Unhealthy alcohol use, smoking, and depressive symptoms are risk factors for cardiovascular disease (CVD). Little is known about their co-occurrence – termed a syndemic, defined as the synergistic effect of two or more conditions-on CVD risk in people with HIV (PWH). H-Cys(Trt)-OH nmr We used data from 5621 CVD-free participants (51% PWH) in the Veteran’s Aging Cohort Study-8, a prospective, observational study of veterans followed from 2002 to 2014 to assess the association between this syndemic and incident CVD by HIV status. Diagnostic codes identified cases of CVD (acute myocardial infarction, stroke, heart failure, peripheral artery disease, and coronary revascularization). Validated measures of alcohol use, smoking, and depressive symptoms were used. Baseline number of syndemic conditions was categorized (0, 1, ≥ 2 conditions). Multivariable Cox Proportional Hazards regressions estimated risk of the syndemic (≥ 2 conditions) on incident CVD by HIV-status. There were 1149 cases of incident CVD (52% PWH) during the follow-up (median 10.1 years). Of the total sample, 64% met our syndemic definition. The syndemic was associated with greater risk for incident CVD among PWH (Hazard Ratio [HR] 1.87 [1.47-2.38], p  less then  0.001) and HIV-negative veterans (HR 1.70 [1.35-2.13], p  less then  0.001), compared to HIV-negative with zero conditions. Among those with the syndemic, CVD risk was not statistically significantly higher among PWH vs. HIV-negative (HR 1.10 [0.89, 1.37], p = .38). Given the high prevalence of this syndemic combined with excess risk of CVD, these findings support linked-screening and treatment efforts.We previously reported that penta-acetyl geniposide ((Ac)5GP, an active derivative of geniposide) showed anti-arthritic effect on adjuvant-induced arthritis (AIA) rats by promoting the apoptosis of AIA fibroblast-like synoviocyte (FLS). This study aimed to demonstrate the effects of (Ac)5GP on migration, invasion, and inflammation of TNF-α-stimulated rheumatoid arthritis (RA) FLS (MH7A cell) and to explore the involved mechanisms. MTT assay was used to determine the applied non-cytotoxic doses of (Ac)5GP (12.5, 25, 50 μM) in vitro. Results of wound-healing, transwell, and phalloidin staining assays indicated that (Ac)5GP reduced the migration, invasion, and F-actin cytoskeletal reorganization of TNF-α-stimulated MH7A. Results of ELISA and western blot assays confirmed that (Ac)5GP reduced TNF-α-induced production of pro-inflammatory cytokines (like IL-1β, IL-6, IL-8) and matrix metalloproteinases (MMPs, such as MMP-2 and MMP-9). Moreover, (Ac)5GP inhibited TNF-α-induced activation of Wnt/β-catenin pathway, evidenced by reducing the protein levels of Wnt1, p-GSK-3β (Ser9), and β-catenin and preventing β-catenin nuclear translocation. Importantly, the combination of XAV939 (an inhibitor of Wnt/β-catenin) promoted the actions of (Ac)5GP on TNF-α-induced migration, invasion, and inflammation, further revealing the involvement of Wnt/β-catenin pathway underlying the therapeutic effects of (Ac)5GP on TNF-α-stimulated MH7A. In vivo, (Ac)5GP relieved the progression and severity of rat collagen-induced arthritis, related to reducing the levels of IL-1β, IL-6, IL-8, MMP-2, and MMP-9 as well as inhibiting Wnt/β-catenin pathway in synovial tissues. Collectively, (Ac)5GP could suppress TNF-α-induced migration, invasion, and inflammation in RA FLS involving Wnt/β-catenin pathway and (Ac)5GP might be as a candidate agent for RA treatment.This study examined the impact of a state policy requiring that any school with a habitual truancy rate of 8% or higher to be trained in Tier 1 school-wide Positive Behavioral Interventions and Supports (SW-PBIS). A regression discontinuity (RD) design was used to examine how the schools’ mandate status related to SW-PBIS training as well as student suspensions, truancy, and achievement in 410 public middle and high schools, of which 261 were affected by the mandate. We further examined the growth trajectories (i.e., improvement) of implementation fidelity over time using growth mixture modeling (GMM). Contrary to the intent of the policy to improve student outcomes, the RD results suggested that the mandate did not significantly impact reading and math achievement, truancy rates, or SW-PBIS training in 2010-2011 through 2013-2014. Mandated schools had higher suspension rates in 2010-2011 through 2013-2014 than the non-mandated schools; however, these differences in the suspension rates appear to have persisted from years prior to the mandate. Descriptive analyses suggested that mandated schools had statistically significantly higher rates of training, and the GMM analyses on the fidelity data indicated that mandated schools were significantly more likely to be in an improving implementation growth trajectory over time. Taken together, results suggested that the policy showed some promise for improving SW-PBIS training and fidelity over time, but it had little to no impact on student outcomes.

Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation.

Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using sixfold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively.

On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11s per knee.

The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.

The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.