Ayurvedic management of neurological deficits post COVID-19 vaccination - A report of two cases.

The world witnessed much research fund allocation on the COVID-19 outbreak's epidemiology, pathology, impact on lifestyles, social behaviours and treatment possibilities. The highly contagious nature of the disease compelled scientific communities and related organisations to hasten vaccine development and supplies. Well-timed international collaborations resulted in quicker development of varied forms of vaccines against COVID-19. Prospective observational studies and systematic reviews on vaccine trials reported their safety and efficacies. Nevertheless, post-marketing surveillance is quintessential to ascertain such safety and efficacy claims. There have been scattered reports lately of several adverse temporal events, such as haematological, immunological and neurological untoward occurrences following COVID-19 inoculation. There is a growing piece of evidence of the impact of COVID vaccination on patients with neurological-neuroimmunological disorders. Here two unrelated cases of neurological deficits post-COVID vaccination are reported. One was an incidence of Acute Disseminated Encephalomyelitis, while the other was an acute exacerbation of Multiple Sclerosis following vaccination. Ayurvedic treatments were effective in either of these conditions. Case series and case reports shall judiciously add information to vaccine safety data and acknowledge the necessity of clinician approval, based on detailed individualised assessments before mass vaccination.

Detection of COVID-19 and Classification of Pneumonia Using Deep Neural Networks Algorithms

COVID-19 which is a subclass of severe acute respiratory syndrome (SARS) is a viral disease which emerged from China in 2019. At first, there are shorthand of test kits available to diagnose the COVID-19 disease. The tests available to diagnose the COVID-19 are RT-PCR (real-time polymerase chain reaction), Rapid Antigen test and Antibody test. But in these, only RT-PCR has the high accuracy, and it is a time-taking process. It takes nearly from 4 to 48 h. Here, AI plays an important role in diagnosing the disease. In the recent years, AI becomes a part of medical field and is widely used in classification. The chest X-Rays are used to detect the COVID-19 using deep learning and the model used to detect the COVID-19 is ResNet18 which is a residual network containing 18 layers. In this work, we classified four types of classes to make sure that our model performance is better and classify accurately. The data set contain a total of 5365 images. In this, we used 80% of data for the training and 20% for validation. The accuracy obtained in classification of three classes is 96.67% and for four classes, the accuracy is 91%. We have also used another model for comparison which ResNet50 and achieved an accuracy of 75%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Development of Efficient E-Health Records Using IoT and Blockchain Technology

In the COVID-19 pandemic situation, the transmission of health records sharing and maintaining the integrity of data to receive the end-user is not consistent. Already, some countries decide to conduct lockdown using data models. In this scenario, the end user loss of integrity, loss of confidentiality and security on health records impacts the patients. The security of electronic health records (EHR) is a challenging task especially in COVID-19 like situations. It is a necessity to share secure EHR to physicians and patients which plays a vital role in COVID-19, and also data availability, scalability, and performance of data to receive the end-user is the loss of integrity. In this paper, we proposed an integrated approach of blockchain 4.0 technology used in IoT-based cloud environment that reduce the latency while transmission of health care records and introduce the blockchain-based electronic health records named as BEHR transmission between the physicians and patients. The performance of the proposed approach tested with the NodeJs software and ApacheJmeter open source JMeter Cloud Test environment and proved that the simulation-results of BEHR are an efficient approach in response time and file storage and transmission of EHR than the present conventional system.