A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu.

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.

Analysis of the laboratory and CT radiological findings in covid-19 patients on admission in comparison with their clinical severity status

Purpose: To retrospectively analyse the clinical status of COVID 19 patients with CT and laboratory findings Method: A total of 250 RT PCR test positive patients data were collected during the period of March 2020 to June 2021. Results: Present study showed that patients who did not need oxygen on admission, 9% of patients had a severe CT score, 41% with moderate CT score and 51% with mild CT score. Those who needed oxygen 17% of patients had a severe CT score, 40% with moderate CT score and 43% with mild CT score. Study also showed that in those who needed ICU admission, 27% of patients had a severe CT score, 56% with moderate CT score and 17% with mild CT score. Laboratory parameters such as Lactate Dehydrogenase (LDH), Ferritin, C Reactive Protein (CRP) and D-Dimer showed a significant p value between severity categories. Conclusion: This study has shown that the CT severity score alone does not always have quantifiable relation to the clinical severity of the patient. In present study found laboratory parameters has no positive correlation between mild CT score, but with moderate and severe CT score scans, a significant correlation was found. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Over the last two years, governments all over the world have used a variety of containment measures to control the spread of covid, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented in actuality. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, concretely detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework that enables the execution of a wide range of standard epidemiological models for any infectious disease on a population's most recent real contact graph while keeping all contact information private locally on the participants' devices. Our theoretical constructs are supported by a proof-of-concept implementation in which we show that a 2-week simulation over a population of half a million can be finished in 7 minutes with each participant consuming less than 50 KB of data. © 2022 Owner/Author.

Posttraumatic Stress Disorder Among Adolescent Girls Infected with COVID-19: A Cross-Sectional Study

Background: Pandemics are known to cause an increase in the prevalence of posttraumatic stress disorder (PTSD). The current COVID-19 pandemic has led to a surge in the same along with other problems such as anxiety and depression. Aim(s): To find out the prevalence of PTSD and other mental health abnormalities among adolescent girls who have contracted COVID-19 infection and the factors associated with it. Method(s): This was a cross-sectional study carried out for 6 months at RL Jalappa Hospital and Research Center, Kolar. Adolescent girls infected with COVID-19 were included. A total of 100 girls took part in the study after matching inclusion and exclusion criteria. To assess for PTSD, the Mini International Neuropsychiatry Interview-KID (MINI-KID) and CPSS-5 Interviewer Version (CPSS 5-I) questionnaire were used. Data entered in Microsoft office excel were analyzed using SPSS v 22 (IBM Corp., Armonk, NY, USA). To check for the association between factors, chi-square test was applied. Result(s): The majority of girls were aged 18 years, belonged to nuclear families, and had a hospital stay for 7 days to14 days. About 15% of the girls had PTSD diagnosed using two tools. Panic disorder and separation anxiety were commonly seen. Duration of COVID symptoms adolescent girls had before being diagnosed with COVID-19 by RT-PCR and duration of the hospital stay along with symptoms like throat pain, headache, and diarrhea were a few clinical factors that had a statistically significant association with PTSD. Mental health screening seems to be disregarded in COVID-19 cases. COVID-19 can be a traumatic stressor event among adolescents which can elicit PTSD-like responses and also exacerbate mental health problems. Conclusion(s): Early screening for PTSD or other mental health abnormalities among adolescents post COVID-19 infection should be considered. Copyright © 2022 Indian Association for Child and Adolescent Mental Health, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses.

Prediction of COVID-19 by analysis of Breathing Patterns using the Concepts of Machine Learning and Deep Learning Techniques

The corona virus, otherwise known as the 'Covid-19' is a pandemic that struck the world in December of 2019 and continues on till this day as of writing this research article. It's a virus that targets & affects an individual's immune system. Its most common symptoms include fever, dry cough & tiredness. The most commonly used method used to detect the presence of the COVID-10 virus is the Reverse Transcription Polymerase Chain Reaction Test also known as the RT-PCR test. It is an invasive biomedical procedure that utilizes a nasal swab for the sample collection and provides results in about 24 hours after testing. The research work presented in this paper makes use of parameters such as the breathing patterns, smoking and drinking habits, etc. to detect the likelihood of an individual being proned to the Covid-19 virus. This is achieved by making use of a data set which will be used to train the various machine learning and deep learning algorithms. © 2022 IEEE.

