Various charging Methods in Electric Vehicle Application: A Review

During the covid pandemic, air quality has improved due to prolonged lockdown conditions. Hence according to the international energy agency, about 22% of environmental pollution is contributed by the transportation sector. Electric vehicles help in reducing the contribution towards carbon emission and help in mitigating the fossil fuel crisis and also promotes sustainable transportation. To enhance the growth of electric vehicle, charging infrastructure and range anxiety issues in the long drive has to be resolved. This paper reviews the various charging methods available for an electric vehicle. Some charging methods are wired and wireless charging, solar-powered, battery-swapping, vehicle-to-grid and vehicle-to-vehicle charging. A comparative study of these methods is tabulated. Based on the limitation of each method the optimum charging method for a vehicle is adapted for a particular application. © 2023 IEEE.

A Rare Case Of Eosinophilic Myocarditis Due To Subacute Sars-cov-2 Illness

Background: Eosinophilic myocarditis is a rare inflammatory cardiomyopathy with a poor prognosis. SARS-CoV-2 (COVID-19) illness has been associated with myocarditis, particularly of lymphocytic etiology. Although there have been cases of eosinophilic myocarditis associated with COVID-19 vaccination, there have been few reported cases secondary to COVID-19 illness, with the majority being diagnosed via post-mortem autopsy. Case: A 44-year-old woman with no significant medical history other than recent COVID-19 illness 6 weeks prior presented with progressive dyspnea. Patient developed acute dyspnea and diffuse pruritic rash after taking hydroxyzine. Labs were significant for mild eosinophilia. Echocardiography showed biventricular systolic dysfunction with left ventricular ejection fraction of 40%, and a moderate pericardial effusion that was drained percutaneously. She underwent left heart and right heart catheterization showing elevated biventricular filling pressures, Fick cardiac index of 1.6 L/min/m2, and no coronary disease. She was started on intravenous diuretics and transferred to our facility for further management. Her course was complicated by cardiogenic shock requiring intra-aortic balloon pump (IABP) support. Mixed venous saturations continued to decline and the patient was placed on veno-arterial extracorporeal membrane oxygenation (VA-ECMO) support. The patient underwent endomyocardial biopsy (EMB) showing marked interstitial infiltration of eosinophils and macrophages with myocyte injury (see image). She was intubated with mechanical ventilation as well due to worsening pulmonary edema and hypoxemia. She was started on intravenous steroids with improvement of hemodynamics and myocardial function and eventually VA- ECMO was decannulated to low-dose inotropic support which in turn was ultimately weaned after 3 days of mechanical support. Conclusion(s): Eosinophilic myocarditis is a rare and under-recognized sequela of acute COVID-19 infection associated with high mortality rates. It requires prompt diagnosis and aggressive supportive care, including temporary mechanical circulatory support. There are few literature-reported cases of COVID-19 myocarditis requiring use of both IABP and VA-ECMO, none of which were used in biopsy-proven eosinophilic myocarditis, with most of these cases resulting in either fatal or unreported outcomes. Most cases of covid myocarditis required IV glucocorticoids therapy in conjunction with IVIG or interferon therapy. Here, we present a rare case of cardiogenic shock secondary to biopsy-proven eosinophilic myocarditis associated with recent COVID-19 illness with a survival outcome after temporary use of IABP and VA-ECMO support, as well as aggressive immunosuppressive therapy.Copyright © 2022

COVID-19 detection using hybrid deep learning model in chest x-rays images

The novel-corona-virus is presently accountable for 547,782 deaths worldwide. It was first observed in China in late 2019 and, the increase in number of its affected cases seriously disturbed almost every nation in terms of its economical, structural, educational growth. Furthermore, with the advancement of data-analytics and machine learning towards enhanced diagnostic tools for the infection, the growth rate in the affected patients has reduced considerably, thereby making it critical for AI researchers and experts from medical radiology to put more efforts in this side. In this regard, we present a controlled study which provides analysis of various potential possibilities in terms of detection models/algorithms for COVID-19 detection from radiology-based images like chest x-rays. We provide a rigorous comparison between the VGG16, VGG19, Residual Network, Dark-Net as the foundational network with the Single Shot MultiBox Detector (SSD) for predictions. With some preprocessing techniques specific to the task like CLAHE, this study shows the potential of the methodology relative to the existing techniques. The highest of all precision and recall were achieved with DenseNet201 + SSD512 as 93.01 and 94.98 respectively.

