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Abstract

This study examines how the public health legislation and COVID-19 pandemic responses in Sweden and India were influenced by the differences in their healthcare systems and legal frameworks. Sweden avoided rigorous lockdowns in favour of voluntary measures, public confidence, and individual accountability in its decentralized and universal healthcare system. Under the Communicable Diseases Act of 2004, the Swedish Public Health Agency took the lead in leading the nation s response, emphasizing the protection of high-risk populations while preserving economic stability. On the other hand, the Epidemic Diseases Act of 1897 and the Disaster Management Act of 2005 imposed strict lockdowns and enforcement mechanisms on India heterogeneous healthcare system, which is marked by notable regional differences. To contain the infection, India used intensive testing, contact tracing, and digital technologies like the Aarogya Setu app. Both nations distinct methods were also evident in their vaccination efforts. Following EU rules, Sweden gave priority to high-risk groups and rolled out the immunization program with strong public trust. Despite obstacles like vaccine scepticism and supply chain problems, India initiated a huge immunization effort employing both internationally and domestically produced vaccinations.

This comparative analysis looks at the results and difficulties that both nations faced. Sweden approach led to higher death rates per capita but less economic disturbance, while India stringent efforts caused major social and economic unrest, particularly during the second wave of the virus, but initially controlled the outbreak. To effectively manage future health crises, the analysis emphasizes the significance of adaptable, context-specific public health measures and a strong healthcare infrastructure. The results give policymakers around the world important insights into the efficacy of various public health strategies during a global health emergency.

Abstract

This research paper provides extensive research of the recent advances and significant contributions in the field of computational neuroscience. The study encompasses the integration of mathematical models, computational methodologies, and empirical findings to unravel the complexities of brain function, neural networks, and their implications in understanding cognition, behavior, and neurological disorders. It highlights key achievements, methodologies, challenges, and future directions in computational neuroscience.

Abstract

Diabetes mellitus arises from a lack of insulin production by the pancreas or the body s inability to use the insulin produced effectively. Its a global concern with increasing number affected worldwide. Plants offer a promising avenue for developing hypoglycemic drugs, given the historical use of many plant-derived compounds in diabetes treatment, particularly in systems like Ayurveda. Indian medicinal plants have been extensively studied for their potential in managing diabetes, with numerous reports in scientific literature. This article aims to provide a detailed overview of various plant species native to India and their bio-active components that demonstrate significant hypoglycemic effects. Utilizing herbs for their hypoglycemic properties is a prominent aspect of traditional Indian medicine and warrants further exploration, considering the abundance of literature available in the topic. The paper delves into the chemical composition, activities, and applications of these isolated constituents from Indian plants in diabetes treatment.

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In this study, our goal was to screen novel potential FTO1inhibitors by structure-based virtual screening, ADME prediction, molecular dynamics simulation, and binding free energy calculations. We loaded the FTO target protein (PDB ID: 3LFM) at 2.57Å from the RSBC Protein Data Bank. We performed a virtual screening of a large compound library using it as a receptor. We selected seven top ranked compounds and further evaluated their binding modes and energies to FTO by molecular docking, molecular dynamics simulations, and binding free energy calculation methods. In addition, we predicted their ADME properties and stability using online computational tools. Among them, we identified a selection of potential compounds that showed high binding affinity, favorable ADME characteristics, and good stability with FTO. These candidates have the potential to be future herald compounds in the development of novel FTO drug targets. Our study provides new insights into FTO inhibitor discovery and demonstrates the feasibility and efficiency of using computational approaches for FTO inhibitor discovery.  

Abstract

This study aims to quantitatively assess the analgesic effect of morphine in patients with cancer-related pain. By implementing a pre- and post-treatment research design, we conducted a statistical analysis using the paired t-test to compare pain levels before and after morphine administration. A sample of cancer patients (n=number of participants) receiving palliative care was evaluated for pain intensity using a standard pain scale. The participants were administered morphine as part of their routine pain management protocol. Pain assessments were recorded both prior to and subsequent to the administration of morphine. The data collected were analyzed using SPSS software to calculate the mean difference in pain scores and examine the significance of morphine  effect on pain reduction. The paired t-test allowed for a controlled comparison within the same individuals, thus accounting for individual variations in pain perception and morphine response. Preliminary results indicate a statistically significant reduction in pain levels post-morphine treatment (p<0.05), implying that morphine is an effective analgesic for the alleviation of cancer pain. This research contributes to the overarching understanding of opioid efficacy in pain management and underscores the importance of individualized analgesic regimens. Furthermore, it brings attention to the need for rigorous, data-driven approaches in analyzing pain management strategies. Future research directions include exploring factors influencing morphine efficacy and tailoring pain management practices to enhance patient outcomes.