Virtual Safety Assistant: An Efficient Tool for Ensuring Safety During Covid-19 Pandemic

Using intelligent virtual assistants for controlling employee population in workspaces is a research area that remains unexplored. This paper presents a novel application of virtual humans to enforce Covid-19 safety measures in a corporate workplace. For this purpose, we develop a virtual assistant platform, Chloe, equipped with automatic temperature sensing, facial recognition, and dedicated chatbots to act as an initial filter for ensuring public health. Whilst providing an engaging user interaction experience, Chloe minimizes human to human contact, thus reducing the spread of infectious diseases. Chloe restricts the employee population within the office to government-approved safety norms. We experimented with Chloe as a virtual safety assistant in a company, where she interacted and screened the employees for Covid-19 symptoms. Participants filled an online survey to quantify Chloe’s performance in terms of interactivity, system latency, engagement, and accuracy, for which we received positive feedback. We performed statistical analysis on the survey results that reveal positive results and show effectiveness of Chloe in such applications. We detail system architecture, results and limitations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Scars of a long battle - A rare case of post covid vanishing lung

Introduction: Vanishing lung syndrome , a primary bullous disease of the lung is defined as a large bulla occupying at least one third of a hemithorax. Usually it associated with riskfactors of smoking, marijuana abuse, alpha 1 antitrypsin deficiency. Here we present a rare case of vanishing lung syndrome developed in a post covid patient without any comorbidities making it a rare presentation. History: A 35year,male with no significant cigaratte smoking presented with acute onset dyspnoea along with dry cough and right sided chest pain for 1 week duration .no history of any recent trauma Past history of COVID 19 one month back, he was hospitilized was put on NIV and HFNC and was discharged on domicillary oxygen and other medications. Clinical Findings: On examination there was hyperresont note in right side along with diminished air entry in all areas in right side along with left side mammary, infraaxillary, infrascapular areas. Diagnosis and Management: Diagnosis was made with the help of contrast enchanced computed tomography aided by other serological and microbilogical workup. Patient was managed conservatively antibiotics ,analgesics and other supportive measures. Learning Points: We are well aware of lung fibrosis post covid , our intention was to throw light into the new entity of bullous lung disease Bullous lung disease (with or without pneumothorax) should be part of differential diagnosis in a patient returning with chest pain and dyspnoea after SARS-Cov-2 infection.CT imaging essential to differentiate radiographically presumed complex pneumothoraces from large bullae to prevent erroneous chest drain insertion into a bulla.

A case series on bullous lung disease in post COVID patients

Introduction: After the aftermath of covid 19 we are left to learn and understand the multiple respiratory manifestations of post covid 19 sequele The presence of bullous lung disease in post covid patients is one such a rare entity ,has been infrequently reported, studied Eventhough the exact mechanism of formation of bullae in post covid 19 are unknown, an emerging association has been observed.A bulla is an air containing space within the lung parenchyma that arises from destruction dilatation and confluence of airspaces distal to terminal bronchioles and is larger than 1 cm in diameter .Its wall are composed of attenuated and compressed parenchyma. Here in this case series we describe this unique presentation of bullous lung diseases in post covid 19 patients. Case Series: Here wepresent a case series of 7 patients without any known comorbidities who were diagnosed with post covid bullous lung disease Diagnosis was made with the help of contrast enchanced computed tomography aided by other serological and microbilogical workup. Patient was managed conservatively antibiotics, analgesics and other supportive measures. Learning Points: We are well aware of lung fibrosis post covid, our intention was to throw light into the new entity of bullous lung disease in post covid period. Bullous lung disease (with or without pneumothorax) should be part of differential diagnosis in a patient returning with chest pain and dyspnoea after SARSCov- 2 infection. CT imaging essential to differentiate radiographically presumed complex pneumothoraces from large bullae to prevent erroneous chest drain insertion into a bulla.

Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries.

