Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN)

COVID-19 endemic has made the entire world face an extraordinary challenging situation which has made life in this world a fearsome halt and demanding numerous lives. As it has spread across 212 nations and territories and the infected cases and deaths are increased to 5,212,172 and 334,915 (as of May 22 2020). Still, it is a real hazard to human health. Severe Acute Respiratory Syndrome cause vast negative impacts economy and health populations. Professionals involved in COVID test can commit mistakes when testing for identifying the disease. Evaluating and diagnosing the disease by medical experts are the significant key factor. Technologies like machine learning and data mining helps substantially to increase the accuracy of identifying COVID. Artificial Neural Networks (ANN) has been extensively used for diagnosis. Proposed Single Hidden Layer Feedforward Neural Networks (SLFN)-COVID approach is used to detect COVID-19 for disease detection on creating the social impacts and its used for treatment. The experimental results of the proposed method outperforms well when compared to existing methods which achieves 83% of accuracy, 73% of precision, 68% of Recall, 82% of F1-Score. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Chest X-ray scoring systems as a tool in predicting severity of COVID -19 pneumonia

Background: Even though very useful a routine of CT scan Is not feasible in a pandemic setting due to the large number of patients and lack resources. Therefore if we are able to use chest x-ray scores for assessing the severity of covid-19 pneumonia it could be very useful in taming the pandemic. Methods: A Retrospective study was conducted using data from 118 confirmed cases of covid -19 pneumonia admitted to our hospital from march to august 2021.Chest x-ray of each patient was scored using RALE Score, BRIXIA score and modified chest x-ray score developed at Dr. Soetemo general hospital, Surabaya, Indonesia. These scores were compared with clinical severity of the desease. Results: Among the 118 patients selected 65 (55%) were males and 53(45%) were females. All three scoring systems are significantly correlated with the clinical severity of the disease, with the strengths of correlation in order from the strongest to weakest as rale score (p< 0.01, correlation coefficient 0.865), brixia score (p< 0.01, correlation coefficient 0.852), and Dr. Soetomo General Hospital score (p< 0.01, correlation coefficient 0.804). All three scoring systems correlate significantly with each other. Dr. Soetomo General Hospital score correlates more towards Brixia score (p< 0.01, correlation coefficient 0.808) than RALE score (p< 0.01, correlation coefficient 0.836). Brixia to RALE score correlates with a coefficient of 0.857 (p< 0.01). Conclusion: All the three chest x-ray scoring systems are equally useful for aassesing the severity of COVID-19 pneumonia.

The Challenge of COVID-19: A Compelling Mission for Veterinary Profession

While thecovid -19 pandemic has been devastating and disrupting the normal life of people across the globe, veterinarians have crucial obligations, opportunities, and contributions to make the country self-reliant, self-sufficient and self-sustaining. They accomplish this by enhancing the health and wellbeing of animals, environment and community. They also aid in detecting and responding to zoonotic diseases, maintaining food security and water quality, and promoting wildlife and ecosystem health. Failing to seize this moment could definitely undermine public health and global security for generations. Despite the fact that the strategic modifications of the veterinary education system, client dealing as well as the trade and investment regulations implemented in the aftermath of the covid-19 pandemic has an immense prospective for progress, there are several pitfalls in this system. This must be addressed in order to face the challenge, accept the future and relinquish the opportunities to create a more sustainable profession.

Phase-Resolved Lockdown Features of Pollution Parameters Over an Urban and Adjoining Rural Region During COVID-19

In this study, we investigate the temporal variations in columnar aerosol pollutants and their possible association with the simultaneously measured black carbon (BC) aerosol mass concentration and associated biomass burning (BB) over urban (Delhi) and rural (Panchgaon) sites during the lockdown phases of the COVID-19 pandemic. We also show the impact of lockdown measures on boundary layer ozone and its primary precursors, NO2, and water vapor (H2O), potent greenhouse gases that destroy protective ozone. For this purpose, we used multiple datasets, namely, black carbon (BC) aerosol mass concentration and biomass burning (BB) aerosols using an aethalometer at Amity University Haryana (AUH), Panchgaon, India, and satellite retrievals from NASA’s MODIS and OMI at both the stations. The analysis was conducted during the pre-lockdown period (1–25 March), lockdown 1st phase (25 March–14 April), lockdown 2nd phase (15 April–3 May), lockdown 3rd phase (4–17 May), lockdown 4th phase (18–31 May), and post-lockdown (1–30 June) period in 2020. Our diagnostic analysis shows a substantial reduction in AOD (Delhi: −20% to −80%, Panchgaon: −20% to −80%) and NO2 (Delhi: −10% to −42.03%, Panchgaon −10% to −46.54%) in comparison with climatology (2010–2019) during all four phases of lockdown. The reduction in AOD is attributed to lockdown measures and less transport of dust from west Asia than climatology. Despite a reduction in NO2, there is an increase in the ozone amount (Delhi: 1% to 8% and Panchgaon: 1% to 10%) during lockdown I, II, and III phases. The observed enhancement in ozone may be resultant from the complex photochemical processes that involve the presence of NO2, CO, volatile organic compounds (VOCs), and water vapor. The reduction in AOD and NO2 and enhancement in ozone are stronger at the rural site, Panchgaon than that at the urban site, Delhi. Copyright © 2022 Sonbawne, Fadnavis, Vijayakumar, Devara and Chavan.

