Background/Aims: The prognostic value of serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) for patients with borderline resectable or locally advanced pancreatic cancer who undergo conversion surgery or continue chemotherapy without surgery has not been well-established. Methods: A retrospective analysis of patients with pancreatic ductal adenocarcinoma was conducted at Seoul National University Bundang Hospital between March 2013 and February 2022.Patients underwent PET/CT at baseline and subsequent radiologic evaluations following chemotherapy. Changes in the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, and total lesion glycolysis were analyzed.Based on their treatment regimens, patients were stratified into the conversion surgery group or nonconversion surgery group. Survival outcomes and various clinical factors were assessed. Results: Among 121 patients, 52 underwent conversion surgery, and 69 continued to receive chemotherapy without surgery. A significant reduction in the SUVmax was correlated with prolonged recurrence-free survival and overall survival in the conversion surgery group. Confirmation of a pathologic response indicated a significant association between reductions in the SUVmax and positive outcomes. Reductions in the metabolic tumor volume and total lesion glycolysis were associated with improved progression-free survival and overall survival in the nonconversion surgery group. Conclusions Serial PET/CT scans demonstrated prognostic value in pancreatic ductal adenocarcinoma patients, revealing distinct correlations in the conversion surgery group and nonconversion surgery group.

Background/Aims: The prognostic value of serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) for patients with borderline resectable or locally advanced pancreatic cancer who undergo conversion surgery or continue chemotherapy without surgery has not been well-established. Methods: A retrospective analysis of patients with pancreatic ductal adenocarcinoma was conducted at Seoul National University Bundang Hospital between March 2013 and February 2022.Patients underwent PET/CT at baseline and subsequent radiologic evaluations following chemotherapy. Changes in the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, and total lesion glycolysis were analyzed.Based on their treatment regimens, patients were stratified into the conversion surgery group or nonconversion surgery group. Survival outcomes and various clinical factors were assessed. Results: Among 121 patients, 52 underwent conversion surgery, and 69 continued to receive chemotherapy without surgery. A significant reduction in the SUVmax was correlated with prolonged recurrence-free survival and overall survival in the conversion surgery group. Confirmation of a pathologic response indicated a significant association between reductions in the SUVmax and positive outcomes. Reductions in the metabolic tumor volume and total lesion glycolysis were associated with improved progression-free survival and overall survival in the nonconversion surgery group. Conclusions Serial PET/CT scans demonstrated prognostic value in pancreatic ductal adenocarcinoma patients, revealing distinct correlations in the conversion surgery group and nonconversion surgery group.

Background/Aims: The prognostic value of serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) for patients with borderline resectable or locally advanced pancreatic cancer who undergo conversion surgery or continue chemotherapy without surgery has not been well-established. Methods: A retrospective analysis of patients with pancreatic ductal adenocarcinoma was conducted at Seoul National University Bundang Hospital between March 2013 and February 2022.Patients underwent PET/CT at baseline and subsequent radiologic evaluations following chemotherapy. Changes in the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, and total lesion glycolysis were analyzed.Based on their treatment regimens, patients were stratified into the conversion surgery group or nonconversion surgery group. Survival outcomes and various clinical factors were assessed. Results: Among 121 patients, 52 underwent conversion surgery, and 69 continued to receive chemotherapy without surgery. A significant reduction in the SUVmax was correlated with prolonged recurrence-free survival and overall survival in the conversion surgery group. Confirmation of a pathologic response indicated a significant association between reductions in the SUVmax and positive outcomes. Reductions in the metabolic tumor volume and total lesion glycolysis were associated with improved progression-free survival and overall survival in the nonconversion surgery group. Conclusions Serial PET/CT scans demonstrated prognostic value in pancreatic ductal adenocarcinoma patients, revealing distinct correlations in the conversion surgery group and nonconversion surgery group.

Background/Aims: The prognostic value of serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) for patients with borderline resectable or locally advanced pancreatic cancer who undergo conversion surgery or continue chemotherapy without surgery has not been well-established. Methods: A retrospective analysis of patients with pancreatic ductal adenocarcinoma was conducted at Seoul National University Bundang Hospital between March 2013 and February 2022.Patients underwent PET/CT at baseline and subsequent radiologic evaluations following chemotherapy. Changes in the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, and total lesion glycolysis were analyzed.Based on their treatment regimens, patients were stratified into the conversion surgery group or nonconversion surgery group. Survival outcomes and various clinical factors were assessed. Results: Among 121 patients, 52 underwent conversion surgery, and 69 continued to receive chemotherapy without surgery. A significant reduction in the SUVmax was correlated with prolonged recurrence-free survival and overall survival in the conversion surgery group. Confirmation of a pathologic response indicated a significant association between reductions in the SUVmax and positive outcomes. Reductions in the metabolic tumor volume and total lesion glycolysis were associated with improved progression-free survival and overall survival in the nonconversion surgery group. Conclusions Serial PET/CT scans demonstrated prognostic value in pancreatic ductal adenocarcinoma patients, revealing distinct correlations in the conversion surgery group and nonconversion surgery group.

Background: While enteral feeding intolerance (EFI) is associated with worse clinical outcomes in critically ill patients, the relationship between the number of days of EFI and mortality outcomes remains unclear. Methods: We retrospectively analyzed adult patients admitted to the medical intensive care unit (ICU) with septic shock at a tertiary referral center. EFI was defined as the presence of vomiting, abdominal distension, pain, diarrhea, or radiographic evidence of ileus. EFI status was assessed daily, and we evaluated the prognostic impact of total number of EFI days during the first 3 days of enteral feeding on clinical outcomes. Results: A total of 94 patients were included in the analysis, with 77 (81.9%) experiencing EFI. During the first 3 days of enteral feeding, 25 patients (26.6%) experienced EFI for 1 day, 22 patients (23.4%) experienced EFI for 2 days, and 30 patients (31.9%) experienced EFI for all 3 days. The total number of EFI days was identified as an independent risk factor of 90-day mortality (adjusted hazard ratio, 1.400; 95% CI 1.021–1.919). Higher total EFI days was significantly associated with increased ICU mortality (P for trend=0.036), in-hospital mortality (P for trend=0.007), 30-day mortality (P for trend=0.004), and 90-day mortality (P for trend=0.006). Conclusions An increase in the total number of EFI days was significantly associated with mortality outcomes in patients with septic shock, suggesting that EFI may serve as a useful indicator for predicting outcomes in this population.

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.