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.

Virulence Genes, Antimicrobial Resistance, and Genotypes of Campylobacter jejuni Isolated from Chicken Slaughterhouses in South Korea.

Campylobacter jejuni represents one of the leading causes of bacterial gastroenteritis in humans and is primarily linked to chicken meat contamination. In the present study, we analyzed the virulence and survival genes, antimicrobial resistance, and the clonal distribution of 50 C. jejuni isolates obtained from various sources in 14 chicken slaughterhouses across 8 provinces in South Korea from 2019 to 2022. Furthermore, we determined their genetic relatedness to human-derived isolates registered in PubMLST using multilocus sequence typing (MLST). All isolates harbored various virulence and survival genes (flhA, cadF, cdtA, cdtC, cmeA, and sodB) out of 17 tested genes, as confirmed via polymerase chain reaction analysis. Adherence factor gene virB11 was not detected in any isolate. All isolates harbored 12 or more virulence and survival genes. Antimicrobial susceptibility testing indicated that ciprofloxacin resistance was the most prevalent (84.0%), followed by nalidixic acid (82.0%) and tetracycline (52.0%) resistance. MLST analysis of the isolates revealed 18 sequence types (STs), including four new ones. Overlapping STs between chicken slaughterhouse and human-derived isolates included ST42, ST45, ST50, ST137, ST354, and ST464. Our study identified 11 clonal complexes (CCs), with CC-21 being the most prevalent in both human and chicken slaughterhouse-derived isolates. This study provides comprehensive insights into recent C. jejuni isolates from chicken slaughterhouses, including data on quinolone resistance and virulence factors. The MLST-based genetic relatedness between isolates from humans and chicken slaughterhouses in this study suggests the potential of C. jejuni transmission from chickens to humans through the food chain. This study suggests the need for improved management practices in chicken slaughterhouses to reduce the transmission of chicken slaughterhouse-derived C. jejuni to humans.

Treatment, Education, and Prognosis of Slow Learners (Borderline Intelligence).

Objectives: Borderline intelligence has been excluded from formal diagnostic systems and not included in disability diagnoses, leaving gaps in treatment, education, and social welfare despite various functional impairments. Therefore, we aimed to shed light on ways to enhance the intelligence and functioning of individuals with borderline intelligence by reviewing research on its progression, education, and treatment. Methods: Ten studies that met the inclusion and exclusion criteria were included in the final literature review and analyzed according to detailed topics (participant characteristics, design, and results). Results: Borderline intelligence is associated with various comorbid conditions, such as anxiety, depression, attention deficit/hyperactivity disorder, and addictive disorders, which negatively impact its course and prognosis. Individuals with borderline intelligence often face challenges in academics, employment, interpersonal relationships, and health owing to lifelong cognitive impairments. The treatment of borderline intelligence necessitates addressing environmental factors, such as neglect and abuse, as well as treating comorbid mental disorders, which are crucial for prognosis. Tailoring treatment programs for cognitive profile characteristics have been proposed, and studies have reported the effectiveness of pharmacotherapy, working memory training, and intensive rehabilitation training. Therefore, early intervention during childhood brain development is necessary. Risk factors, such as lack of parental education, and their impact on treatment outcomes have also been reported. Conclusion: Extensive research is needed on education, treatment, and prognosis related to borderline intelligence. Active intervention for children with borderline intelligence is essential to improve their functioning and quality of life.

The impact of coping strategies and positive resources on post-traumatic stress symptoms among bereaved families of the Sewol ferry disaster.

Introduction: This study investigated the long-term prevalence of, and factors associated with, post-traumatic stress disorder (PTSD) among the bereaved families of the Sewol ferry disaster, in which 250 students lost their lives during a school excursion. Methods: Eight years after the disaster, 181 family members were surveyed, and the prevalence of clinical PTSD symptoms was estimated. The Positive Resources Test (POREST), the Duke-UNC Functional Social Support Questionnaire, and the Brief COPE were evaluated using self-report measures. The multivariable binomial logistic regression was used to identify protective and risk factors for PTSD. Results: PTSD symptoms were present in 49.7% of the family members 8 years after the incident. A one-point increase in the score on the optimism subscale of the POREST was associated with a 20.1% decreased likelihood of having clinical PTSD symptoms (OR = 0.799; p = 0.027; 95% CI = 0.655-0.975). Conversely, a one-point increase in the score on the avoidant subscale of Brief COPE was associated with a 13.2% increased likelihood of having clinical PTSD symptoms (OR = 1.132; p = 0.041; 95% CI = 1.005-1.274). Discussion: Our results provide evidence of the need for long-term mental health monitoring of bereaved families of disaster victims, along with valuable insights for the development of mental health intervention programs.

Effect of Rumen-Protected L-Tryptophan or L-Ascorbic Acid on Plasma Metabolites and Milk Production Characteristics of Lactating Holstein Cows during Summer Conditions.

This study investigated the effects of rumen-protected L-tryptophan or L-ascorbic acid supplementation on the productivity of lactating Holstein cows during a high-temperature period. Thirty cows were assigned to three dietary groups: control (CON), treatment 1 (TRT 1; rumen-protected L-tryptophan, 20 g/cow/d), and treatment 2 (TRT 2; rumen-protected L-ascorbic acid, 20 g/cow/d). As the high-temperature period progressed, the decrease in milk yield and dry matter intake (DMI) in the TRT 1 and TRT 2 groups was lower than that in the CON group. The total protein level in the plasma of the TRT 1 group was higher than that in the CON group (p < 0.05). Milk melatonin concentration was higher in the TRT 1 group than in the CON and TRT 2 groups (p < 0.05). Thus, the present results indicate that rumen-protected L-tryptophan or L-ascorbic acid has positive effects in preventing declines in DMI and milk yield by reducing heat stress in Holstein cows. In particular, rumen-protected L-tryptophan is considered effective in increasing the melatonin concentration in milk.

Updated Clinical Practice Guidelines for the Diagnosis and Management of Long COVID.

"Long COVID" is a term used to describe a condition when the symptoms and signs associated with coronavirus disease 2019 (COVID-19) persist for more than three months among patients infected with COVID-19; this condition has been reported globally and poses a serious public health issue. Long COVID can manifest in various forms, highlighting the need for appropriate evaluation and management by experts from various fields. However, due to the lack of clear clinical definitions, knowledge of pathophysiology, diagnostic methods, and treatment protocols, it is necessary to develop the best standard clinical guidelines based on the scientific evidence reported to date. We developed this clinical guideline for diagnosing and treating long COVID by analyzing the latest research data collected from the start of the COVID-19 pandemic until June 2023, along with the consensus of expert opinions. This guideline provides recommendations for diagnosis and treatment that can be applied in clinical practice, based on a total of 32 key questions related to patients with long COVID. The evaluation of patients with long COVID should be comprehensive, including medical history, physical examination, blood tests, imaging studies, and functional tests. To reduce the risk of developing long COVID, vaccination and antiviral treatment during the acute phase are recommended. This guideline will be revised when there is a reasonable need for updates based on the availability of new knowledge on the diagnosis and treatment of long COVID.

Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.