The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

No abstract

No abstract

Hemorrhagic shock (HS), a major cause of early death from trauma, accounts for around 40% of mortality, with 33-56% of these deaths occurring before the patient reaches a medical facility. Intravenous fluid therapy and blood transfusions are the cornerstone of treating HS. However, these options may not be available soon after the injury, resulting in death or a poorer quality of survival. Therefore, new strategies are needed to manage HS patients before they can receive definitive care. Recently, various forms of neuromodulation have been investigated as possible supplementary treatments for HS in the prehospital phase of care. Here, we provide an overview of neuromodulation methods that show promise to treat HS, such as vagus nerve stimulation, electroacupuncture, trigeminal nerve stimulation, and phrenic nerve stimulation and outline their possible mechanisms in the treatment of HS. Although all of these approaches are only validated in the preclinical models of HS and are yet to be translated to clinical settings, they clearly represent a paradigm shift in the way that this deadly condition is managed in the future.

No abstract