Effectiveness of Virtual Therapeutic Exercises in Improving the Physical and Mental Health among the Patients Recovering from COVID-19

Introduction: A novel coronavirus (COVID-19), caused a series of acute atypical respiratory syndrome termed as SARS CoV-2. It has a varying degrees of symptoms like headache, high fever, dizziness, generalized weakness, diarrhoea and vomiting. But primarily it affects the respiratory system causing breathlessness and sometimes may be fatal. People recovered from the illness had variety of physical and mental illness. Aims: To find the effectiveness of virtual therapeutic exercises and mindfulness programme among the subjects recovering from COVID-19. Materials and methods: A total number of 32 male subjects aged between (55 - 70) years were included in this study. The subjects were included based on the prescription of a medical officer/ Curved length of Aortic Knuckle (AKC), total length of Left Heart Border (LHBT) and Aortic Knuckle Index (AKI) were measured. Statistical analysis was carried out with the help of IBM-SPSS (IBM Corporation) and Microsoft Excel. Results: AKC and AKI were measured,and correlated with total lenght of LHBT. A positive correlation between AKC, AKI and LHBT was noted. Conclusion: Simple measurement of aortic knob in PA chest x-ray may help in predicting cardiovascular disorder. pulmonologist. The duration of the study was eight weeks. Fitness assessment scale, hamilton stress anxiety scale and modified respiratory assessment scale was used. The study was conducted in the Symbiosis Medical College for Women, Pune. Results: The statistical analyses were done using the SPSS software version 18 executed at a 95% confidence interval. A paired t-test was done to find the effectiveness of the therapeutic exercises. The level of significance in all tests was set to p < 0.05. Positive changes were observed in health-related fitness among the subjects. Conclusion: This study reports about finding that virtual therapeutic training also provides the best results in physical and mental health among the patients recovering from COVID-19.

Forecasting the lung diseases from Rediography scans with hybrid Transfer Learning Techniques

Lung related issues are rapidly increasing day by day as it is very important to identify the disease and get treated earliest possible as lungs are part of very complex system, expanding and relaxing thousands of times each day allow us to breathe by bringing oxygen into our bodies and sending carbon dioxide out. Lung related issues are directly preoperational to breathing problems. X-rays are one of the important ways of identifying the status of lungs. As there are many communicable diseases like Covid-19, the person should be identified early and should be treated to control the spread of virus. Lung Opacity is one of the major problem faced by many people and also a very serious problem if not treated early it will spread entire lungs and which leads to cancer similarly Pneumonia is another disease which is an infection to one's lungs caused by spread of virus. All these diseases directly affect Respiratory system of human. The paper aims to lung diseases classification among Pneumonia, Lung opacity, Normal and Covid-19 using the proposed hybrid model. The Deep Transfer Learning model helps to extract good features which helps for better learning and greater results. The Ensembled model of Deep Transfer Learning is used in this paper, which is a combination of VGG, EfficientNet and DenseNet. Considering the output of image augmentation as input for Ensembled model and classification of lung disease. The accuracy of the proposed hybrid model is very much accurate when compared to individual base models. © 2021 IEEE.

Current perceptions on advanced molecular diagnostics for drug-resistant mycobacterium tuberculosis

Globally, rising drug-resistant tuberculosis is a significant public health concern. Prompt diagnosis of tuberculosis and detection of drug-resistant TB within a clinically appropriate timeframe is important for the effective management of the disease. Imaging approaches Chest X-rays, CT, MRI, nuclear medicine technique as PET/CT are non-specific, plays an important role in the diagnosis and assessment of TB, but PET/CT sometimes results in false-positive or negative due to benign lesions. Currently using the point of care molecular modalities, GeneXpert MTB/RIF and line probe assays focused only on resistance-conferring mutations in specific target hotspot regions, but did not identify novel mutations, outside mutations and they may miss some locally prevalent rifampicin-conferring mutations, and not provided a large number of antibiotics/antibiotic groups that are used for DRTB treatment. Recently revolutionized high throughput next generation sequencing (NGS) technologies are offering new prospects for molecular diagnosis, for example, infectious disease pathogens like tuberculosis, influenza, and most recently SARS-CoV-2. NGS is an essential resource for the tuberculosis community either target, WGS, or NGS;a rapid method that offers a complete spectrum of Mycobacterium tuberculosis resistance mutations, strain typing for transmission surveillance, unlike traditional molecular or phenotypic DST. It shall be helpful for early regimen design and TB management before mutations emerge and therefore, we believe that the worldwide TB infection will be eliminated by the use of NGS.