Rogershovmand3209

Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels. CVDs are the leading cause of deaths worldwide. Though yoga is gaining popularity as a therapy, especially in CVD patients, there is a lack of a comprehensive review reporting its role in the management of various CVDs and their risk factors. Thus, we performed a comprehensive literature search in the PubMed/Medline electronic database. An aggregate of 603 articles published from inception were screened and 85 articles that are applicable were reported. This review suggests that yoga may play a role as an adjuvant in the management of various CVDs and their risk factors. However, many studies had a small sample size, different types and durations of the yoga interventions, and did not provide the details of mechanisms behind the improvements. Thus, further studies are warranted to explore the mechanisms of the impacts of yoga. BACKGROUND The etiology of bipolar disorder (BD) is multifactorial, involving both environmental and genetic factors. Current pharmacological treatment is associated with several side effects, which are the main reason patients discontinue treatment. Epigenetic alterations have been studied for their role in the pathophysiology of BD, as they bridge the gap between gene and environment. OBJECTIVE Evaluate the effects of histone deacetylase inhibitors on behavior and epigenetic enzymes activity in a rat model of mania induced by ouabain. METHODS Adult male rats were subjected to a single intracerebroventricular injection of ouabain (10-3 M) followed by 7 days of valproate (200 mg/kg) or sodium butyrate (600 mg/kg) administration. Locomotor and exploratory activities were evaluated in the open-field test. Histone deacetylase, DNA methyltransferase, and histone acetyltransferase activity were assessed in the frontal cortex, hippocampus, and striatum. RESULTS Ouabain induced hyperactivity in rats, which was reversed by valproate and sodium butyrate treatment. Ouabain did not alter the activity of any of the enzymes evaluated. However, valproate and sodium butyrate decreased the activity of histone deacetylase and DNA methyltransferase. Moreover, there was a positive correlation between these two enzymes. CONCLUSION These results suggest that targeting epigenetic mechanisms may play an important role in mania-like behavior management. PURPOSE The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. check details The purpose of this study is to develop a deep learning model which predicts atherosclerosis using retinal fundus images and to verify its clinical implications by conducting a retrospective cohort analysis. DESIGN Retrospective cohort study. METHODS The database at Health Promotion Center of Seoul National University Hospital (HPC-SNUH) was used. The deep learning model was trained on 15,408 images to predict carotid artery atherosclerosis, which we named the deep learning-funduscopic atherosclerosis score (DL-FAS). We constructed a retrospective cohort of participants aged 30-80 years who had completed elective health check-ups at HPC-SNUH. Using DL-FAS the as the main exposure, we followed participants for the primary outcome of death due to CVD until Dec. 31st, 2017. RESULTS For predicting carotid artery atherosclerosis among testing-set subjects, the model achieved an AUROC, AUPRC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 0.569, 0.583, 0.891, 0.404, 0.465, and 0.865 respectively. The cohort comprised of 32,227 participants, 78 CVD deaths, and 7.6-year median follow-up. Those with DL-FAS greater than 0.66 had an increased risk of CVD deaths compared to DL-FAS less then 0.33 (HR, 95%CI; 8.83, 3.16-24.7). Risk association was significant among intermediate and high Framingham risk score (FRS) subgroups. The DL-FAS improved the concordance by 0.0266 (95% CI, 0.0043-0.0489) over the FRS-only model. Relative integrated discrimination index (IDI) was 20.45% and net reclassification index (NRI) was 29.5%. CONCLUSIONS We developed a deep learning model which can predict atherosclerosis from retinal fundus images. The resulting DL-FAS was an independent predictor of CVD deaths when adjusted for FRS and added predictive value over FRS. PURPOSE Mean sensitivity (MS) derived from a standard test grid using microperimetry is a sensitive outcome measure in clinical trials investigating new treatments for degenerative retinal diseases. Here, we hypothesize that the functional decline is faster at the edge of the dense scotoma (eMS) than using overall MS. DESIGN Multicenter, international, prospective cohort study ProgStar study (NCT01977846). METHODS Stargardt disease patients (carrying at least one mutation in ABCA4) were followed over 12 months with microperimetry using a Humphrey 10-2 test grid. Custom software was developed to automatically define and selectively follow the test points directly adjacent to dense scotoma points and to calculate their mean sensitivity (eMS). RESULTS Among 361 eyes (185 patients), the mean age was 32.9 ± 15.1. At baseline, MS was 10.4 ± 5.2 dB (N=361) and the eMS was 9.3 ± 3.3 dB (N=335). The yearly progression rate of MS (1.5 ± 2.1 dB/yr) was significantly lower (β = -1.33, p less then .001) than for eMS (2.9 ± 2.9 dB/yr). There was no difference in progression rates using automated vs manual grading (β = .09, p = .461). CONCLUSIONS In Stargardt disease, macular sensitivity declines significantly faster at the edge of the dense scotoma than in the overall test grid. An automated, time-efficient approach for extracting and grading eMS is possible and appears valid. Thus, eMS offers a valuable tool and sensitive outcome measure to follow Stargardt patients in clinical trials, allowing clinical trial designs with shorter duration and/or smaller cohorts. PURPOSE To compare gradient boosting classifier (GBC) analysis of optical coherence tomography angiography (OCTA)-measured vessel density (VD) and OCT-measured tissue thickness to standard OCTA VD and OCT thickness parameters for classifying healthy eyes and eyes with early to moderate glaucoma. DESIGN Comparison of diagnostic tools. METHODS One-hundred-eight healthy eyes and 193 glaucomatous eyes with OCTA and OCT imaging of the macula and optic nerve head (ONH) were studied. Four GBCs were evaluated that combined 1) all macula VD and thickness measurements (Macula GBC), 2) all ONH VD and thickness measurements (ONH GBC), 3) all VD measurements from the macula and ONH (Vessel Density GBC), and 4) all thickness measurements from the macula and ONH (Thickness GBC). ROC curve (AUROC) analyses compared the diagnostic accuracy of GBCs to standard instrument provided parameters. A fifth GBC that combined all parameters (Full GBC ) also was investigated. RESULTS GBCs had better diagnostic accuracy than standard OCTA and OCT parameters with AUROCs ranging from 0.