As of November 2021, more than 24.80 crore people are diagnosed with the coronavirus in that around 50.20 lakhs people lost their lives, because of this infectious disease. By understanding the people's sentiment's expressed in their social media (Facebook, Twitter, Instagram etc.) helps their governments in controlling, monitoring, and eradicating the coronavirus. Compared to other social media's, the twitter data are indispensable in the extraction of useful awareness information related to any crisis. In this article, a sentiment analysis model is proposed to analyze the real time tweets, which are related to coronavirus. Initially, around 3100 Indian and European people's tweets are collected between the time period of 23.03.2020 to 01.11.2021. Next, the data pre-processing and exploratory investigation are accomplished for better understanding of the collected data. Further, the feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, pre-trained Word2Vec, and fast text embedding's. The obtained feature vectors are fed to the ensemble classifier (Gated Recurrent Unit (GRU) and Capsule Neural Network (CapsNet)) for classifying the user's sentiment's as anger, sad, joy, and fear. The obtained experimental outcomes showed that the proposed model achieved 97.28% and 95.20% of prediction accuracy in classifying the both Indian and European people's sentiments.

Effectiveness of ChAdOx1 nCoV-19 Corona Virus Vaccine (CovishieldTM) in preventing SARS-CoV2 infection, Chennai, Tamil Nadu, India, 2021 (preprint)

Background: India experienced the second wave of the COVID-19 pandemic in March 2021, driven by the delta variant. Apprehensions around the usefulness of vaccines against delta variant posed a risk to the vaccination program. Therefore, we estimated the effectiveness of two doses of the ChAdOx1 nCoV-19 (Covishield) vaccine against COVID-19 infection among individuals [≥]45 years in Chennai, India. Methods: A community-based cohort study was conducted from May to September 2021 in a selected geographic area in Chennai, Tamil Nadu. The estimated sample size was 10,232. We enumerated individuals from all eligible households and periodically updated vaccination and COVID-19 infection data. We computed vaccine effectiveness with its 95% confidence interval for two doses of the Covishield vaccine against any COVID-19 infection. Results: We enrolled 69,435 individuals, of which 21,793 were above 45 years. Two dose coverage of Covishield in the 18+ and 45+ age group was 18% and 31%, respectively. The overall incidence of COVID-19 infection was 1099 per 100,000 population. The vaccine effectiveness against COVID-19 disease in the [≥]45 age group was 61.3% (95% CI: 43.6 - 73.4) at least two weeks after receiving the second dose of Covishield. Genomic analysis of 74 (28 with two doses, 15 with one dose, and 31 with zero dose) out of the 90 aliquots collected from the 303 COVID-19 positive individuals in the 45+ age group showed delta variants and their sub-lineages. Conclusion: We demonstrated the effectiveness of two doses of the ChAdOx1 vaccine against the delta variant in the general population of Chennai. We recommend similar future studies considering emerging variants and newer vaccines. Two-dose vaccine coverage could be ensured to protect against COVID-19 infection.

Commutator Bus Transit Recommendation by Predicting Dynamic Bus Flow Occupancy Using Machine Learning

T Public bus transportation in India plays a major role in short and long-distance. The majority of the population in India depends on the bus daily for going to work, pursuing studies and many other essential purposes. Owing to this the congestion inside the bus increases. In the pandemic situation like COVID social distancing should be maintained and avoid the exchange of money and ticket plays an important factor. Further, the number of passengers within the bus has to fix with a limit that in turn has to plan for increased inefficient transportation. The proposed work implies that upon making the paper-based ticketing system to application-oriented ticket process exchange of money and the ticket can be avoided. Ticket payment by RFID card or scanning QR code can be done that makes travelling by bus much more comfortable during the pandemic situation. Machine learning helps in analyzing the dynamic count of passengers within the bus based on the source and destination a passenger enters inside the bus. Thus the congestion inside the bus predicted to plan the travel.

Measures Taken by Small and Medium Size Organizations to Manage Performance During Covid Crisis Effectively

The value of ensuring an effective management plan for an organisation during a crisis cannot be overemphasised, since it dictates the continued existence of the organisation and averts the possibility of closure of its work process. Its significance lies in the detection, whether from the internal or external environment, of the possible causes of the crisis, as well as the development of an efficient crisis management mechanism that will assist organisation to manage the crisis situation. The study aims to identify the factors that define effective performance management measures during Covid crisis scenario with respect to small and medium size IT and non-IT organizations.It was found that factors that influence the performance during work from home situation are Task and Time Management and the Rewards and Recognition.