Gastrointestinal manifestations and outcomes of Covid-19 infection. A single-center prospective study from Northern India

Background and Aim: Fever, cough, dyspnea, running nose, and loss of smell are typical Covid-19 manifestations. However, gastrointestinal symptoms have also been reported. To find the gastrointestinal manifestations and their effect on clinical outcome, we conducted this prospective study in Northern India. Methods: All adult patients of confirmed COVID-19 infection (SARS CoV-2 RT PCR positive) presented at our institution were enrolled for the study. Demographics, medical history, symptomatology, laboratory data, and clinical outcomes were documented. Results: In our cohort of 5034 patients, 73.63% showed at least one gastrointestinal (GI) symptom at diagnosis. The common gastrointestinal manifestations were loss of appetite in 62.88%, nausea/vomiting in 45.34%, diarrhea in 32.45%, deranged LFT in 27.81%, Gastrointestinal bleeding in 15.29%, abdominal pain/epigastric burning in 11.5%and pancreatitis in 5.02% No definite cause of pancreatitis could be found in 1.02% (38/3707) of such patients. Hence, SARS CoV-2 may be a possible etiology. Most of them (81.3%) were mild to moderately severe as per Atlanta classification. Interestingly, most of them, 63.15%, developed acute peripancreatic fluid collections. Of the patients, 38.92% had GI symptoms alone. In 46.2% of patients, the GI symptoms preceded the respiratory or other symptoms. Patients with pancreatitis alone showed significantly higher levels of white blood cells (P 0.06), C-reactive protein (P 0.020), and D dimer (P 0.0007) levels and had higher mortality. In our cohort with GI manifestations, 762 (20.55%) of patients required mechanical ventilation, and 437 (11.78%) died. We found that proper GI symptoms, particularly diarrhea, were associated with lower mortality (OR 0.3;95% CI 0.182). Conclusion: Covid-19 infection may manifest GI symptoms like nausea, vomiting, diarrhea, deranged LFTs, pancreatitis. They predict lower mortality, except pancreatitis.

Covid-19 detection from chest X-Ray images using advanced deep learning techniques

Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task. We used a publicly open CXR image dataset and implemented the detection model with task-specific pre-processing and near 80:20 split. This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597, which shall help better decision-making for various aspects of identification and treat the infection. © 2021 Tech Science Press. All rights reserved.

COVID-19 Detection: An Approach Using X-Ray Images and Deep Learning Techniques

In the recent history of human civilization, a pandemic affecting such an enormous population like COVID-19 was about 140 years ago-The Smallpox Worldwide Epidemic (1877–1977, Deaths-500 M). It can be easily inferred that the health management system over the globe in the nineteenth century was too underdeveloped than that of today, which also refers to the fact that the present epidemic must not be allowed to last much longer as the number of deaths is increasing nonlinearly (506 K, with 10.3 M affected). While the medical community around the globe is striving to find a permanent cure, it becomes evident responsibility of all professionals who can contribute in stabilizing the medical management systems of countries particularly underdeveloped/developing countries or those with highest rate of increase in COVID-19 cases like USA, Brazil. In this regard, this study introduces a fast, robust and practically effective method for detection of COVID-19 from chest x-ray images utilizing enhanced deep learning techniques. An object detection network is proposed to be trained with publicly existing datasets. In this model, SSD is used with ResNet101 as a base layer and some pre-processing, achieving a sensitivity of 0.9495 and a specificity of 0.9247. If practically implemented, this can prove very beneficial in aiding economies and health systems of the above-mentioned countries. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3D printing for development of medical equipment amidst coronavirus (COVID-19) pandemic—review and advancements

Background and aims: Coronavirus (COVID-19) has surfaced as a global pandemic and has created an unprecedented global demand for medical equipment. The shortage of onsite workforce, need for social distancing and less time available for sourcing have further made it difficult for the governments and the medical professionals to combat the pandemic. This study’s prime objective is to review the advancements in the area of 3D printing to develop medical equipment and explore the potential of 3D printing in addressing the shortage of medical equipment mainly the personal protective equipment (PPE) amidst COVID-19 pandemic. Methods: 3D printing or additive manufacturing has emerged as a new manufacturing process with tremendous potential to develop complex products in short time with minimal human interventions. The paper summarises 3D printing’s potential to serve the increasing need for medical equipment, mainly personal protective equipment (PPE) and ventilator equipment in the ongoing global COVID-19 pandemic. Results: The minimum human interventions required to carry out production using 3D printing also make the technology an excellent option to deal with the current situation. Conclusions: The recommendations and opinions presented in the paper shall act as a stimulant to develop components very critical for the pandemic and help save precious lives globally. © 2020, Sociedade Brasileira de Engenharia Biomedica.