PURPOSE: This study was conducted to assess the satisfaction, and factors influencing the satisfaction, of patients and guardians with the internal environment and safety of ambulances in Korea. METHODS: Participants were patients and guardians who were transported by public emergency medical service ambulance to an emergency medical center in June 2015. Data were collected using self-administered questionnaires. The degree of satisfaction with the ambulance was categorized as satisfaction (Likert scale 4 or 5) or dissatisfaction (Likert scale 1 or 2). The questionnaires comprised 3 categories: 1) demographics, 2) internal environment (space, light, temperature, humidity, soundproof, and odor), and 3) safety (vibration, leaning of body, and falling objects). RESULTS: Among 84 cases, 80.5% of patients and 83.7% of guardians gave positive responses regarding general satisfaction with the internal environment of the ambulance, but these percentages were lower regarding adequacy of space and light. Four factors had a statistically significantly influence on the degree of satisfaction: 1) sex of guardian regarding adequacy of space, 2) number of guardians regarding general satisfaction, 3) severity of patient regarding guardian's satisfaction with space, 4) diagnosis of patient (trauma vs non-trauma) regarding vibration during transport (all p<0.05). CONCLUSION: Overall, patients and guardians were satisfied with the internal environment and safety of ambulances except for adequacy of space and light.
Ambulances* ; Demography ; Diagnosis ; Emergencies* ; Emergency Medical Services* ; Humans ; Humidity ; Korea* ; Patient Satisfaction ; Pilot Projects* ; Vibration

Discharge against medical advice remains a problematic issue worldwide because it may not only lead to adverse medical outcomes for the patients but also medicolegal problems for emergency physicians. Recently, there have been cases in Korea in which a patient in the emergency room, who had been discharged from hospital without following medical instructions, filed a lawsuit against the hospital and emergency medical staff for their responsibility for their worsening disease since discharge. The court acknowledged the responsibility of the medical staff. To minimize the legal risk and reach the optimal ethical standard for these patients, this paper suggests the best practice guideline for the emergency physicians for patients who request discharge against medical advice from the emergency department in Korea.

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.

The radioprotective efficacy of a methanol extract of the red algae Polyopes lancifolia (Harvey) kawaguchi et wang (mPL) was evaluated in mice subjected to total-body gamma irradiation. mPL protection against radiation-induced oxidative stress was examined by histological evaluation of intestinal crypt-cell survival and liver activities of the antioxidant enzymes superoxide dismutase (SOD) and catalase (CAT). mPL (100 mg/kg body weight) administered intraperitoneally at 24 h and 1 h prior to irradiation protected jejunal crypt cells from radiation-induced apoptosis (p < 0.01). The pretreatment of mPL attenuated a radiation-induced decrease in villous height (p < 0.05), and improved jejunal crypt survival (p < 0.05). The dose reduction factor was 1.14 at 3.5 days after irradiation. Treatment with mPL prior to irradiation resulted in significantly higher (p < 0.01) levels of SOD and CAT activities, compared to those levels of irradiated control mice with vehicle treatment. These results suggest that mPL is a useful radioprotective agent capable of defending intestinal progenitor cells against total-body irradiation, at least in part through mPL antioxidative activity.
Animals ; Apoptosis ; Catalase ; Cats ; Liver ; Methanol ; Mice ; Oxidative Stress ; Rhodophyta ; Stem Cells ; Superoxide Dismutase

This study evaluated the cellular localization of cyclic AMP-responsive element binding protein-binding protein (CBP) expression in pig retinas during postnatal development. Immunohistochemistry and Western blot analysis were performed on retinal tissue from 2-day-old, 5-week-old, and 6-month-old pigs. Western blot analysis detected the expression of CBP in the retinas of 2-day-old piglets and showed that it was significantly decreased in the retinas of 5-week-old and 6-month-old pigs. Immunohistochemically, CBP was intensely immunostained in protein kinase C alpha (PKCalpha)-positive-bipolar cells, glutamine synthetase-positive Muller cells, and in ganglion cells in 2-day-old piglets. CBP was detected weakly in the inner plexiform, outer nuclear, and rod and cone layers. CBP immunoreactivity in the ganglion cell layer was decreased in the retinas of 5-week-old and 6-month-old pigs, while clear CBP expression detected in the neurite of PKCalpha-positive bipolar cells in the inner nuclear layer. In addition, CBP immunoreactivity in Muller cells and glial fibrillary acidic protein-positive glial processes was particularly noteworthy in pig retinas, but not in rat retinas. The results indicate that CBP is expressed differentially in the retinal neurons and glial cells according to growth and animal species, and may play an important role in homeostasis in Muller cells, neurite extention in bipolar cells, and signal transduction in photoreceptor cells in the porcine retina.
Animals ; Blotting, Western ; Carrier Proteins ; Ganglion Cysts ; Glutamine ; Homeostasis ; Humans ; Immunohistochemistry ; Infant ; Neurites ; Neuroglia ; Photoreceptor Cells ; Protein Kinase C-alpha ; Rats ; Retina ; Retinal Neurons ; Retinaldehyde ; Signal Transduction ; Swine

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.