Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objective: Danshensu, a phenylpropanoid compound, is derived from the dry root and rhizome of Danshen (Salvia miltiorrhiza), a traditional Chinese medicinal herb. Evidence suggests that danshensu protects isolated rat hearts against ischemia/reperfusion injury by activating the protein kinase B (Akt)/extracellular signal-regulated kinase (ERK) pathway or by inhibiting autophagy and apoptosis through the activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, danshensu promotes the postischemic regeneration of brain cells by upregulating the expression of brain-derived neurotrophic factor (BDNF) in the peri-infarct region. However, basic and clinical studies are needed to investigate the antidepressant effects danshensu and determine whether brain mTOR signaling and BDNF activation mediate these effects. The aforementioned need prompted us to conduct the present study. Methods: Using a C57BL/6 mouse model, we investigated the antidepressant-like effects of danshensu and the mechanisms that mediate these effects. To elucidate the mechanisms, we analyzed the roles of Akt/ERK–mTOR signaling and BDNF activation in mediating the antidepressant-like effects of danshensu. Results: Danshensu exerted its antidepressant-like effects by activating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) of Akt/ERK–mTOR signaling and promoting BDNF release. Treatment with danshensu increased the level of glutamate receptor 1 phosphorylation at the protein kinase A site. Conclusion Our study may be the first to demonstrate that the antidepressant effects of danshensu are dependent on the activation of the AMPAR–mTOR signaling pathway, are correlated with the elevation of BDNF level, and facilitate the insertion of AMPAR into the postsynaptic membrane. This study also pioneers in unveiling the potential of danshensu against depressive disorders.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Steatotic liver disease (SLD) is a common manifestation in chronic hepatitis C (CHC). Metabolic alterations in CHC are associated with metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to elucidate whether hepatitis C virus (HCV) eradication mitigates MASLD occurrence or resolution. Methods: We enrolled 5,840 CHC patients whose HCV was eradicated by direct-acting antivirals in a nationwide HCV registry. MASLD and the associated cardiometabolic risk factors (CMRFs) were evaluated at baseline and 6 months after HCV cure. Results: There were 2,147 (36.8%) patients with SLD, and 1,986 (34.0%) of them met the MASLD criteria before treatment. After treatment, HbA1c (6.0% vs. 5.9%, p<0.001) and BMI (24.8 kg/m2 vs. 24.7 kg/m2, p<0.001) decreased, whereas HDL-C (49.1 mg/dL vs. 51.9 mg/dL, p<0.001) and triglycerides (102.8 mg/dL vs. 111.9 mg/dL, p<0.001) increased significantly. The proportion of patients with SLD was 37.5% after HCV eradication, which did not change significantly compared with the pretreatment status. The percentage of the patients who had post-treatment MASLD was 34.8%, which did not differ significantly from the pretreatment status (p=0.17). Body mass index (BMI) (odds ratio [OR] 0.89; 95% confidence intervals [CI] 0.85–0.92; p<0.001) was the only factor associated with MASLD resolution. In contrast, unfavorable CMRFs, including BMI (OR 1.10; 95% CI 1.06–1.14; p<0.001) and HbA1c (OR 1.19; 95% CI 1.04–1.35; p=0.01), were independently associated with MASLD development after HCV cure. Conclusions HCV eradication mitigates MASLD in CHC patients. CMRF surveillance is mandatory for CHC patients with metabolic alterations, which are altered after HCV eradication and predict the evolution of MASLD.