Background: and Purpose Cerebral venous flow alterations potentially contribute to age-related white matter changes, but their role in small vessel disease has not been investigated. Methods: This study included 297 patients with hypertensive intracerebral hemorrhages (ICH) who underwent magnetic resonance imaging. Cerebral venous reflux (CVR) was defined as the presence of abnormal signal intensity in the dural venous sinuses or internal jugular vein on time-of-flight angiography. We investigated the association between CVR, dilated perivascular spaces (PVS), and recurrent stroke risk. Results: CVR was observed in 38 (12.8%) patients. Compared to patients without CVR those with CVR were more likely to have high grade (>20 in the number) dilated PVS in the basal ganglia (60.5% vs. 35.1%; adjusted odds ratio [aOR], 2.64; 95% confidence interval [CI], 1.25 to 5.60; P=0.011) and large PVS (>3 mm in diameter) (50.0% vs. 18.5%; aOR, 3.87; 95% CI, 1.85 to 8.09; P<0.001). During a median follow-up of 18 months, patients with CVR had a higher recurrent stroke rate (13.6%/year vs. 6.2%/year; aOR, 2.53; 95% CI, 1.09 to 5.84; P=0.03) than those without CVR. Conclusions CVR may contribute to the formation of enlarged PVS and increase the risk of recurrent stroke in patients with hypertensive ICH.

No abstract available.
Head and Neck Neoplasms* ; Head* ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors* ; Ischemic Attack, Transient* ; Radiotherapy* ; Stroke*

BACKGROUND: Hair follicles are among a handful of organs that exhibit immune privilege. Dysfunction of the hair follicle immune system underlies the development of inflammatory diseases, such as alopecia areata. METHODS: Quantitative reverse transcription PCR and immunostaining was used to confirm the expression of major histocompatibility complex class I in human dermal papilla cells. Through transcriptomic analyses of human keratinocyte stem cells, major histocompatibility complex class I was identified as differentially expressed genes. Organ culture and patch assay were performed to assess the ability of WNT3a conditioned media to rescue immune privilege. Lastly, CD8? T cells were detected near the hair bulb in alopecia areata patients through immunohistochemistry. RESULTS: Inflammatory factors such as tumor necrosis factor alpha and interferon gamma were verified to induce the expression of major histocompatibility complex class I proteins in dermal papilla cells. Additionally, loss of immune privilege of hair follicles was rescued following treatment with conditioned media from outer root sheath cells. Transcriptomic analyses found 58 up-regulated genes and 183 down-regulated genes related in MHC class I? cells. Using newborn hair patch assay, we demonstrated that WNT3a conditioned media with epidermal growth factor can restore hair growth. In alopecia areata patients, CD8? T cells were increased during the transition from mid-anagen to late catagen. CONCLUSION Identification of mechanisms governing epithelial and mesenchymal interactions of the hair follicle facilitates an improved understanding of the regulation of hair follicle immune privilege.

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.