Happy hypoxemia: A perplexing clinical entity in Coronavirus disease 2019

The novel pandemic, coronavirus disease 2019 (COVID-19), is a public health exigency of global concern with expanding cases worldwide. Severe acute respiratory syndrome-corona virus-2 is the virus that accounts for COVID-19. The disease manifests with a wide array of symptoms ranging from mild upper respiratory tract infection to severe pneumonia and death. The Happy hypoxemia, synonymously Silent hypoxemia, is described as a clinical entity in individuals with COVID-19, generally expressed as objective hypoxemia lacking respiratory distress symptoms. The condition is characterized by a drop in oxygen saturation and varying arterial blood gas. Intrapulmonary shunting, intravascular microthrombi, loss of lung perfusion control, and impaired diffusion capacity contributes to hypoxemic etiology in COVID-19. The ventilation-perfusion mismatch, covering from shunts to alveolar dead space ventilation, is the principal distinctive feature, suggesting numerous medical goals. © 2022. Chithira V. Nair et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Cancer clinical trial access during the COVID-19 pandemic

Introduction: Cancer clinical trial conduction during the COVID-19 pandemic required a rapid move to virtual engagement to support participant and research team safety. We were faced with the challenge of translating our approach from in-person to virtual engagement for recruitment, enrollment, and delivery of the study intervention. We present our strategies to conduct cancer clinical trials focused on cancer risk reduction during the COVID-19 pandemic. Subject Recruitment: Our multimodal approach utilized online platforms and established approaches like posters, flyers, and collaborating with community health workers to recruit participants. Our virtual engagement strategies include direct outreach to potential participants via email, the electronic medical record (EMR), and social media. Contact via email and the EMR was guided by study-defined eligibility criteria. Social media outreach was through institutional Twitter, Facebook, and video channel accounts. Twitter posts and chats were employed. Enrollment: e-consenting and remote consenting processes were instituted via REDCAP. Delivery of the Educational Risk Reduction Intervention: We also conducted the intervention using the Zoom platform and through a recorded video of the educational risk reduction intervention, which is shared with the participant via REDCAP. Challenges: As documented by others, the greatest challenge to virtual engagement is lack of internet access and lack of digital literacy. These factors have a greater impact on underserved populations, including the elderly, those with low socioeconomic status, those located farther from the cancer center, and racially/ethnically diverse populations. Conclusion: By translating study outreach and processes to virtual engagement, we were able to facilitate clinical trial access across diverse community subgroups and support subject participation in clinical trials during the COVID-19 pandemic.

Photocatalytic assisted microwave-based plasma pyrolyser: A solution for COVID-19 related wastes?

The aim of this mini-review is to explore the potential of indigenous photocatalytic assisted microwave plasma based pyrolyser for COVID-19 related wastes (gloves, masks, bottles, personal protective equipment (PPE), etc.) conversion into liquid oil. Plasma pyrolysis can provide solution for complete pyrolysis of typical hospital wastes not limited to PPE wastes. Literature survey reveals that pyrolysis of polystyrene (PS) plastic waste feedstock can yield up to 80% of pyrolytic oil including styrene, ethylbenzene and toluene with their physico-chemical properties such as density, viscosity, flash point, freezing point, pour point, and high heating values very similar to the conventional diesel and therefore, possess potential as an alternative source for power generation. It is suggested that char produced from pyrolysis has requisite properties for electro-oxidation of CO2. Moreover, if such a system could be integrated with a microwave heating process having absorbents like silicon carbide and different catalyst combinations, up to 32% energy saving could be done in the process that would be rapid, uniform and energy-efficient.

Factors Shopping through m-Commerce of and its Impact on Consumer Purchase Intention: A Covid-19 Pandemic Paradigm

India is the fastest-growing retail market and M-commerce is growing exponentially. However, Covid-19 pandemic has created global disruptions in all spheres and India is no exception. There is a noticeable shift in consumers buying through smartphones leading to increase in online grocery shopping per se and due to fear of vulnerability to pandemic in a physical store. Constructs of perceived equity, perceived usefulness, perceived value and vulnerability to disease have been considered to understand the influence and impact of these aspects on consumer buying intention through smartphones (M-commerce). Descriptive and quantitative research approach was adopted to test the theories of this investigation and a survey was conducted PAN India and data collected through a structured questionnaire from 204 respondents. Data so collected was analysed through factor analysis for identifying key aspects and structural equation modelling (SEM) for developing the model. Findings revealed that vulnerability to disease and perceived equity had the most significant influence on consumer online grocery purchase intention followed by perceived value. The insights can be utilized by various brands to align their M-commerce strategies to consumer preference in the new normal of sustainable business. © 2021 Ecological Society of India. All rights reserved.

