Black spot of sweet persimmon, caused by Alternaria alternata, occurred in an orchard in Gyeongnam province, Korea in 2012. The symptom was appearance of 0.5 to 4 cm black spots on the surface of fruit. The pathogen was isolated from flesh of disease lesions. The causal agent was identified as A. alternata by morphological characteristics and sequencers of the internal transcribed spacer (ITS) 1 and ITS4 regions of rRNA. Artificial inoculation of the pathogen resulted in development of disease symptoms and the re-isolated pathogen showed characteristics of A. alternata.
Alternaria* ; Diospyros* ; Fruit* ; Korea

PURPOSE: On-line medical control, in addition to indirect control like protocols, is known to exert a positive effect on the quality of prehospital care. Because the decision-making process of directing physicians depends on the information provided by prehospital providers via telecommunication, brief and organized reporting of significant points is of paramount importance. METHODS: Telecommunications regarding direct medical control provided by emergency physicians in a university hospital were recorded from May 1 to June 30, 2012. All communications were between cellular phones. Analysis of the recorded dialogues was performed by an independent researcher. RESULTS: A total of 115 cases were included for analyses. Affiliated fire offices were reported in 107 (93.0%) cases, while certification of responding officers was reported in only 62 (53.9%) cases. All five vital signs were reported in only 9 cases (7.8%), including blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. Procedures delivered before telephone contact were reported in 30.4% of cases, and reporting rate of patient response to treatment was 16.5%. Estimated times of arrival to the destined hospital were reported in only 8.7%. CONCLUSION: Reporting procedures regarding prehospital direct medical control should be concise and comprehensive, including essential elements like certification of the provider, consciousness and vital signs of the patient, and estimated time of hospital arrival.
Blood Pressure ; Cellular Phone ; Certification ; Consciousness ; Emergencies ; Emergency Medical Service Communication Systems ; Emergency Medical Services ; Fires ; Heart Rate ; Humans ; Oxygen ; Physician Executives* ; Respiratory Rate ; Telecommunications ; Telephone ; Vital Signs

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Background: To identify a screening tool for obstructive sleep apnea (OSA) and evaluate the effects of endoscopic transsphenoidal surgery on improving OSA in patients with acromegaly. Methods: We prospectively enrolled adults with acromegaly scheduled for endoscopic transsphenoidal surgery. All measurements were conducted when participants were admitted for a baseline work-up for acromegaly before surgery and surveillance approximately 3 to 6 months after surgery. Respiratory event index (REI) was used as a surrogate for apnea-hypopnea index (Trial Registration: NCT03526016). Results: Of the 35 patients with acromegaly (median age, 47 years; 40% men; median body mass index, 24.4 kg/m2), 24 (68.6%) had OSA (REI ≥5/hour), 15 (42.9%) had moderate-to-severe OSA (REI ≥15/hour). At baseline, serum insulin-like growth factor 1 (IGF-1) levels were positively correlated with the REI (ρ=0.53, P=0.001). The sensitivity and negative predictive value of a Snoring, Tiredness, Observed apnea, high blood Pressure-Body mass index, age, Neck circumference, and Gender (STOP-Bang) score ≥ 3 were 93.3% and 87.5%, respectively, detecting moderate-to-severe OSA. Biochemical acromegaly remission was achieved in 32 (91.4%) patients. The median difference in the REI was –9.5/hour (95% confidence interval, –13.3 to –5.3). Half of the 24 patients diagnosed with OSA preoperatively had REI <5/hour postoperatively. In a linear mixed-effects model, changes in the REI across surgery were related to changes in IGF-1 levels. Conclusion The STOP-Bang questionnaire is a reliable tool for OSA among patients with acromegaly. Improvement in OSA severity after surgery is related to decreased IGF-1 levels.

Comorbid conditions impact the survival of patients with severe acute kidney injury (AKI) who require continuous renal replacement therapy (CRRT). The weights assigned to comorbidities in predicting survival vary based on type of index, disease, and advances in management of comorbidities. We developed a modified Charlson Comorbidity Index (CCI) for use in patients with AKI requiring CRRT (mCCI-CRRT) and improved the accuracy of risk stratification for mortality. Methods: A total of 828 patients who received CRRT between 2008 and 2013, from three university hospital cohorts was included to develop the comorbidity score. The weights of the comorbidities were recalibrated using a Cox proportional hazards model adjusted for demographic and clinical information. The modified index was validated in a university hospital cohort (n = 919) using the data of patients treated from 2009 to 2015. Results: Weights for dementia, peptic ulcer disease, any tumor, and metastatic solid tumor were used to recalibrate the mCCI-CRRT. Use of these calibrated weights achieved a 35.4% (95% confidence interval [CI], 22.1%–48.1%) higher performance than unadjusted CCI in reclassification based on continuous net reclassification improvement in logistic regression adjusted for age and sex. After additionally adjusting for hemoglobin and albumin, consistent results were found in risk reclassification, which improved by 35.9% (95% CI, 23.3%–48.5%). Conclusion: The mCCI-CRRT stratifies risk of mortality in AKI patients who require CRRT more accurately than does the original CCI, suggesting that it could serve as a preferred index for use in clinical practice.

Background: Inter-hospital transfer (IHT) for emergency department (ED) admission is a burden to high-level EDs. This study aimed to evaluate the prevalence and ED utilization patterns of patients who underwent single and double IHTs at high-level EDs in South Korea. Methods: This nationwide cross-sectional study analyzed data from the National Emergency Department Information System for the period of 2016–2018. All the patients who underwent IHT at Level I and II emergency centers during this time period were included. The patients were categorized into the single-transfer and double-transfer groups. The clinical characteristics and ED utilization patterns were compared between the two groups. Results: We found that 2.1% of the patients in the ED (n = 265,046) underwent IHTs; 18.1% of the pediatric patients (n = 3,556), and 24.2% of the adult patients (n = 59,498) underwent double transfers. Both pediatric (median, 141.0 vs. 208.0 minutes, P < 0.001) and adult (median, 189.0 vs. 308.0 minutes, P < 0.001) patients in the double-transfer group had longer duration of stay in the EDs. Patient's request was the reason for transfer in 41.9% of all IHTs (111,076 of 265,046). Unavailability of medical resources was the reason for transfer in 30.0% of the double transfers (18,920 of 64,054). Conclusion The incidence of double-transfer of patients is increasing. The main reasons for double transfers were patient's request and unavailability of medical resources at the firsttransfer hospitals. Emergency physicians and policymakers should focus on lowering the number of preventable double transfers.