Mask Recognition through Deep Learning

A major part of computer vision is formed by Object detection. Most of the such tasks are done with efficient object detection. This paper aims to incorporate techniques for facial mask detection to achieve an accurate and efficient mask detection algorithm. The goal is to examine various deep learning algorithms to perform mask detection in this era of Covid. This paper aims on building an application based on facial mask recognition using different deep learning algorithms and compare the results to find out the most accurate algorithm. © 2022 IEEE.

COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery

The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COVID-19 pneumonia but chest imaging, including CT scans and X-Ray imagery, can also be used as a secondary important tool for the diagnosis of pneumonia, including COVID pneumonia. However, the interpretation of chest imaging in COVID-19 pneumonia can be challenging, as the signs of the disease on imaging may be subtle and may overlap with normal pneumonia. In this paper, we propose a hybrid model with the name COVINet which uses ResNet-101 as the feature extractor and classical K-Nearest Neighbors as the classifier that led us to give automated results for detecting COVID pneumonia in X-Rays and CT imagery. The proposed hybrid model achieved a classification accuracy of 98.6%. The model's precision, recall, and F1-Score values were also impressive, ranging from 98-99%. To back and support the proposed model, several CNN-based feature extractors and classical machine learning classifiers have been exploited. The outcome with exploited combinations suggests that our model can significantly enhance the accuracy and precision of detecting COVID-19 pneumonia on chest imaging, and this holds the potential of being a valuable resource for early identification and diagnosis of the illness by radiologists and medical practitioners. © 2023

A Multifaceted and Multi-Institutional Analysis of the COVID19-Associated Mucormycosis Outbreak in the Delhi Area Indicates the Simultaneous Convergence of Multiple Risk Factors

Background. A major outbreak of COVID19-associated mucormycosis (CAM) in India in spring 2021 aggravated the death toll of COVID19. As the causes of that CAM outbreak remain unclear, we performed a multifaceted study of host, pathogen, environmental, and heath care-related factors in adult CAM patients (pts) in the metropolitan New Delhi area. Methods. We reviewed the records of all pts diagnosed with culture- or biopsyproven CAM at 7 hospitals in the New Delhi area (April 1 - June 30, 2021). We used a multivariate logistic regression model to compare clinical characteristics of either all CAM cases (analysis 1, n = 50) or only pts with CAM after moderate or severe COVID19 (analysis 2, n = 31). As controls for both analyses, we used 69 COVID19-hospitalized contemporary pts. Selected hospital fomites were cultured for Mucorales. Additionally, we compared meteorological data and fungal spore concentrations in outdoor air before the CAM outbreak (January-March 2021) and during the outbreak (April-June 2021). Mucorales isolates from CAM pts were identified by MALDI-TOF-MS and ITS sequencing. A subset of 15 isolates underwent whole genome sequencing (WGS). Results. Risk factors for CAM in both analyses were newly diagnosed diabetes mellitus (odds ratio [OR] 8.26/5.67) and active cancer (OR 5.98/5.68) (Figure 1). Supplemental oxygen for COVID19 was associated with a lower CAM risk in both analyses (OR 0.13/0.17). Another significant CAM risk predictor identified only in analysis 1 was severe COVID19 (WHO score >= 6, OR 4.09), while remdesivir therapy (OR 0.40) and ICU admission for COVID19 were protective (OR 0.41) (Figure 1). No Mucorales were cultured from hospital fomites. The CAM incidence peak coincided with a significant uptick in environmental spore concentrations but was not linked to specific meteorological factors. Rhizopus was the predominant Mucorales genus (64%) identified by MALDI-TOF-MS and ITS sequencing;WGS found no clonal population of isolates but detected 2 cases of the rare pathogen Lichtheimia ornata. Figure 1 Conclusion. An intersection of host, environmental, pathogen and healthcare-related factors might have contributed to the emergence of CAM. Surrogates of access to advanced treatment of COVID19 were associated with lower CAM risk.

SARS-CoV-2 virus: mechanism of pathogenesis, replication and significant impact on children, cancerous, diabetic patients and pregnant women - a review

'Corona', this alarming word comes from the 'Latin' word 'Crown' that protects the virus. On Dec, 19, firstly, this virus was isolated from three patients having pneumonia connected to a cluster of acute illnesses. WHO declared it a 'pandemic' in Jan, 20 but later in Feb, WHO's general director Tedros Adhanom Ghebreyesus named the virus nCOVID-19. It was first identified in Wuhan, China, as a respiratory illness causing novel diseases (SARS and MERS). CDC informed corona primarily causes mild to moderate upper RTI and, in a few cases, lower RTI (pneumonia, bronchitis). Transmission occurs through direct contact or air droplets of sneezing, coughing, etc. The origin is not clear, but recent studies reported that ACE 2, a membrane exopeptidase receptor, was used to enter the human cell. The primary symptoms are fever above 104 degrees F, shortness of breath, pneumonia, throat soreness, diarrhea, etc. Available approved therapeutics include hydroxychloroquine. This current review updates about the viral transmission and main effect of this virus on children, pregnant women, diabetic, and cancer patients.

Flattening the curve: Pandemic-Induced revaluation of urban real estate

We show that the COVID-19 pandemic brought house price and rent declines in city cen-ters, and price and rent increases away from the center, thereby flattening the bid-rent curve in most U.S. metropolitan areas. Across MSAs, the flattening of the bid-rent curve is larger when working from home is more prevalent, housing markets are more regu-lated, and supply is less elastic. Housing markets predict an urban revival with urban rent growth exceeding suburban rent growth for the foreseeable future, as working from home recedes. (c) 2021 Elsevier B.V. All rights reserved.

