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.

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.

Wound healing is a dynamic process that involves a series of molecular and cellular events aimed at replacing devitalized and missing cellular components and/or tissue layers. Recently, extracellular vesicles (EVs), naturally cell-secreted lipid membrane-bound vesicles laden with biological cargos including proteins, lipids, and nucleic acids, have drawn wide attention due to their ability to promote wound healing and tissue regeneration. However, current exploitation of EVs as therapeutic agents is limited by their low isolation yields and tedious isolation processes. To circumvent these challenges, bioinspired cell-derived nanovesicles (CDNs) that mimic EVs were obtained by shearing mesenchymal stem cells (MSCs) through membranes with different pore sizes. Physical characterisations and high-throughput proteomics confirmed that MSC-CDNs mimicked MSC-EVs. Moreover, these MSC-CDNs were efficiently uptaken by human dermal fibroblasts and demonstrated a dose-dependent activation of MAPK signalling pathway, resulting in enhancement of cell proliferation, cell migration, secretion of growth factors and extracellular matrix proteins, which all promoted tissue regeneration. Of note, MSC-CDNs enhanced angiogenesis in human dermal microvascular endothelial cells in a 3D PEG-fibrin scaffold and animal model, accelerating wound healing in vitro and in vivo. These findings suggest that MSC-CDNs could replace both whole cells and EVs in promoting wound healing and tissue regeneration.

Countertransference has become a multifaceted, multidimensional, all-encompassing clinical phenomenon. The current study aimed to understand the positive growth experiences of countertransference in six Malaysian counsellors by utilising a transcendental phenomenological design. Moustakas's eight data analysis steps were applied in the data analysis. The themes that emerged from in-depth interviews included (1) improved self-confidence, (2) skill enhancement, (3) acceptance, (4) openness, (5) self-kindness, (6) a deeper understanding of self, and (7) redefining the meaning. The findings demonstrated that the insights gained through the countertransference experiences added positive values to their professional and personal lives. This study also revealed in-depth descriptions of the positive transformations from countertransference among Malaysian counsellors. This study has significant implications for counselling practices holistically.
Counselors

Neurodegenerative diseases (NDDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD) are a heterogeneous group of disorders characterized by progressive degeneration of neurons. NDDs threaten the lives of millions of people worldwide and regretfully remain incurable. It is well accepted that dysfunction of mitochondria underlies the pathogenesis of NDDs. Dysfunction of mitochondria results in energy depletion, oxidative stress, calcium overloading, caspases activation, which dominates the neuronal death of NDDs. Therefore, mitochondria are the preferred target for intervention of NDDs. So far various mitochondria-targeting drugs have been developed and delightfully some of them demonstrate promising outcome, though there are still some obstacles such as targeting specificity, delivery capacity hindering the drugs development. In present review, we will elaborately address 1) the strategy to design mitochondria targeting drugs, 2) the rescue mechanism of respective mitochondria targeting drugs, 3) how to evaluate the therapeutic effect. Hopefully this review will provide comprehensive knowledge for understanding how to develop more effective drugs for the treatment of NDDs.

Objective: This study aimed to determine the effect of radiotherapy (RT) on the risk of herpes zoster (HZ) in patients with gynecological cancers via a nationwide population-based study. Methods: Based on patient data obtained from the National Health Insurance Research Database, 1928 gynecological cancer patients were identified with 1:1 matching for RT and non-RT cohorts by age, index date, and cancer type. Another cohort consisting of 964 noncancer individuals matched was used as normal control. The incidence of HZ was compared between cancer patients with and without RT. Age, comorbidities, cancer-related surgery and chemotherapy (CT), and cancer type were adjusted as confounders. Results: The risk of HZ in cancer patients was higher than that of non-cancer individuals (14.23 versus 8.34 per 1,000 person-years [PY], the adjusted hazard ratio [aHR]=1.38, p=0.044). In the cancer population, the incidence of HZ for the RT and non-RT cohorts was 20.55 versus 10.23 per 1,000 PY, respectively (aHR=1.68, p=0.009). Age >50 years was an independent factor for developing HZ. The 5-year actuarial incidence for patients receiving neither RT nor CT, RT alone, CT alone, and combined modalities was 5.4%, 6.9%, 3.7%, and 9.9%, respectively (p<0.001). In the RT cohort, the risk rose rapidly in the first year, becoming steady thereafter. Conclusion This population-based study showed that gynecological cancer patients receiving RT combined with CT had the highest cumulative risk of HZ. Health care professionals should be aware of the potential toxicities.

Carotid blowout syndrome (CBS) is a fatal complication of head and neck cancer. Endovascular treatment, particularly deconstructive embolization, is effective for CBS, but it might result in thromboembolic events. We report the case of a 57-year-old man with underlying recurrent head and neck cancer who had CBS. The patient received endovascular embolization of the right internal, external, and common carotid arteries. Right internal carotid artery to middle cerebral artery embolic occlusion was noted immediately after the procedure, and left-sided weakness and facial palsy were found. Ipsilateral suprabulbar cervical internal carotid artery puncture was performed under fluoroscopic guidance, and rescue suction thrombectomy was successful. The patient had no significant neurological sequela. Transcarotid intraarterial thrombectomy is a reasonable method for managing postembolization large vessel occlusion, even in the neck, after irradiation.