Exertional headache is one entity of Valsalva maneuver-related headache syndrome. It is usually idiopathic, but has occasionally been reported to be associated with secondary causes. However, central nervous system infection has not been mentioned before. We encountered a young man who suffered an isolated exertional headache and was found to have an active varicella-zoster virus central nervous system infection without typical intracranial hypertension or outfl ow obstruction. Intracranial vasoconstriction was detected during headache when the patient performed acute lifting or heavy exertion. The fi ndings in this patient suggest a specifi c relationship between varicella-zoster virus-related vasculopathy and exertional headache from other Valsalva maneuver-related headache syndrome

The bone remodeling process in response to orthodontic forces requires the activity of osteoclasts to allow teeth to move in the direction of the force applied. Receptor activator of nuclear factor-κB ligand (RANKL) is essential for this process although its cellular source in response to orthodontic forces has not been determined. Orthodontic tooth movement is considered to be an aseptic inflammatory process that is stimulated by leukocytes including T and B lymphocytes which are presumed to stimulate bone resorption. We determined whether periodontal ligament and bone lining cells were an essential source of RANKL by tamoxifen induced deletion of RANKL in which Cre recombinase was driven by a 3.2 kb reporter element of the Col1α1 gene in experimental mice (Col1α1.CreER.RANKL) and compared results with littermate controls (Col1α1.CreER.RANKL). By examination of Col1α1.CreER.ROSA26 reporter mice we showed tissue specificity of tamoxifen induced Cre recombinase predominantly in the periodontal ligament and bone lining cells. Surprisingly we found that most of the orthodontic tooth movement and formation of osteoclasts was blocked in the experimental mice, which also had a reduced periodontal ligament space. Thus, we demonstrate for the first time that RANKL produced by periodontal ligament and bone lining cells provide the major driving force for tooth movement and osteoclastogenesis in response to orthodontic forces.
Animals ; Bone Remodeling ; physiology ; Mice ; Mice, Transgenic ; Osteoclasts ; physiology ; Periodontal Ligament ; metabolism ; RANK Ligand ; metabolism ; Tamoxifen ; pharmacology ; Tooth Movement Techniques

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.