The metaverse is an imaginary network of parallel universes. Using this technology might liven up dull lecture halls. By expanding synchronous communication into the "metaverse," many individuals may have meaningful conversations and exchange perspectives. This research focuses on finding out how medical students in the UAE feel about the metaverse system. The conceptual model incorporates elements from the Technology Acceptance Model (TAM), including perceived value and perceived ubiquity as adoption determinants. To test the validity of the suggested framework, a survey was developed and distributed to 369 full-time students at one of the universities in the United Arab Emirates (UAE). Machine learning (ML) and structural equation modeling using partial least squares (PLS-SEM) are used for data analysis. According to the results, the extent to which users saw value in and adoption of the metaverse system was a significant factor in whether or not they intended to participate. This study was helpful since it elucidated the relative significance of various healthcare components, allowing professionals to prioritize their efforts better.

The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

Brain stroke is an intense health condition that happens when a blood clot restricts the normal flow of blood and different nutrients withinside the brain. It can cause neurologic damage, headaches and often death if not cured at a certain stage. According to the WHO, brain stroke has turned out to be the maximum rising disorder that is inflicting death because of late prognosis in both adults and elder one's. Like a “coronary heart attack” it damages or paralyzes the brain. There have been numerous predictions about cerebrovascular, however this disorder is the one that receives less consideration. Some foremost elements that contribute to the hazard of stroke are age, gender, race, and genetic history of the patient. Artificial intelligence based cognitive modeling simulates properties of biological neural networks. Artificial Neural Network offers the basis for the effectiveness of the studies. Different machine learning algorithms offer a sturdy base to discover the accuracy of the disorder. This study implements this model for higher instruction to combat pandemics in future.

Objective: To systematically evaluate the efficacy and safety of mesenchymal stem cell (MSC) therapy for patients with Multiple System Atrophy (MSA) by synthesising available clinical trial evidence and clarifying signals of disease modification. Background: MSA is a rapidly progressive and fatal neurodegenerative disorder for which no disease-modifying therapies exist. MSC therapy has emerged as a potential treatment, with mechanisms centered on neuroprotection and clinical benefit through anti-inflammatory and trophic effects rather than direct cell replacement. Methods: We systematically searched PubMed, Scopus, the Cochrane Library, and Web of Science for studies on mesenchymal stem cell (MSC) therapy in adults with probable or confirmed multiple system atrophy (MSA). Eligible studies included single-arm trials or comparisons with placebo or usual care. The primary outcome was safety and tolerability, assessed by the type and severity of adverse events. Secondary outcomes included the rate of disease progression measured by UMSARS total, Part I, and Part II scores. Results: A total of 123 participants from seven studies were included. MSCs were administered through multiple routes, and adverse events occurred in 65– 70% of participants but were mostly mild and transient. No serious MSC-related toxicity was reported. Several studies suggested slower disease progression following MSC therapy. For example, in Singer et al (2019), patients receiving high-dose MSCs showed a markedly lower rate of UMSARS total score progression compared with a matched historical control group (0.40 ± 0.59 vs 1.44 ± 1.42 points/month, p = 0.004), suggesting a possible dose-dependent effect. However, treatment effects varied across studies depending on dose, administration route, and disease stage. Conclusion: MSC therapy shows potential for disease modification in MSA by slowing neurological deterioration. The treatment was well tolerated, supporting the need for larger, definitive trials with standardised protocols and longer follow-up to confirm clinical benefit. Plain Language Summary: Multiple System Atrophy (MSA) is a rare, rapidly worsening brain disorder that affects movement, balance, and automatic body functions, and there are currently no treatments that can slow its progression. Mesenchymal stem cell (MSC) therapy has been suggested as a possible treatment because it may protect nerve cells and reduce inflammation rather than replace damaged neurons. We reviewed all clinical trials testing MSC therapy in adults with MSA to evaluate safety and possible effectiveness. Major medical databases were searched, and studies reporting side effects and changes in disease severity over time using a standard clinical rating scale were included. Seven studies with 123 patients were included. MSCs were given through different routes, including blood vessels, spinal fluid, or near the brain. Most patients experienced mild, short-term side effects, such as fever or headache, and no serious treatment-related problems were reported. Across studies, patients receiving MSC therapy showed slower worsening of symptoms than expected, particularly in earlier stages of MSA. While these findings are encouraging, the included studies were small and varied in design, which limits the strength of the conclusions. Larger, well-designed clinical trials are needed to determine whether MSC therapy can provide sustained clinical benefits for patients with MSA.

ObjectiveTo systematically evaluate the efficacy and safety of mesenchymal stem cell (MSC) therapy for patients with Multiple System Atrophy (MSA) by synthesizing evidence from clinical trials.Back...