Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Previous studies have shown that testosterone activates the GPRC6A-Duox1 axis, resulting in the production of H 2O 2 which leads to the apoptosis of keratinocytes and ultimately hair loss. Here, we elucidated a molecular mechanism by which the non-genomic action of testosterone regulates cellular redox status in androgenetic alopecia (AGA). Building upon this molecular understanding, we conducted a high-throughput screening assay of Nox inhibitors from a natural compounds library. This screening identified diterpenoid compounds, specifically Tanshinone I, Tanshinone IIA, Tanshinone IIB, and Cryptotanshinone, derived from Salviae Miltiorrhizae Radix. The IC50 values for Nox isozymes were found to be 2.6-12.9 μM for Tanshinone I, 1.9-7.2 μM for Tanshinone IIA, 5.2-11.9 μM for Tanshinone IIB, and 2.1-7.9 μM for Cryptotanshinone. Furthermore, 3D computational docking analysis confirmed the structural basis by which Tanshinone compounds inhibit Nox activity. These compounds were observed to substitute for NADPH at the π-π bond site between NADPH and FAD, leading to the suppression of Nox activity. Notably, Tanshinone I and Tanshinone IIA effectively inhibited Nox activity heightened by testosterone, consequently reducing the production of intracellular H2O2 and preventing cell apoptosis. In an animal study involving the application of testosterone to the back skin of 8-week-old C57BL/6J mice to inhibit hair growth, subsequent treatment with Tanshinone I or Tanshinone IIA alongside testosterone resulted in a substantial increase in hair follicle length compared to testosterone treatment alone. These findings underscore the potential efficacy of Tanshinone I and Tanshinone IIA as therapeutic agents for AGA by inhibiting Nox activity.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: To investigate the association between soluble suppressor of tumorigenicity 2 (sST2) levels and cardiovascular disease predictors in patients with gout. Materials and Methods: We retrospectively reviewed the medical records of patients with gout who were tested for sST2 but did not receive uric acid-lowering therapy. These patients were classified into elevated and normal sST2 groups using a cut-off of >49.6 ng/mL and >35.4 ng/mL in males and females, respectively. Correlations between clinical and laboratory variables, sST2 levels, and elevated sST2 level predictors were assessed using linear and logistic regression analyses. Results: Notably, 27 (11.3%) and 211 (88.7%) of the 238 identified patients had elevated and normal sST2 levels, respectively. Linear regression analysis revealed that male sex (β=-0.190, p=0.002), body mass index (BMI) (β=-0.184, p=0.002), white blood cell count (β=0.231, p<0.001), C-reactive protein (β=0.135, p=0.031), and fasting blood glucose (β=0.210, p<0.001) were independently associated with sST2 levels. In multivariate logistic regression analysis, male sex [odds ratio (OR) 0.112, p=0.001], BMI (OR 0.836, p=0.008), creatinine (OR 5.730, p=0.024), and fasting blood glucose (OR 1.042, p=0.002) predicted elevated sST2 levels. Patients with increased sST2 levels had a significantly higher atherosclerotic cardiovascular disease risk score and a greater proportion of high-risk Framingham Risk Score compared to the normal sST2 group (p=0.002 and p<0.001). Conclusion Patients with gout and elevated sST2 levels have a higher risk of future cardiovascular disorders, which may provide insights into risk stratification and the implementation of intervention strategies.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: To investigate the association between soluble suppressor of tumorigenicity 2 (sST2) levels and cardiovascular disease predictors in patients with gout. Materials and Methods: We retrospectively reviewed the medical records of patients with gout who were tested for sST2 but did not receive uric acid-lowering therapy. These patients were classified into elevated and normal sST2 groups using a cut-off of >49.6 ng/mL and >35.4 ng/mL in males and females, respectively. Correlations between clinical and laboratory variables, sST2 levels, and elevated sST2 level predictors were assessed using linear and logistic regression analyses. Results: Notably, 27 (11.3%) and 211 (88.7%) of the 238 identified patients had elevated and normal sST2 levels, respectively. Linear regression analysis revealed that male sex (β=-0.190, p=0.002), body mass index (BMI) (β=-0.184, p=0.002), white blood cell count (β=0.231, p<0.001), C-reactive protein (β=0.135, p=0.031), and fasting blood glucose (β=0.210, p<0.001) were independently associated with sST2 levels. In multivariate logistic regression analysis, male sex [odds ratio (OR) 0.112, p=0.001], BMI (OR 0.836, p=0.008), creatinine (OR 5.730, p=0.024), and fasting blood glucose (OR 1.042, p=0.002) predicted elevated sST2 levels. Patients with increased sST2 levels had a significantly higher atherosclerotic cardiovascular disease risk score and a greater proportion of high-risk Framingham Risk Score compared to the normal sST2 group (p=0.002 and p<0.001). Conclusion Patients with gout and elevated sST2 levels have a higher risk of future cardiovascular disorders, which may provide insights into risk stratification and the implementation of intervention strategies.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: To investigate the association between soluble suppressor of tumorigenicity 2 (sST2) levels and cardiovascular disease predictors in patients with gout. Materials and Methods: We retrospectively reviewed the medical records of patients with gout who were tested for sST2 but did not receive uric acid-lowering therapy. These patients were classified into elevated and normal sST2 groups using a cut-off of >49.6 ng/mL and >35.4 ng/mL in males and females, respectively. Correlations between clinical and laboratory variables, sST2 levels, and elevated sST2 level predictors were assessed using linear and logistic regression analyses. Results: Notably, 27 (11.3%) and 211 (88.7%) of the 238 identified patients had elevated and normal sST2 levels, respectively. Linear regression analysis revealed that male sex (β=-0.190, p=0.002), body mass index (BMI) (β=-0.184, p=0.002), white blood cell count (β=0.231, p<0.001), C-reactive protein (β=0.135, p=0.031), and fasting blood glucose (β=0.210, p<0.001) were independently associated with sST2 levels. In multivariate logistic regression analysis, male sex [odds ratio (OR) 0.112, p=0.001], BMI (OR 0.836, p=0.008), creatinine (OR 5.730, p=0.024), and fasting blood glucose (OR 1.042, p=0.002) predicted elevated sST2 levels. Patients with increased sST2 levels had a significantly higher atherosclerotic cardiovascular disease risk score and a greater proportion of high-risk Framingham Risk Score compared to the normal sST2 group (p=0.002 and p<0.001). Conclusion Patients with gout and elevated sST2 levels have a higher risk of future cardiovascular disorders, which may provide insights into risk stratification and the implementation of intervention strategies.