Background: Hospital environments, particularly shared rooms, are vulnerable to the transmission of carbapenem-resistant Enterobacterales (CRE). The incidence of CRE colonization in the Korean homeless population remains unknown. This study aimed to analyze the impact of targeted active surveillance of CRE in hospital wards following two outbreaks. Methods: This retrospective study was conducted in a homeless ward with shared rooms at a municipal hospital in Seoul. The CRE incidence was calculated from October 1, 2023, to May 31, 2024. Active surveillance was initiated on January 22, 2024. Poisson regression analysis was used to compare CRE incidence events at three months before and four months after the intervention. The risk factors for CRE colonization were also analyzed. Results: The CRE colonization rate decreased from 1.149 to 0.815 per 1,000 patient-days post-intervention; however, the change was not statistically significant (rate ratio: 0.986, 95% confidence interval (95% CI): 0.389-2.496, P=0.976). In contrast to the secondary cases, one acquired CRE case was detected after the intervention without an outbreak. The CRE colonization rate was higher in the homeless ward than in the general ward. CRE colonization was significantly associated with age (adjusted odds ratio (aOR), 1.071; 95% CI: 1.014-1.132;P=0.014), previous antibiotic exposure (aOR, 6.796; 95% CI: 1.215-38.029; P=0.029), and co-colonization with other multidrug resistant bacteria (aOR, 7.168; 95% CI: 2.224-23.096;P=0.001). Conclusion A relatively high incidence of CRE colonization was observed in the homeless ward. After active surveillance, no CRE outbreaks occurred following the implementation.

Background: Hospital environments, particularly shared rooms, are vulnerable to the transmission of carbapenem-resistant Enterobacterales (CRE). The incidence of CRE colonization in the Korean homeless population remains unknown. This study aimed to analyze the impact of targeted active surveillance of CRE in hospital wards following two outbreaks. Methods: This retrospective study was conducted in a homeless ward with shared rooms at a municipal hospital in Seoul. The CRE incidence was calculated from October 1, 2023, to May 31, 2024. Active surveillance was initiated on January 22, 2024. Poisson regression analysis was used to compare CRE incidence events at three months before and four months after the intervention. The risk factors for CRE colonization were also analyzed. Results: The CRE colonization rate decreased from 1.149 to 0.815 per 1,000 patient-days post-intervention; however, the change was not statistically significant (rate ratio: 0.986, 95% confidence interval (95% CI): 0.389-2.496, P=0.976). In contrast to the secondary cases, one acquired CRE case was detected after the intervention without an outbreak. The CRE colonization rate was higher in the homeless ward than in the general ward. CRE colonization was significantly associated with age (adjusted odds ratio (aOR), 1.071; 95% CI: 1.014-1.132;P=0.014), previous antibiotic exposure (aOR, 6.796; 95% CI: 1.215-38.029; P=0.029), and co-colonization with other multidrug resistant bacteria (aOR, 7.168; 95% CI: 2.224-23.096;P=0.001). Conclusion A relatively high incidence of CRE colonization was observed in the homeless ward. After active surveillance, no CRE outbreaks occurred following the implementation.

Background: Hospital environments, particularly shared rooms, are vulnerable to the transmission of carbapenem-resistant Enterobacterales (CRE). The incidence of CRE colonization in the Korean homeless population remains unknown. This study aimed to analyze the impact of targeted active surveillance of CRE in hospital wards following two outbreaks. Methods: This retrospective study was conducted in a homeless ward with shared rooms at a municipal hospital in Seoul. The CRE incidence was calculated from October 1, 2023, to May 31, 2024. Active surveillance was initiated on January 22, 2024. Poisson regression analysis was used to compare CRE incidence events at three months before and four months after the intervention. The risk factors for CRE colonization were also analyzed. Results: The CRE colonization rate decreased from 1.149 to 0.815 per 1,000 patient-days post-intervention; however, the change was not statistically significant (rate ratio: 0.986, 95% confidence interval (95% CI): 0.389-2.496, P=0.976). In contrast to the secondary cases, one acquired CRE case was detected after the intervention without an outbreak. The CRE colonization rate was higher in the homeless ward than in the general ward. CRE colonization was significantly associated with age (adjusted odds ratio (aOR), 1.071; 95% CI: 1.014-1.132;P=0.014), previous antibiotic exposure (aOR, 6.796; 95% CI: 1.215-38.029; P=0.029), and co-colonization with other multidrug resistant bacteria (aOR, 7.168; 95% CI: 2.224-23.096;P=0.001). Conclusion A relatively high incidence of CRE colonization was observed in the homeless ward. After active surveillance, no CRE outbreaks occurred following the implementation.

Background: Hospital environments, particularly shared rooms, are vulnerable to the transmission of carbapenem-resistant Enterobacterales (CRE). The incidence of CRE colonization in the Korean homeless population remains unknown. This study aimed to analyze the impact of targeted active surveillance of CRE in hospital wards following two outbreaks. Methods: This retrospective study was conducted in a homeless ward with shared rooms at a municipal hospital in Seoul. The CRE incidence was calculated from October 1, 2023, to May 31, 2024. Active surveillance was initiated on January 22, 2024. Poisson regression analysis was used to compare CRE incidence events at three months before and four months after the intervention. The risk factors for CRE colonization were also analyzed. Results: The CRE colonization rate decreased from 1.149 to 0.815 per 1,000 patient-days post-intervention; however, the change was not statistically significant (rate ratio: 0.986, 95% confidence interval (95% CI): 0.389-2.496, P=0.976). In contrast to the secondary cases, one acquired CRE case was detected after the intervention without an outbreak. The CRE colonization rate was higher in the homeless ward than in the general ward. CRE colonization was significantly associated with age (adjusted odds ratio (aOR), 1.071; 95% CI: 1.014-1.132;P=0.014), previous antibiotic exposure (aOR, 6.796; 95% CI: 1.215-38.029; P=0.029), and co-colonization with other multidrug resistant bacteria (aOR, 7.168; 95% CI: 2.224-23.096;P=0.001). Conclusion A relatively high incidence of CRE colonization was observed in the homeless ward. After active surveillance, no CRE outbreaks occurred following the implementation.

