Effectiveness of Yoga in Modulating Markers of Immunity and Inflammation: A Systematic Review and Meta-Analysis.

Chronic inflammation is central to the pathogenesis of many chronic inflammatory conditions. This review aims to analyze whether the practice of yoga, or yogic meditation and breathing, has any effect on the levels of inflammatory cytokines and other inflammatory markers in patients with various chronic inflammatory diseases such as rheumatoid arthritis, neoplastic disorders, and asthma, as well as in healthy subjects, compared to usual care or sham interventions. A comprehensive search of databases (PubMed, CENTRAL, Embase, and CINAHL) was performed. Randomized controlled trials (RCTs) that evaluated the effects of yoga as an intervention on inflammatory markers were analyzed. A total of 26 studies were included. Only two studies had a low risk of bias (RoB); 24 other studies had a high RoB. Most studies (n=24) reported a favorable outcome with yoga, irrespective of the type of yoga used, the condition studied, and the duration of the intervention. The commonly reported inflammatory markers included IL-6 (n=17), tumor necrosis factor-alpha (TNF-a) (n=13), and C-reactive protein (CRP) (n=10). Most studies showed a significant reduction in inflammatory markers in the yoga group (YG) compared to the control group (CG). Few studies also showed significant improvement in markers of cellular immunity (interferon gamma (IFN-g), IL-10, and transforming growth factor-beta (TGF-b); n=2 each) and improved mucosal defense (IgA, IL-6, and IL-2; n=2 each). A meta-analysis of IL-6, TNF-a, and CRP showed yoga had a favorable effect on the levels of these markers, but it was not statistically significant. Current evidence suggests that yoga can be a complementary intervention for various chronic inflammatory conditions. However, the quality of the evidence is poor, along with considerable heterogeneity. In the future, investigators should describe the intervention better, with a uniform assortment of outcome measures and treatment conditions, to generate high-quality evidence.

Smartphone-Based Telestroke Vs"Stroke Physician" led Acute Stroke Management (SMART INDIA): A Protocol for a Cluster-Randomized Trial.

Background: One of the major challenges is to deliver adequate health care in rural India, where more than two-thirds of India's population lives. There is a severe shortage of specialists in rural areas with one of the world's lowest physician/population ratios. There is only one neurologist per 1.25 million population. Stroke rehabilitation is virtually nonexistent in most district hospitals. Two innovative solutions include training physicians in district hospitals to diagnose and manage acute stroke ('Stroke physician model') and using a low-cost Telestroke model. We will be assessing the efficacy of these models through a cluster-randomized trial with a standard of care database maintained simultaneously in tertiary nodal centers with neurologists. Methods: SMART INDIA is a multicenter, open-label cluster-randomized trial with the hospital as a unit of randomization. The study will include district hospitals from the different states of India. We plan to enroll 22 district hospitals where a general physician manages the emergency without the services of a neurologist. These units (hospitals) will be randomized into either of two interventions using computer-generated random sequences with allocation concealment. Blinding of patients and clinicians will not be possible. The outcome assessment will be conducted by the blinded central adjudication team. The study includes 12 expert centers involved in the Telestroke arm by providing neurologists and telerehabilitation round the clock for attending calls. These centers will also be the training hub for "stroke physicians" where they will be given intensive short-term training for the management of acute stroke. There will be a preintervention data collection (1 month), followed by the intervention model implementation (3 months). Outcomes: The primary outcome will be the composite score (percentage) of performance of acute stroke care bundle assessed at 1 and 3 months after the intervention. The highest score (100%) will be achieved if all the eligible patients receive the standard stroke care bundle. The study will have an open-label extension for 3 more months. Conclusion: SMART INDIA assesses whether the low-cost Telestroke model is superior to the stroke physician model in achieving acute stroke care delivery. The results of this study can be utilized in national programs for stroke and can be a role model for stroke care delivery in low- and middle-Income countries. (CTRI/2021/11/038196).

Predictors of Infarction in Tuberculous Meningitis in Indian Patients.

BACKGROUND: Stroke is a devastating complication of tuberculous meningitis (TBM) and is an important determinant of its outcome. We propose a model which would help to predict development of infarction or cerebrovascular events in patients of TBM. METHODS: A prospective study with n=129 patients of TBM were evaluated for predictors and outcomes of stroke. A diagnostic grid was formulated with clinical, laboratory and radiology as parameters to predict the vascular outcomes. All patients were followed up for mortality and disability on the basis of modified rankin score (mRS). MRI & CSF cytokines TNF-alpha, IFN- gamma & IL-6,8, 10 were measured at baseline and 3 months. The diagnosis of TBM included definite, probable & possible types and stage I & II with early and late onset of symptoms respectively. RESULTS: The mortality was 16.2% and 19.4% of all patients developed stroke. The mean GCS, barthel index and mRS at admission was 57.03± 9.5,10.2±2.3 & 3.3±0.03 respectively mild to moderate infection and functional limitation. Barthel index (BI) happened to be a strong predictor [F=32.6, p=0.001, t=15.5, ßeta coefficient =0.002] followed by biomarker TNF-α [F=18.9, p=0.02, t= -2.07, ßeta coefficient=-0.04]. N=25 patients developed stroke with TNF-α, IL-6, IFN -γ showing statistically significant increase in all the stroke affected TBM (95% CI; 4.5 to 1.2; p=0.003). At 3 months, it was observed that mRS was statistically significant between stage I & II (95% CI; 5.4 to 2.1; p=0.04). CONCLUSIONS: Our data revealed that 19.4% patients developed vascular events during the hospital stay or follow up. We recruited late onset TBM as compared to early onset. BI, TNF-α, IL6 are most potent predictors of stroke post TBM.

Vascular Endothelial Growth Factor as Predictive Biomarker for Stroke Severity and Outcome; An Evaluation of a New Clinical Module in Acute Ischemic Stroke.

BACKGROUND: The need to study prognosis after incidence of acute ischemic stroke (AIS) has fueled researchers to identify predictors apart from neurological, functional, or disability measures. The purpose of this study was to test and validate a newly developed clinico-biomarker assessment module in AIS and also to investigate the role of serum vascular endothelial growth factor (VEGF) after AIS. MATERIALS AND METHODS: A randomized controlled study with sample size of 250 patients suffering from AIS within 2 weeks of the index event were conducted and followed up for a period of three months. Age, gender, stroke subtype, previous stroke history, dysarthria, stroke localization, wakeup strokes, and Glasgow Coma Scale (GCS) were dichotomized as present or absent using the National Institute of Health Stroke Scale (NIHSS) which consists of four subcategories. The additional serum VEGF was scored between 1 and 4 (0-200 = 1, 200-300 = 2, 300-400 = 3, and 400-500 = 4). All these were summed under a clinical biomarker (CB) module with highest score of 30. RESULTS: The mean VEGF in 125 patients was 378.4 + 98.9 pg/ml, indicating a moderately high increase with a score of 3 on CB module. Multiple regression analysis revealed that the CB model was fit to predict prognosis and severity [R2 = 0.86, F (23.4, 6);P = 0.001], with NIHSS subscore, prestroke status, and VEGF being very strong predictors. When only the clinical module was tested on all 250 patients, it was found that the NIHSS subscore, time to stroke onset and prestroke functional status were the most common [R2 = 0.79; F (45,9);P = 0.005]. CONCLUSION: This study demonstrates that VEGF is highly upregulated in AIS with severe disability as compared to healthy controls. This biomarker is a strong predictor of severity and functionality when combined with clinical variables three months post the ishemic event.