The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4,409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root-mean-square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (p  less then  5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males.Fiscal tools are recommended as a part of a comprehensive approach to diet-related disease prevention, however, widespread adoption has been hampered by political and economic resistance. The aim of this study was to support an advocacy coalition in the Solomon Islands with evidence-based consideration of the development and implementation of a tax on sugar-sweetened beverages (SSBs), sensitive to local contextual factors and constraints. In 2017-19, we conducted a prospective policy analysis, including document analysis and qualitative interviews with key stakeholders to elicit policy-relevant data, a quantitative analysis to frame the policy problem and examine appropriate implementation mechanisms, and economic modelling to outline the potential benefits associated with different proposed policy solutions. Applying an action-oriented approach to prospective policy analysis enabled us as researchers to engage in the needs of a 'pro-SSB tax' advocacy coalition and prepare them to exploit policy opportunities created by the meeting of policy 'streams'. Our analysis demonstrated that SSBs were being consumed in relatively large amounts, especially by children, and that there were likely to be substantial health and economic benefits associated with a SSB tax. Increasing fiscal uncertainty for key sectors had created an environment prime for the advocacy coalition to pursue the adoption of an SSB tax. However, we found that policymakers face a number of practical challenges in securing effective adoption and implementation of global food policy recommendations, including that it is difficult to demonstrate the potential efficacy of interventions in the local context. The development of a policy package based on local factors resulted in a policy product that was likely to be more persuasive for local policymakers and policy leaders. We suggest that there is substantial scope for researchers to more effectively engage with policy advocates to inform and shape real-world health policy improvements.
  Although recurrence and de novo formation of arteriovenous malformations (AVMs) have been reported following complete resection, the occurrence of hemorrhage in the same location of an AVM with no detectable lesion (lesion-negative hemorrhage) has not been described after microsurgery.
 
  To characterize the incidence and properties of lesion-negative hemorrhage following complete microsurgical resection.
 
  A prospectively maintained registry of AVM patients seen at our institution between 1990 and 2017 was used. Microsurgically treated patients were selected, and the incidence of a lesion-negative hemorrhage was calculated and described with a Kaplan-Meier curve. Baseline characteristics as well as functional outcome at last follow-up were compared between patients with and without a lesion-negative hemorrhage.
 
  From a total of 789 AVM patients, 619 (79%) were treated, and 210 out of 619 patients (34%) underwent microsurgery with or without preoperative embolization or radiosurgery. The microsurgically treated cohort was followed up for a mean of 6.1± 3.0 yr after surgery with 5 (2.4%) patients experiencing postresection lesion-negative hemorrhage (3.9 per 1000 person-years) at an average of 8.6± 9.0 yr following surgery. Follow-up angiograms after hemorrhage (up to 2 mo posthemorrhage) confirmed the absence of a recurrent or de novo AVM in all cases. All patients with a lesion-negative hemorrhage initially presented with rupture before resection (Fisher P=.066; log-rank P=.057). The occurrence of a lesion-negative hemorrhage was significantly associated with worse modified Rankin scale scores at last follow-up (P=.031).
 
  A lesion-negative hemorrhage can occur following complete microsurgical resection in up to 2.4% of patients.  https://www.selleckchem.com/products/ad-5584.html  Exploration of possible underlying causes is warranted.
 A lesion-negative hemorrhage can occur following complete microsurgical resection in up to 2.4% of patients. Exploration of possible underlying causes is warranted.
  Molecular characterization of glioma has implications for prognosis, treatment planning, and prediction of treatment response. Current histopathology is limited by intratumoral heterogeneity and variability in detection methods. Advances in computational techniques have led to interest in mining quantitative imaging features to noninvasively detect genetic mutations.
 
  To evaluate the diagnostic accuracy of machine learning (ML) models in molecular subtyping gliomas on preoperative magnetic resonance imaging (MRI).
 
  A systematic search was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify studies up to April 1, 2020. Methodological quality of studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS)-2. Diagnostic performance estimates were obtained using a bivariate model and heterogeneity was explored using metaregression.
 
  Forty-four original articles were included. The pooled sensitivity and specificity for predicting isocitrate dehydrogenase (IDH) mutation in training datasets were 0.