Yoga as an Add-on Therapy in Parkinson's Disease: A Single Group Open-label Trial.

OBJECTIVE: We aimed to evaluate the effect of yoga on motor and non-motor symptoms and cortical excitability in patients with Parkinson's disease (PD). METHODS: We prospectively evaluated 17 patients with PD at baseline, after one month of conventional care, and after one month of supervised yoga sessions. The motor and non-motor symptoms were evaluated using the Unified Parkinson's disease Rating Scale (motor part III), Hoehn and Yahr stage, Montreal Cognitive Assessment, Hamilton depression rating scale, Hamilton anxiety rating scale, non-motor symptoms questionnaire and World Health Organization quality of life questionnaire. Transcranial magnetic stimulation was used to record resting motor threshold, central motor conduction time, ipsilateral silent period (iSP), contralateral silent period (cSP), short interval intracortical inhibition (SICI), and intracortical facilitation. RESULTS: The mean age of the patients was 55.5 ± 10.8 years, with a mean duration of illness of 4.0 ± 2.5 years. The postural stability of the patients significantly improved following yoga (0.59 ± 0.5 to 0.18 ± 0.4, p = 0.039). There was a significant reduction in the cSP from baseline (138.07 ± 27.5 ms) to 4 weeks of yoga therapy (116.94 ± 18.2 ms, p = 0.004). In addition, a significant reduction in SICI was observed after four weeks of yoga therapy (0.22 ± 0.10) to (0.46 ± 0.23), p = 0.004). CONCLUSION: Yoga intervention can significantly improve postural stability in patients with PD. A significant reduction of cSP and SICI suggests a reduction in GABAergic neurotransmission following yoga therapy that may underlie the improvement observed in postural stability. CLINICALTRIALSGOV IDENTIFIER: CTRI/2019/02/017564.

Synergistic Analysis of Lung Cancer's Impact on Cardiovascular Disease Using ML-Based Techniques.

Cancer patients are known to have a higher likelihood of developing Cardiovascular Disease (CVD) compared to non-cancer individuals. Although various types of cancer can contribute to the onset of CVD, lung cancer is inherently linked with increased susceptibility. To bridge this hypothesis, we propose a Lung cancer detection and Cardiovascular Disease Prediction (LCDP) system through lung Computed Tomography (CT) scan images. The lung cancer detection module of the LCDP system utilizes Transfer Learning (TL) with AdaDenseNet for classification. It employs the improvised Proximity-based Synthetic Minority Over-sampling Technique (Prox-SMOTE), improving accuracy. In the CVD prediction module, the feature extraction was performed using the VGG-16 model, followed by classification using a Support Vector Machine (SVM) classifier. The impact and interdependence of lung cancer on CVD were evident in our evaluation, with high accuracies of 98.28% for lung cancer detection and 91.62% for CVD prediction.