Effects of SARS-Cov-2 infection and rhino-orbital mucormycosis on concentrations of inflammatory biomarkers in Indian populations

Rhino-orbital mucormycosis is a rare life threatening invasive fungal infection that has recently shown a very high mortality rate in India during COVID-19 pandemic. We have designed the present study to find out associations between COVID-19 induced rhino-orbital mucormycosis and concentrations of inflammatory markers, i.e. D-dimer, Ferritin, IL-6, CRP and PCT, in blood serum of Indian population. There were four groups in the study, viz. control group with healthy subjects, treatment group-1 with patients suffering from SARS-COV-2 infection, treatment group-2 with patients suffering from both SARS-COV-2 infection and rhino-orbital mucormycosis, and treatment group-3 with patients suffering from rhino-orbital mucormycosis after SARS-COV-2 infection recovery. Inflammatory markers were quantified with standard protocols, and recorded data were subjected to statistical analyses. We found that patients suffering from SARS-COV-2 infection were more susceptible to rhino-orbital mucormycosis, as they had higher concentrations of inflammatory markers in their blood than the other subjects. Diabetes mellitus, hypertension, cardiovascular diseases and renal disorders were the associated comorbidities with the patients. We also found higher concentrations of inflammatory markers in males than the females, indicating towards their higher susceptibility in developing rhino-orbital mucormycosis than females. Present study therefore suggests that the frequent occurrence of rhino-orbital mucormycosis in India during second wave of COVID-19 was possibly due to indiscriminate use of corticosteroids by COVID-19 patients. Subjects with previous history of comorbidities like diabetes mellitus, hypertension, cardiovascular disorders and renal diseases are the most susceptible population groups for developing infection. Moreover, males are at higher risk of developing mucormycosis than the females. © 2022 Innovative Publication, All rights reserved.

A Big Data Approach for Healthcare Analysis During Covid-19

In the present times, with the massive growth of the Internet, unbelievably enormous measures of data are in our reach. Although our lives have been changed by prepared access to boundless information, still we need to explore the use of technology in various thrust areas. In this paper, we have analyzed and classify the mental state of people to raise awareness about mental health, especially during COVID-19. I have adopted the big data approach to accomplish this project. Two standard datasets have been used for our experiments. The idea behind our work is to use propose a customized mental health solution with the use of big data approach that can be useful for health care as well. We have applied state-of-the-art classifiers algorithm and found that the CountVec with the multinomial Naïve Bayes method gives the highest accuracy in terms of precision and recall. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Review on COVID-19 Diagnosis Using Imaging and Artificial Intelligence

The coronavirus epidemic is still on a surge and has harsh impacts on various factors across the globe including the economy and health. Though the recovery rate is also increasing, daily reporting cases are also increasing substantially. The best way till now is to take precautions and following the government guidelines. Till today, many different countries are line up to produce effective vaccination, but still, no such vaccine has completed its trial, and further, it will take a long time for the production and distribution among common citizens. We currently have a test process known as reverse transcription-polymerase chain reaction (RT-PCR) that is not reliable during the early stage of the disease. Also, a fast diagnosis is required as RT-PCR is time taking operation. Hence, imaging can be useful for the diagnosis as it can be quick and more reliable even in the early stage of the COVID-19 disease. Artificial techniques can be applied to radiological images such as CT scans and X-rays. In this article, we review the various research and responses in diagnosing the said disease using AI techniques on radiological images. Our findings suggest that using AI techniques like Convolution Neural Networks plays an important role in the diagnosing the COVID-19 by providing quick results and accuracy. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Inclusive Development Using Connected Devices and Data Analysis

The covid-19 pandemic has exposed the state of health systems all over the world. It can be observed that COVID tests are not being done even on mainstream and marginalized people, who are the worst sufferers of this pandemic. This is due to the fact, that the health care facilities are already unaffordable for many, and now it is out of reach altogether. Specifically, the unprivileged section of the society which is also referred to as shadow economy, these people are those who are not monitored by any government agency, does not comprise of any credit history to avail various government non-government benefits as well. IoT sensors and mobile technology prove to be a powerful tool for them to be financially included and creating a wave towards the transition to formality. So, these people can avail of healthcare benefits. In this research work, a sensor-based framework has been proposed so that people with no documents can also be trusted and brought into the mainstream economy with full fundamental rights. The paper proposes a methodology for computing trust and value without the use of financial instruments and collateral grantees, this is done by using sensors, mobile tracking, and data analysis from which a trust value is assigned to a marginalized individual. This trust value score will aid the beneficiary to avail various public and private sector facilities and become a case of inclusive development. © 2020 IEEE.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

Purpose - The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach - This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings - The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications - One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value - Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.