The Epidemiological and Clinical Characteristics of the Largest Outbreak of Enterohemorrhagic Escherichia coli in Korea

Background: The largest outbreak of enterohemorrhagic Escherichia coli (EHEC) O157:H7 occurred at a preschool in South Korea from June 12 to 29, 2020. This study aimed to analyze the epidemiological and clinical characteristics of EHEC infection in this outbreak. Methods: Epidemiological investigation was performed on all 184 children and 19 workers at the preschool using a standard questionnaire to assess symptoms, food intake, attendance, and special activity history. Pulsed-field gel electrophoresis analysis of confirmed cases was performed to determine genetic relevance. Results: During this outbreak, 103 children were affected, whereas only one infection was identified in adults. Of the 103 pediatric patients, 85 had symptoms (82.5%), including diarrhea, abdominal pain, bloody stool, fever, and vomiting. Thirty-two patients (31.1%) were hospitalized, 15 (14.6%) were diagnosed with hemolytic uremic syndrome, and 4 (3.9%) received dialysis treatment. Pulsed-field gel electrophoresis analysis identified 4 genotypes with high genetic relevance (92.3%). Epidemiological investigation revealed that this outbreak might have occurred from ingesting foods stored in a refrigerator with a constant temperature above 10°C, which was conducive to bacterial growth. Despite several measures after outbreak recognition, new infections continued to appear. Therefore, the preschool was forced to close on June 19 to prevent further person-to-person transmission. Conclusion: Our findings from the response to the largest outbreak will help prepare countermeasures against future EHEC outbreak.ope

Feasibility and Efficacy of Morning Light Therapy for Adults with Insomnia: A Pilot, Randomized, Open-Label, Two-Arm Study

Background and Objectives: Light therapy (LT) is used as an adjunctive treatment for sleep problems. This study evaluates the impact of LT on sleep quality and sleep-related parameters in patients with sleep disorders. Materials and Methods: We performed a pilot, randomized, open-label clinical trial. Fourteen patients aged 20-60 years with insomnia were randomized into the control and LT groups (1:1 ratio). The LT group was instructed to use a device that provides bright LT (6000 K, 380 lux, wavelength 480 nm) for at least 25 min before 09:00 a.m. for two weeks. A self-reported questionnaire was used to evaluate circadian preference, mood, and sleep-related parameters. We analyzed serum cortisol levels and clock genes' expression. Results: The Epworth Sleepiness Scale (ESS), insomnia severity index(ISI), and Pittsburgh Sleep Quality index(PSQI) were significantly improved within the LT group only after the two-week period. When comparing the two groups, only the change in ESS was significant (mean difference, control: -0.14 vs. LT: -1.43, p = 0.021) after adjusting for the baseline characteristics. There were no significant differences in serum cortisol or clock genes' expression. Conclusions: LT can improve daytime sleepiness in patients with sleep disorders; however, further well-designed studies are warranted to confirm its efficacy.ope

Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation

The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.ope

Comparison of high-dose IVIG and rituximab versus rituximab as a preemptive therapy for de novo donor-specific antibodies in kidney transplant patients

De novo donor-specific antibody (dnDSA) is associated with a higher risk of kidney graft failure. However, it is unknown whether preemptive treatment of subclinical dnDSA is beneficial. Here, we assessed the efficacy of high-dose intravenous immunoglobulin (IVIG) and rituximab combination therapy for subclinical dnDSA. An open-label randomized controlled clinical trial was conducted at two Korean institutions. Adult (aged ≥ 19 years) kidney transplant patients with subclinical class II dnDSA (mean fluorescence intensity ≥ 1000) were enrolled. Eligible participants were randomly assigned to receive rituximab or rituximab with IVIG at a 1:1 ratio. The primary endpoint was the change in dnDSA titer at 3 and 12 months after treatment. A total of 46 patients (24 for rituximab and 22 for rituximab with IVIG) were included in the analysis. The mean baseline estimated glomerular filtration rate was 66.7 ± 16.3 mL/min/1.73 m2. The titer decline of immune-dominant dnDSA at 12 months in both the preemptive groups was significant. However, there was no difference between the two groups at 12 months. Either kidney allograft function or proteinuria did not differ between the two groups. No antibody-mediated rejection occurred in either group. Preemptive treatment with high-dose IVIG combined with rituximab did not show a better dnDSA reduction compared with rituximab alone. Trial registration : IVIG/Rituximab versus Rituximab in Kidney Transplant With de Novo Donor-specific Antibodies (ClinicalTrials.gov Identifier: NCT04033276, first trial registration (26/07/2019). © 2023, The Author(s).ope