Current update on COVID-19 vaccines

Novel Coronavirus, also known as SARS-CoV-2, is an infectious disease that primarily affects the respiratory tract and gastrointestinal tract. The viral spread occurs through respiratory droplets produced while coughing and sneezing. Major vaccine targets for COVID-19 are spike protein, M protein, envelope protein, receptor binding domain, nucleic acids etc. Different mechanisms through which a vaccine can be developed to evoke an immune response include virus inactivated vaccine, live attenuated vaccine, subunit vaccines, virus-like particles, replicating and non-replicating viral vectors and nucleic acid-based vaccines. The mainstay of COVID-19 treatment is supportive and symptomatic therapy with dexamethasone, hydroxychlorquine and anti-viral agents like Darunavir, Ribavirin, Remdesivir, Interferons. Maintaining social distance, personal hygiene, use of N-95 masks aids in limiting the spread of the infection. There is a vital requirement for an ideal and potent vaccine with a favorable beneFit-risk proFile to evoke an immune response against SARS-CoV-2 infection. There are currently 11 approved vaccines in the market of which the PFizer/ BioNTech vaccine have produced the most efFicacy (95%), followed by Moderna (94%), Sputnik (91.6%), Covaxin (90%), Janssen (90%) and Astra Zeneca (70%). Vaccine hesitancy, vaccine-related adverse reactions and the high cost of the vaccine can be a major barrier to public acceptance and global access to the vaccine.

Clinicodemographic profile and outcomes of severe COVID-19 infection in young adults

Introduction: COVID-19 is a respiratory and systemic disorder caused by the SARS-CoV-2 virus with a range of severity from mild respiratory symptoms to severe lung injury, multiorgan failure, and death. The main risk factors of the disease are increased age and underlying comorbidity. Newer reports show that younger patients can also suffer from severe COVID pneumonia of which the data are limited. This study intends to uncover the factors that resulted in severe COVID-19 infection in young adults. Objectives: To study the clinicodemographic profile and outcomes of severe COVID-19 infection in young adults. Materials and methods: This single-center retrospective study included 163 hospitalized patients in the age group 18 to 35 years diagnosed with severe COVID-19 infection at a tertiary care hospital in Uttar Pradesh from July 2020 to November 2020. Details about patient's demographics, clinical features, previous comorbidities, laboratory and radiological investigations, and hospital outcomes were obtained from patient records and analyzed. Results: Out of 163 patients, 60.1% (98) were males and 39.8% (65) were females. The most common comorbidity was diabetes 68.7%, hyperlipidemia 33.1% and obesity 32%. 30.6.% (51) of patients were smokers. Patients presented with shortness of breath (66.9%), cough (65.6%) and fever (60.7%) respectively. Multilobe infiltrates were found in chest xray of (75.4%) patients,. Mean length for ICU stay was 15.5 days (range 3-46). Mechanical ventilation was required in 26.9% of patients .In patients requiring mechanical ventilation, 17 (38%) were discharged and 27(62.8%) died. Of the mechanically ventilated patients 44 had abnormal BMI. Overall mortality was 27% (37patients). Discussions: There was a male sex predominance with diabetes. Obesity, smoking, and hyperlipidemia were the major risk factors. The major presenting symptoms in these patients were shortness of breath, cough, and fever. Only a quarter of patients required mechanical ventilation, and in those obesity was found to be a major risk factor. Conclusion: Our study provides insight into presenting characteristics, demographics, and overall outcomes of severe COVID-19 infection in young adults. The preconceived notion of COVID-19 being a disease of the elderly should be changed. In medical emergencies like the COVID pandemic, it is very important to analyze patient demographics to identify the population at risk. Such knowledge not only allows us to produce strategies to help control the spread of disease but also helps us to risk stratify to prevent mortality. It is crucial to learn from an epidemic like this so we can be better prepared for the future.