An 18-year-old man collapsed at his workplace while putting desiccant into a cylindrical mixer, 2 m x 1 m in dimension, which contained rubber powder. His coworker found him collapsed, and he was transported to a hospital by a 119 rescue team, where he died. Prior to the incident, liquid nitrogen had been placed into the mixer to lower the temperature of the rubber powder. There were no injuries or disease that could have caused death. Analysis of the gas in the mixer revealed that the O2 concentration had dropped to 3.7% in 2 minutes following addition of the liquid nitrogen. Therefore, it was concluded that the cause of death was asphyxia due to oxygen deficiency caused by liquid nitrogen evaporation.
Adolescent ; Anoxia* ; Asphyxia* ; Autopsy ; Cause of Death ; Humans ; Nitrogen* ; Oxygen* ; Rubber

To determine chronic alcoholism (or alcohol abuse) in postmortem cases, investigating the evidence in incident (or medical) reports is important, but it may not be certain. The indicator of alcohol abuse over long time periods was analyzed from hair as ethyl glucuronide (EtG). Beta-hydroxybutyrate (BHB) was analyzed from blood as a representative indicator of ketosis. Moreover, the blood was analyzed for ethanol (ethyl alcohol, EtOH) and EtG before death to determine drinking. Case 1 had chronic alcoholism and a history of diabetic disease. EtG concentration was 1,244 pg/mg in hair, and BHB in the blood was 276 mg/L. EtOH was less than 0.010% in the blood, however EtG was 0.38 mg/ L as drinking positive. Case 2 had a habit of drinking well, with EtG in hair of 54 pg/mg, BHB in the blood of 371 mg/L, EtOH of < 0.010%, and EtG of 0.81 mg/L.Although the EtOH was not detected in the blood, it was evaluated that alcohol was consumed before death, due to the EtG detected. In conclusion, forensic information from simultaneous analysis of EtG and BHB in biological samples (hair or blood) could be more cause of death effective assistant in chronic alcoholism (or alcohol abuse).

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

PURPOSE: We investigated the prevalence and risk factors of allergic rhinitis (AR), nationwide in random children and adolescents of Korea. METHODS: A modified International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire survey was done in 1,820 children from elementary, middle, and high school nationwide in Korea. The subjects were selected by the stratifying sampling method by school grade and five regions. Current AR was defined as having AR symptoms during the last 12 months with a history of physician-diagnosed AR. Skin prick tests for 18 common allergens were performed. RESULTS: The number of males was 945, and that of females was 875. The mean age of the patients was 12.61+/-3.40 years. The prevalence of current AR and atopic current AR were 29.0% and 18.7%, respectively. Risk factors for current AR were male (adjusted odds ratio [aOR], 1.486; 95% confidence interval [CI], 1.189-1.856), family history of paternal AR (aOR, 3.208; 95% CI, 2.460-4.182), family history of maternal AR (aOR, 3.138; 95% CI, 2.446-4.025), antibiotic use in infancy (aOR, 1.547; 95% CI, 1.228-1.949), mold exposure during infancy (aOR, 1.416; 95% CI, 1.103-1.819), mold exposure during the last 12 months (aOR, 1.285; 95% CI, 1.012-1.630), and sensitization on skin prick tests (aOR, 2.596; 95% CI, 2.055-3.279). Risk factors for atopic current AR were the same as those of current AR, whereas breast-milk feeding (aOR, 0.720; 95% CI, 0.530-0.976) was a protective factor. Sensitized allergens as risk factors for current AR were Dermatophagoides pteronyssinus, Dermatophagoides farina, ragweed, mugwort, oak, alder, birch, Japanese hop, cat, and dog. CONCLUSION: The prevalences of current AR and atopic current AR were 29.0% and 18.7%, respectively. Male, sex parental AR, antibiotic use in infancy, mold exposure during the last 12 months, mold exposure during infancy, and atopic sensitization were risk factors for current AR. Breast-milk feeding was a protective factor for atopic current AR. Aeroallergen sensitization was an important risk factor for AR.
Adolescent ; Allergens ; Alnus ; Ambrosia ; Animals ; Artemisia ; Asian Continental Ancestry Group ; Asthma ; Betula ; Cats ; Child ; Dermatophagoides pteronyssinus ; Dogs ; Female ; Fungi ; Humans ; Humulus ; Hypersensitivity ; Korea ; Male ; Odds Ratio ; Parents ; Prevalence* ; Pyroglyphidae ; Rhinitis* ; Risk Factors* ; Skin