LAP1 (LULL1). We previously demonstrated that depletion of LAP1 from hepatocytes has more modest effects on liver triglyceride secretion and steatosis development than depletion of torsinA. We now show that depletion of LULL1 alone does not significantly decrease triglyceride secretion or cause steatosis. However, simultaneous depletion of both LAP1 and LULL1 leads to defective triglyceride secretion and marked steatosis similar to that observed with depletion of torsinA. Depletion of both LAP1 and torsinA from hepatocytes generated phenotypes similar to those observed with only torsinA depletion, implying that the 2 proteins act in the same pathway in liver lipid metabolism. Our results demonstrate that torsinA and its activators dynamically regulate hepatic lipid metabolism.