Purpose: Wrestlers have a lot of direct skin-to-skin contact between wrestlers during matches, and many studies show that wrestlers are vulnerable to the spread of skin infections. However, there have been few studies on skin infections in Korean wrestlers. The purpose of this study was to compare the characteristics of skin diseases in wrestlers and other athletes. Methods: Athletes who visited for skin diseases in the Department of Family Medicine in the Jincheon National Training Center in 2018 were investigated. We calculated the duration of skin disease per training period (DSD/TP) as the number of visits×7 days×1,000/official training days. Athletes with a DSD/TP above the median value or equal to were defined as the high DSD/TP group, and the others were defined as the low DSD/TP group. A chi-square test was used to compare the odds ratio [OR] for these groups about infectious and noninfectious skin diseases. Results: Thirty wrestlers and 89 other sports players visited the infirmary with skin diseases. The probability of belonging to the high DSD/TP group was significantly higher when the wrestlers visited for skin infections than the other athletes (OR, 7.714; 95% confidence interval [CI], 2.699–22.048). However, there was no significant difference in noninfectious skin diseases between wrestling and other sports (OR, 0.569; 95% CI, 0.246–1.320). Conclusion This is the first study that shows Korean national wrestlers with skin diseases receive more treatment for infectious skin diseases than other sports. This study can provide important information on the prevention of wrestlers for skin infections.

Purpose: Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables. Methods: The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital. Results: A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833–0.972).External validation confirmed an AUC of 0.833 (95% CI, 0.800–0.865). Conclusion Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.

Coronavirus disease 2019 (COVID-19) vaccination in immunocompromised, especially transplant recipients, may induce a weaker immune response. But there are limited data on the immune response after COVID-19 vaccination in liver transplant (LT) recipients, especially on the comparison of Ab responses after different vaccine platforms between mRNA and adenoviral vector vaccines. Thus, we conducted a prospective study on LT recipients who received two doses of the ChAdOx1 nCoV-19 (ChAdOx1), mRNA-1273, or BNT162b2 vaccines compared with healthy healthcare workers (HCWs). SARS-CoV-2 S1-specific IgG Ab titers were measured using ELISA.Overall, 89 LT recipients (ChAdOx1, n=16 [18%]) or mRNA vaccines (mRNA-1273 vaccine, n=23 [26%]; BNT162b2 vaccine, n=50 [56%]) received 3 different vaccines. Of them, 16 (18%) had a positive Ab response after one dose of COVID-19 vaccine and 62 (73%) after 2 doses. However, the median Ab titer after two doses of mRNA vaccines was significantly higher (44.6 IU/ml) than after two doses of ChAdOx1 (19.2 IU/ml, p=0.04). The longer time interval from transplantation was significantly associated with high Ab titers after two doses of vaccine (p=0.003). However, mycophenolic acid use was not associated with Ab titers (p=0.53). In conclusion, about 3-quarters of LT recipients had a positive Ab response after 2 doses of vaccine, and the mRNA vaccines induced higher Ab responses than the ChAdOx1 vaccine.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

BACKGROUND/OBJECTIVES: The present study aimed to further investigate the potential health beneficial effects of long-term seaweed supplementation on lipid metabolism and hepatic functions in DIO mice.MATERIALS/METHODS: Four brown seaweeds (Undaria pinnatifida [UP], Laminaria japonica [LJ], Sargassum fulvellum [SF], or Hizikia fusiforme [HF]) were added to a high fat diet (HFD) at a 5% ratio and supplemented to C57BL/6N mice for 16 weeks. Triglycerides (TGs) and total cholesterol (TC) in the liver, feces, and plasma were measured. Fecal bile acid (BA) levels in feces were monitored. Hepatic insulin signaling- and lipogenesis-related proteins were evaluated by Western blot analysis. RESULTS: Fasting blood glucose levels were significantly reduced in the LJ, SF, and HF groups compared to the HFD group by the end of 16-week feeding period. Plasma TG levels and hepatic lipid accumulation were significantly reduced in all 4 seaweed supplemented groups, whereas plasma TC levels were only suppressed in the UP and HF groups compared to the HFD group. Fecal BA levels were significantly elevated by UP, LJ, and SF supplementation compared to HFD feeding only. Lastly, regarding hepatic insulin signaling-related proteins, phosphorylation of 5′-AMP-activated protein kinase was significantly up-regulated by all 4 types of seaweed, whereas phosphorylation of protein kinase B was up-regulated only in the SF and HF groups. Lipogenesis-related proteins in the liver were effectively down-regulated by HF supplementation in DIO mice. CONCLUSIONS Brown seaweed consumption showed hypotriglyceridemic effects in the prolonged DIO mouse model. Specifically, combinatory regulation of BA excretion and lipogenesis-related proteins in the liver by seaweed supplementation contributed to the reduction of plasma and hepatic TG levels, which inhibited hyperglycemia in DIO mice. Thus, the discrepant and species-specific functions of brown seaweeds provide novel insights for the selection of future targets for therapeutic agents.