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e necessity of long-term follow-up of survivors of COVID-19.

Infection by SARS-CoV-2 caused some mild impairments of survivors within the first three months of their discharge and the duration of SARS-CoV-2 antibody was limited, which indicates the necessity of long-term follow-up of survivors of COVID-19.As the coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, there are growing concerns about patients' mental health. We investigated psychological problems in COVID-19 patients assessed with self-reported questionnaires including the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7 scale, and Impact of Event Scale-Revised Korean version. Ten patients who recovered from COVID-19 pneumonia without complications underwent self-reported questionnaires about 1 month after discharge. Of them, 10% reported depression and posttraumatic stress disorder (PTSD) while 50% had depression during the treatment. Perceived stigma and history of psychiatric treatment affected PTSD symptom severity, consistent with previous emerging infectious diseases. Survivors also reported that they were concerned about infecting others and being discriminated and that they chose to avoid others after discharge. Further support and strategy to minimize their psychosocial difficulties after discharge should be considered.Previous exposure to antimicrobials is a major risk factor for Clostridioides difficile infection (CDI). Antibiotic prescription and C. difficile toxin assay records of patients admitted to a tertiary hospital in Korea from 2009 to 2013 were collected to investigate the association between antibiotic consumption and CDI incidence. A Spearman's correlation analysis between CDI incidence (positive result of toxin assay/10,000 admissions) and antibiotic consumption (defined daily dose/1,000 patient-days) was performed on a monthly basis. Using the matched month approach, we found a significant correlation between CDI rate and moxifloxacin consumption (Spearman's r = 0.351, P less then 0.001). Furthermore, using the one-month delay approach, we found that the consumption of clindamycin (Spearman's r = 0.272, P = 0.037) and moxifloxacin (Spearman's r = 0.297, P = 0.022) was significantly correlated with CDI incidence. Extended-spectrum cephalosporins did not have any effect on CDI incidence.

Korea is one of the countries with the highest rate of suicide, while suicidality is known to be closely related to mental illnesses. The study aimed to evaluate the suicide rates in psychiatric patients, to compare it to that of the general population, and to investigate the differences among psychiatric diagnoses and comorbidities.

Medical records and mortality statistics of psychiatric patients at Seoul National University Hospital from 2003 to 2017 were reviewed. The standardized mortality ratio (SMR) for suicide was calculated to compare the psychiatric patients with the general population. The diagnosis-specific standardized mortality rate and hazard ratio (HR) were adjusted by age, sex, and psychiatric comorbidity (i.e., personality disorder and/or pain disorder).

A total of 40,692 survivors or non-suicidal deaths and 597 suicidal death were included. The suicide rate among psychiatric patients was 5.13-fold higher than that of the general population. Proxalutamide Psychotic disorder had the highest SMR (13.03; 95% confidence interval [CI], 11.23-15.03), followed by bipolar disorder (10.26; 95% CI, 7.97-13.00) and substance-related disorder (6.78; 95% CI, 4.14-10.47). In survival analysis, psychotic disorder had the highest HR (4.16; 95% CI, 2.86-6.05), which was further increased with younger age, male sex, and comorbidity of personality disorder.

All psychiatric patients are at a higher risk of suicide compared to the general population, and the risk is highest for those diagnosed with psychotic disorder.

All psychiatric patients are at a higher risk of suicide compared to the general population, and the risk is highest for those diagnosed with psychotic disorder.

Although international guidelines recommend palliative care approaches for many serious illnesses, the palliative needs of patients with serious illnesses other than cancer are often unmet, mainly due to insufficient prognosis-related discussion. We investigated physicians' and the general public's respective attitudes toward prognostic disclosure for several serious illnesses.

We conducted a cross-sectional survey of 928 physicians, sourced from 12 hospitals and the Korean Medical Association, and 1,005 members of the general public, sourced from all 17 administrative divisions in Korea.

For most illnesses, most physicians (adjusted proportions - end-organ failure, 99.0%; incurable genetic or neurologic disease, 98.5%; acquired immune deficiency syndrome [AIDS], 98.4%; stroke or Parkinson's disease, 96.0%; and dementia, 89.6%) and members of the general public (end-organ failure, 92.0%; incurable genetic or neurologic disease, 92.5%; AIDS, 91.5%; stroke or Parkinson's disease, 92.1%; and dementia, 86.9utonomy for several serious illnesses. The low response rate of physicians might limit the generalizability of the results.

This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.

A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.

F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.

The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.