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Vaccines are vital for public health, but concerns about adverse effects, particularly myocarditis and pericarditis linked to COVID-19 vaccines-, persist. This study investigates the application of Brighton Collaboration case definition to national vaccine safety data related to post-COVID-19 vaccine myo/pericarditis, utilizing claims under the Korea National Vaccine Injury Compensation Program (NIVCP). This study analyzed 190 medical records of individuals who claimed to have developed myo/pericarditis after receiving the COVID-19 vaccine, as reported to the NVICP between specified dates, categorizing cases based on the Brighton criteria for myocarditis or pericarditis. Between 2021–2022, NVICP received 190 cases meeting the Brighton criteria for myocarditis or pericarditis at levels 1, 2, or 3. Most cases fell into Level 2 (70%), followed by Level 1 (29%), and one at Level 3 (1%), with Level 1 cases showing a higher hospitalization rate (87.3%) and a notable proportion requiring admission to the Intensive Care Unit (25.5%). Chest pain and Troponin-I/T elevation were common findings in Level 1 cases, while Level 2 cases exhibited similar patterns but at a slightly lower frequency. Electrocardiogram and echocardiography findings differed between the two levels. The Brighton Collaboration case definition proved valuable for classifying and assessing AEFI data, enhancing our understanding of the potential relationship between myocarditis and the COVID-19 vaccine. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding resets insulin resistance caused by pregnancy however, studies on the association between breastfeeding and diabetes mellitus (DM) have reported inconsistent results. Therefore, we aimed to investigate the risk of DM according to breastfeeding duration in large-scale population-based retrospective study. In addition, machine-learning prediction models for DM and hemoglobin A1c (HbA1c) were developed to further evaluate this association. Methods: We used the Korean National Health and Nutrition Examination Surveys database, a nationwide and population-based health survey from 2010 to 2020. We included 15,946 postmenopausal women with a history of delivery, whom we divided into three groups according to the average breastfeeding duration: (1) no breastfeeding, (2) < 12 months breastfeeding, and (3) ≥ 12 months breastfeeding. Prediction models for DM and HbA1c were developed using an artificial neural network, decision tree, logistic regression, Naïve Bayes, random forest, and support vector machine. Results: In total, 2248 (14.1%) women had DM and 14,402 (90.3%) had a history of breastfeeding. The prevalence of DM was the lowest in the < 12 breastfeeding group (no breastfeeding vs. < 12 months breastfeeding vs. ≥ 12 months breastfeeding; 161 [10.4%] vs. 362 [9.0%] vs. 1,725 [16.7%], p < 0.001). HbA1c levels were also the lowest in the < 12 breastfeeding group (HbA1c: no breastfeeding vs. < 12 months breastfeeding vs. ≥ 12 months breastfeeding; 5.9% vs. 5.9% vs. 6.1%, respectively, p < 0.001). After adjustment for covariates, the risk of DM was significantly increased in both, the no breastfeeding (adjusted odds ratio [aOR] 1.29; 95% CI 1.29, 1.62]) and ≥ 12 months of breastfeeding groups (aOR 1.18; 95% CI 1.01, 1.37) compared to that in the < 12 months breastfeeding group. The accuracy and the area under the receiver-operating-characteristic curve of the DM prediction model were 0.93 and 0.95, respectively. The average breastfeeding duration was ranked among the top 15 determinants of DM, which supported the strong association between breastfeeding duration and DM. This association was also observed in a prediction model for HbA1c. Conclusions: Women who did not breasted had a higher risk of developing DM than those who breastfed for up to 12 months. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age. [ABSTRACT FROM AUTHOR]

Although preterm birth (PTB), a birth before 34 weeks of gestation accounts for only less than 3% of total births, it is a critical cause of various perinatal morbidity and mortality. Several studies have been conducted on the association between maternal exposure to PM and PTB, but the results were inconsistent. Moreover, no study has analyzed the risk of PM on PTB among women with cardiovascular diseases, even though those were thought to be highly susceptible to PM considering the cardiovascular effect of PM. Therefore, we aimed to evaluate the effect of PM10 on early PTB according to the period of exposure, using machine learning with data from Korea National Health Insurance Service (KNHI) claims. Furthermore, we conducted subgroup analysis to compare the risk of PM on early PTB among pregnant women with cardiovascular diseases and those without. A total of 149,643 primiparous singleton women aged 25 to 40 years who delivered babies in 2017 were included. Random forest feature importance and SHAP (Shapley additive explanations) value were used to identify the effect of PM10 on early PTB in comparison with other well-known contributing factors of PTB. AUC and accuracy of PTB prediction model using random forest were 0.9988 and 0.9984, respectively. Maternal exposure to PM10 was one of the major predictors of early PTB. PM10 concentration of 5 to 7 months before delivery, the first and early second trimester of pregnancy, ranked high in feature importance. SHAP value showed that higher PM10 concentrations before 5 to 7 months before delivery were associated with an increased risk of early PTB. The probability of early PTB was increased by 7.73%, 10.58%, or 11.11% if a variable PM10 concentration of 5, 6, or 7 months before delivery was included to the prediction model. Furthermore, women with cardiovascular diseases were more susceptible to PM10 concentration in terms of risk for early PTB than those without cardiovascular diseases. Maternal exposure to PM10 has a strong association with early PTB. In addition, in the context of PTB, pregnant women with cardiovascular diseases are a high-risk group of PM10 and the first and early second trimester is a high-risk period of PM10. [ABSTRACT FROM AUTHOR]