Gauging the impact of a pandemic on the lives and livelihoods of female domestic worker across Indian cities

Domestic workers are one of the most unprotected groups of the global workforce in informal employment who remain outside the ambit of social security and legal protection. Despite their significant contribution to the economy and society, they are often invisible and undervalued. The pre-existing adversities and vulnerabilities became all the more evident during the recent health crisis. While different groups of workers faced constraints to support their livelihood, the women domestic workers were hardest hit, facing total or near unemployment, job losses and economic distress. This paper attempts to analyse the impact of pandemic on the lives and livelihoods of female domestic workers in five Indian cities including Pune, Lucknow, Jhansi, Katni and Bhopal. The analytical findings from the random sample survey of 250 female domestic worker provides a temporal analysis of the impact of covid-19 on the nature of work, income, expenditure and consumption across the various cities of India. Our findings not only increase our understanding on the impact of the crisis for domestic worker but also informal workers in general. This also help inform the policy response of authorities towards addressing the exclusion of domestic workers from the ambit of the privileges that workers employ in the formal sector. © International Journal of Health Sciences 2022.

A case report of early eosinophilic and ulcerative colitis

Introduction: Eosinophilic colitis (EC) is a rare inflammatory gastrointestinal disorder with a poorly understood pathophysiology. Recent studies suggest that eosinophil-driven breakdown of colonic wall structure may be contributing to both EC and early stages of inflammatory bowel disease (IBD). We report a case of a young woman with biopsy findings suggestive of both EC and ulcerative colitis (UC) in an early disease state. Case description/methods: An 18 year old female with a history of irritable bowel syndrome (IBS) for 2 years, obesity (BMI 31), depression, anxiety, migraines and ovarian cysts presents for outpatient colonoscopy for suspected ulcerative colitis after 3 months of diarrhea, vomiting, diffuse abdominal pain, chills and body aches. A CT abdomen showed a 12mm left ovarian cyst and no other discernible pathology. Her medications include bupropion, escitalopram, hydroxyzine, Depo-Provera, dicyclomine, and mesalamine rectal enema. She denies toxic habits and her vital signs were unremarkable. Her CMP, CBC and coagulation panels were also unremarkable, and COVID testing was negative. Her colonoscopy showed diffuse mild inflammation extending from the rectum to the descending colon suggestive of left-sided ulcerative colitis. Biopsies were taken from the right colon, left colon and terminal ileum. Pathology report showed mild active colitis with moderate eosinophilia in the right colon with at least 50 eosinophils per high power field (HPF), with similar findings in the left colon. The eosinophilia extended into the lamina propria with focal epithelial invasion of eosinophils, but no overt distortion of crypts or other signs of chronic colitis were noted. The terminal ileum showed no diagnostic pathology. Overall, the findings suggest a combination of early ulcerative colitis with eosinophilic colitis. Discussion: First described in the mid-19th century, eosinophils are continuously active in mucus and antibody secretion and should populate colonic mucosa in counts no higher than 50 per high power field. In contrast to EC, UC is characterized by mucosal infiltration by neutrophils helped in part by eosinophilic secretion of chemokines. Although our patient demonstrates left-sided colitis, histology did not show characteristic crypt abscesses nor neutrophilic invasion, but rather an eosinophilic predominance with a relatively mild phenotype. In light of current pathophysiologic literature, this may represent an early stage of UC development, or a mixed EC/UC phenotype rarely observed otherwise. (Figure Presented).

A Three-Fold Machine Learning Approach for Detection of COVID-19 from Audio Data

Most work on leveraging machine learning techniques has been focused on using chest CT scans or X-ray images. However, this approach requires special machinery, and is not very scalable. Using audio data to perform this task is still relatively nascent and there is much room for exploration. In this paper, we explore using breath and cough audio samples as a means of detecting the presence of COVID-19, in an attempt to reduce the need for close contact required by current techniques. We apply a three-fold approach of using traditional machine learning models using handcrafted features, convolutional neural networks on spectrograms and recurrent neural networks on instantaneous audio features, to perform a binary classification of whether a person is COVID-positive or not. We provide a description of the preprocessing techniques, feature extraction pipeline, model building and a summary of the performance of each of the three approaches. The traditional machine learning model approaches state-of-the-art metrics using fewer features as compared to similar work in this domain. © 2021, Springer Nature Switzerland AG.

Correlation of risk perception with the COVID-19 related knowledge and preventive measures: A study on Indian pharmacy students

Introduction: A novel threat to mankind occurred in December 2019 which was an outbreak of infection caused by a novel coronavirus (SARS-CoV-2 or 2019-nCoV). The infection was first developed in Wuhan, China, and has affected more than 200 countries around the world till now. Objective: The present study aims to assess the knowledge related to coronavirus disease (COVID-19), risk perception and preventive behaviours among the Pharmacy students in a part of India approximately 3 months after the onset of this outbreak in India. Methods: This survey was conducted from 2nd to 5th of September 2020 with Indian Pharmacy students (1st to 4th year). The knowledge, self-reported preventive behaviours and risk perceptions of COVID-19 were assessed using an online questionnaire. A total of 21 questions were there in the questionnaire in which 14 questions were about knowledge related to COVID-19, 4 items regarding preventive behaviours and 3 about risk perception. Results: A total of 268 participants completed the questionnaire. The participants were under the age group of 15-30 years. A high level of disease-related knowledge was found in the participants (77.66%). On an average 96.1% of participants were practising preventive behaviours. The aggregate score of items in risk perception section was found to be in the moderate range i.e., 5.38 out of 8. A significant negative correlation was obtained between risk perception and preventive behaviours. Conclusion: The trajectory and severity of this outbreak are very high, therefore, effective treatment against this global threat is required to be developed as early as possible. In the present study, a high level of disease-related knowledge and preventive behaviours were observed among the participants with a moderate level of risk perception. © IJCRR.