AIMSThe purpose of this study was to evaluate whether yoga training in addition to standard medical therapy can improve cardiac function and reduce N terminal pro B-type natriuretic peptide (NT pro BNP) in heart failure (HF).METHODS130 patients were recruited and randomized into two groups: Control Group (CG) (n = 65), Yoga Group (YG). In YG, 44 patients and in CG, 48 patients completed the study. Cardiac function using left ventricular ejection fraction (LVEF), myocardial performance index (Tei index), and NT pro BNP, a biomarker of HF, was assessed at baseline and after 12 weeks.RESULTImprovement in LVEF, Tei index, and NT pro BNP were statistically significant in both the groups. Furthermore, when the changes in before and after 12 weeks were in percentage, LVEF increased 36.88% in the YG and 16.9% in the CG, Tei index was reduced 27.87% in the YG and 2.79% in the CG, NT pro BNP was reduced 63.75% in the YG and 10.77% in the CG. The between group comparisons from pre to post 12 weeks were significant for YG improvements (LVEF, P < 0.01, Tei index, P < 0.01, NT pro BNP, P < 0.01).CONCLUSIONThese results indicate that the addition of yoga therapy to standard medical therapy for HF patients has a markedly better effect on cardiac function and reduced myocardial stress measured using NT pro BNP in patients with stable HF.

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The diagnosis of brain tumours has sparked attention in several research fields recently. Since the human body has anatomical structure by nature, finding brain tumours is an extremely laborious and time-consuming task. Cells develop quickly and uncontrollably, which causes brain tumours. It may cause death if not addressed in the beginning stages. Although there have been many substantial efforts and encouraging results in this field, precise segmentation and classification remain difficult tasks. Because of the variability in tumour location, shape, and size, detecting brain tumours is a significant difficulty. One of the most crucial problems with artificial intelligence systems is medical diagnostics using image processing and machine learning. Magnetic resonance imaging (MRI) is one of the technologies frequently used to find tumours in the brain (MRI). It provides crucial details that are employed in the process of carefully scanning the internal organisation of the human body. The variety and intricacy of brain tumours make it difficult to classify MR images. Sigma sifting, versatile limit, and detection locale are a portion of the cycles in the recommended technique for finding a brain cancer in MR pictures.