Introduction: Allergic rhinitis (AR) and asthma are closely associated in children. Reduced FEF25%-75% which reflects small airway airflow limitation is frequently observed in asthma. This study aimed to examine the proportion of small airway dysfunction in children with AR and to determine its associated factors.Methods: The medical records of 144 aged 6-18-year children with AR without overt asthmatic symptoms were retrospectively reviewed. Subjects were divided into 2 groups according to the FEF25%-75% values; normal FEF25%-75% group (n = 129) and reduced FEF25%-75% group (n = 15). Clinical data, allergen sensitization profile, exhaled nitric oxide, spirometry, and methacholine provocation test results were compared between the two groups.Results: The mean FEV1 and FEF25%-75% values in the reduced FEF25%-75% group (73.5 ± 9.4%pred and 56.0 ± 7.7%pred, respectively) were significantly lower than in the normal FEF25%-75% group (87.0 ± 12.5%pred and 99.1 ± 21.4%pred, respectively). The mean disease duration was significantly longer in the reduced FEF25%-75% group than in the normal FEF25%-75% group (5.39 ± 1.85 y vs 3.14 ± 1.80 y, p < 0.001). Subjects with positive bronchial hyperresponsiveness (MChPC20<16 mg/mL) were more frequently detected in the reduced FEF25%-75% group than in the normal FEF25%-75% group (26.7% vs 8.52%, p = 0.013). Long disease duration and severity of AR were significantly associated with impaired FEF25%-75% values.Conclusions: Subjects with AR alone may have impaired FEF25%-75% values which is considered as a marker of early bronchial involvement. Longer disease duration and severity of AR are important risk factors for progressive declines in small airway function. Physicians should be aware of need for the measurement of FEF25%-75% values for early detection of small airway dysfunction, particularly in children with severe long-lasting allergic rhinitis. [ABSTRACT FROM AUTHOR]

Background: Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB. Methods: A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25–40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy. Results: Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value. Conclusions: Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Machine learning analysis for the association between breast feeding and metabolic syndrome in women" published in Scientific Reports. The correction addresses the omission of the Funding section in the original article. The corrected Funding section acknowledges support from the Korea University Medical Center, the Korea Health Industry Development Institute, and 4P Lab, Co., Ltd for data analysis. The correction notice confirms that there was no additional external funding received for the study and clarifies that the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. [Extracted from the article]

Background: Recent data suggested that dysbiosis of the gut microbiome is associated with childhood allergic diseases. Oral administration of probiotic formulations may improve the severity of atopic dermatitis (AD) by restoring imbalanced gut microbiota and reducing intestinal inflammation in children. Objectives: The aim of this study was to investigate the effects of a probiotic mixture on the clinical severity of AD, gut inflammatory markers and alterations in microbiome dysbiosis in children with AD. Methods: A total of 25 subjects were enrolled in this study and administered with a mixture of probiotic strains consisting of Lactobacilli and Bifidobacteria for 4 weeks. The clinical efficacy of the probiotic mixture was assessed using SCORAD index and TEWL. Faecal calprotectin levels were measured as a marker for intestinal inflammation. The composition and diversity of the gut microbiome were analysed using 16S rRNA pyrosequencing. Results: The SCORAD (38.9 ± 17.2 vs 29.0 ± 15.4, P < 0.001) and TEWL (58.3 ± 12.5 vs 27.3 ± 8.7 g/m2/h, P = 0.028) were significantly decreased after 4 weeks administration of the probiotic mixture. The faecal calprotectin level (121.5 [27.7–292.9] vs 37.0 μg/g [12.6–108.9 μg/g], P = 0.038) was significantly decreased. The α‐diversity and composition of the gut microbiome were not significantly changed, but β‐diversity was increased after 4 weeks. Conclusions: The oral administration of the probiotic mixture was effective in reducing clinical severity and intestinal inflammation in children with AD. Gut microbial diversity was slightly increased after administration of the probiotic mixture. The results of this study suggest that a probiotic mixture can alleviate AD by decreasing inflammation and modulating the gut microbiota in children with AD. [ABSTRACT FROM AUTHOR]

It has been shown that aerobic exercise improves atopic dermatitis (AD), although the mechanism is not clear. Here, we propose a hypothesis that moderate‐intensity aerobic exercise improves AD in a mouse model through modulating allergic inflammation. The DNCB‐treated mouse model for eczema was divided into 3 groups: (a) not subjected to aerobic exercise, (b) subjected to continuous aerobic exercise and (c) subjected to accumulated aerobic exercise. After given exercise using a treadmill device either 30 min/d or 10 min × 3/day at a speed of 16 m/min, for 9 days, respectively, dermatitis symptom score, thickness of epidermis/dermis and eosinophil infiltration were decreased in the 2 exercise groups compared to the sedentary living group. The serum levels of IgE, MCP‐1 and MDC showed a significant decrease both in the continuous or accumulated exercise groups. Moderate‐intensity aerobic exercise ameliorates dermatitis symptoms through immune modulation in the DNCB‐treated mouse model for eczema. [ABSTRACT FROM AUTHOR]