자발적 보고 데이터베이스에서 약물 간 상호작용 신호 탐지의 트리 기반 검색통계량

The concomitant use of multiple drugs can increase the risk of adverse events (AE) as a result of drug-drug interactions (DDI). Because clinical trials focus on the efficacy and safety of a single drug, it is difficult to identify DDIs in general. Thus, it is important to conduct post-market drug safety monitoring through spontaneous reporting systems. Several data statistical methodologies have been proposed to detect signals of DDIs with disproportionately high reporting rates using databases of spontaneous reporting systems. However, the previously proposed methods in detecting signals of DDIs do not reflect a hierarchical structure of AEs, such as the classification system of AE codes. The classification system of AE codes is an important factor in delivering consistent information. For single drug-induced AEs, a tree-based scan statistic is used as a signal detection method that can take into account hierarchical structures of AEs. Spontaneous reporting systems can have potential reporting biases, which can lead to problems of misdetection of false signals or failure to find true signals. However, most of the DDI signal detection methods do not consider reporting bias. In this study, we have developed a statistical methodology that can reflect the hierarchical structure of AEs and potential reporting bias to detect signals of AEs caused by DDIs. The hierarchical structure of AEs was considered through tree-based scan statistics, and potential reporting bias was ruled out through the assumption that DDIs are multiplication interactions. Our proposed method showed better performance for the area under the precision recall curve (AUPRC) than other methods through simulation studies. In addition, only our proposed method maintained the type I error and false discovery rate at low level in all simulation settings. We also demonstrated the use of the proposed method by analyzing real data in the KAERS database. 여러 약물을 함께 복용하면 약물 간 상호작용으로 이상반응의 위험이 증가할 수 있다. 임상 시험은 일반적으로 단일 약물의 효능과 안전성의 초점을 두어 약물 간 상호작용을 파악하기 어렵다는 문제가 있다. 따라서 약물이 시판된 후 자발적 보고 시스템을 통해 수집된 자료로 약물 간 상호작용에 의한 이상반응을 지속적으로 추적하는 것이 중요하다. 이러한 약물 간 상호작용을 파악하기 위해 자발적 보고 시스템을 통한 약물 간 상호작용 신호 탐지 방법들이 제안이 되었다. 하지만 기존에 제안된 방법들은 약물 간 상호작용 신호 탐지에 이상반응코드의 분류체계와 같은 계층적 구조를 반영하지 못한다. 이상반응코드의 분류체계는 일관된 정보전달을 위해 필수적인 요소이며, 계층적 구조를 고려할 수 있는 단일약물이상반응신호탐지방법으로 트리 기반 검색 통계량이 사용되고 있다. 자발적 보고시스템은 잠재적인 보고 편향이 존재할 수 있으며, 이로 인해 거짓 신호를 잘못 탐지해 내거나 실제 신호를 찾지 못하는 문제가 발생할 수 있다. 하지만 현재 제안된 대부분의 방법들이 보고편향에 대해 고려를 하고 있지 않다. 따라서 본 논문에서는 이상반응의 계층적 구조와 잠재적인 보고편향을 반영할 수 있는 약물 간 상호작용 신호 탐지에 대한 통계량을 제안하였다. 이상반응의 계층적 구조는 트리 기반 검색 통계량을 통해 고려하였으며, 약물 간 상호작용이 곱셈 상호작용이라는 가정을 통하여 잠재적인 보고편향을 배제하였다. 시뮬레이션을 통해 제안된 방법이 다른 방법들보다 정밀도 재현율곡선 아래영역(Area under the precision recall curve)에 대해 뛰어난 성능을 보이는 것을 확인하였다. 또한 제안된 방법만이 1종 오류와 위발견율을 낮은 수준으로 유지하는 결과를 보였으며, 한국의약품안전관리원의 부작용 보고 시스템 자료를 통해 실제 자료 분석에 대한 결과도 확인하였다.open박

충남리포트-124호-한국 경제의 새로운 성장전략 모색과 민선6기 충남의 발전 방향

● 한국경제는 IMF 이후 성장률 하락 현상을 보이고 있으며, 이와 함께 고용부진, 가계부채 증가, 빈곤현상의 심화 등 위기적 현상에 직면. ● 한국은 거시경제 차원에서 볼 때, 구조전환의 시간과 여력을 확보하기 위해 새로운 성장추세의 회복이 필요. ● 새로운 성장은 양질의 고용이 늘어나고, 중소기업의 경쟁력을 강화하며, 기업의 국제경쟁력을 높이고, 혁신과 창의력이 뒷받침되는 성장이어야 함. 또한 성장과실이 골고루 돌아가야 하며, 튼튼한 사회 안전망을 갖추고 있어야 함. ● 한편, 이러한 새로운 성장전략을 모색하는데 있어 한국의 높은 무역 의존도, 낮은 민간소비, 구조적으로 취약한 고용상황 등에 대한 고려가 필요. - 이후 생략1. 한국경제의 현황과 문제점 2. 성장은 필요한가? 3. 새로운 성장전략의 모색 4. 충남이 지향해야 할 발전 방