RESULTS: Resveratrol significantly reduced cardiac hypertrophy and remodeling in aging hearts. In addition, resveratrol significantly ameliorated aging-induced mitochondrial dysfunction (e.g., decreased oxygen respiration and increased hydrogen peroxide emission) and mitochondria-dependent apoptotic signaling (the Bax/Bcl-2 ratio, mitochondrial permeability transition pore opening sensitivity, and cleaved caspase-3 protein levels).Resveratrol also significantly attenuated aging-induced apoptosis (determined via cleaved caspase-3 staining and TUNEL-positive myonuclei) in cardiac muscles. CONCLUSION This study demonstrates that resveratrol treatment has a beneficial effect on aging-induced cardiac remodeling by ameliorating mitochondrial dysfunction and inhibiting mitochondria-mediated apoptosis in the heart.

Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Purpose: In pancreatic cancer, therapeutic investigations targeting liver metastases could improve survival. However, the use of local treatment for oligometastasis in pancreatic cancer remains controversial. This study aimed to investigate the oncological role of local therapy in patients who underwent curative pancreatectomy and subsequently developed liver metastases. Materials and Methods: Data concerning patients who underwent curative pancreatectomy for pancreatic cancer at Severance Hospital in Seoul, South Korea between 2006 and 2018 were retrospectively reviewed. We included patients with one or two liver metastases, as confirmed on imaging. We excluded those with metastases in other organs. The patients were divided into two groups: the NT group, receiving conventional therapy without local treatment; and the LT group, receiving local treatments for liver metastases alongside standard therapy. Results: Of the 43 included patients (NT group, n=33; LT group, n=10), no significant differences were observed in overall survival (OS) [hazard ratio (HR) 0.846; 95% confidence interval (CI) 0.397–1.804; p=0.665] or post-recurrence survival (HR 0.932; 95% CI 0.437–1.985, p=0.855) between the two groups. In multivariate analysis, early recurrence within 6 months (p<0.001) and the use of 5-fluorouracil (FU)-based adjuvant chemotherapy (CTx) (p=0.011), as well as 5-FU-based CTx after liver metastasis (p=0.008) when compared with gemcitabine-based regimens, were significant predictors of poor OS. Conclusion The oncologic role of local treatment for hepatic metastasis remains controversial in patients with hepatic metastasis after radical pancreatectomy. In the era of potent chemotherapeutic regimens, further research is needed to clarify the efficacy of such regimens.

RESULTS: Resveratrol significantly reduced cardiac hypertrophy and remodeling in aging hearts. In addition, resveratrol significantly ameliorated aging-induced mitochondrial dysfunction (e.g., decreased oxygen respiration and increased hydrogen peroxide emission) and mitochondria-dependent apoptotic signaling (the Bax/Bcl-2 ratio, mitochondrial permeability transition pore opening sensitivity, and cleaved caspase-3 protein levels).Resveratrol also significantly attenuated aging-induced apoptosis (determined via cleaved caspase-3 staining and TUNEL-positive myonuclei) in cardiac muscles. CONCLUSION This study demonstrates that resveratrol treatment has a beneficial effect on aging-induced cardiac remodeling by ameliorating mitochondrial dysfunction and inhibiting mitochondria-mediated apoptosis in the heart.

Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Purpose: In pancreatic cancer, therapeutic investigations targeting liver metastases could improve survival. However, the use of local treatment for oligometastasis in pancreatic cancer remains controversial. This study aimed to investigate the oncological role of local therapy in patients who underwent curative pancreatectomy and subsequently developed liver metastases. Materials and Methods: Data concerning patients who underwent curative pancreatectomy for pancreatic cancer at Severance Hospital in Seoul, South Korea between 2006 and 2018 were retrospectively reviewed. We included patients with one or two liver metastases, as confirmed on imaging. We excluded those with metastases in other organs. The patients were divided into two groups: the NT group, receiving conventional therapy without local treatment; and the LT group, receiving local treatments for liver metastases alongside standard therapy. Results: Of the 43 included patients (NT group, n=33; LT group, n=10), no significant differences were observed in overall survival (OS) [hazard ratio (HR) 0.846; 95% confidence interval (CI) 0.397–1.804; p=0.665] or post-recurrence survival (HR 0.932; 95% CI 0.437–1.985, p=0.855) between the two groups. In multivariate analysis, early recurrence within 6 months (p<0.001) and the use of 5-fluorouracil (FU)-based adjuvant chemotherapy (CTx) (p=0.011), as well as 5-FU-based CTx after liver metastasis (p=0.008) when compared with gemcitabine-based regimens, were significant predictors of poor OS. Conclusion The oncologic role of local treatment for hepatic metastasis remains controversial in patients with hepatic metastasis after radical pancreatectomy. In the era of potent chemotherapeutic regimens, further research is needed to clarify the efficacy of such regimens.