ABSTRACT
Background: Healthcare workers at the forefront of the battle against COVID 19 are not only putting their own health and lives at risk but are also fighting to keep their own worries and emotional stress at bay. Aim & Objective: To evaluate emotions, perceived stressors, and factors that helped in reducing stress of healthcare workers who worked during a COVID19 pandemic. Settings and Design: This cross sectional study was conducted among Health Care staff involved in COVID 19 duty in tertiary care hospital of Gujarat. Methods and Material: Google form link was shared though what's up and mail. The questionnaire was completed online. Consent for voluntarily participation was also obtained through online Google form. Statistical analysis used: Data was entered and analysed through Microsoft Excel 2010. Results: Total 106 participants responded to the questionnaire. It was extremely stressful for health care workers to see their colleagues getting infection, as well as the fear that they could transmit the disease to their families or friends. Main factors that helped to reduce the stress were positive attitude from colleagues, improvement of patients conditions and availability of protective equipment. Conclusions: Personal safety, the protection of family members and unpredictability of pandemic were the main concerns. Hospitals should prioritise stress monitoring for health care workers and provide targeted psychological guidance for HCWs during the pandemic. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.
ABSTRACT
Information content that is inaccurate, misleading, or whose source cannot be verified is fake news. This content could be created to purposely harm people’s reputations, deceive them, or draw attention to themselves. Since December 2019, the epidemic of coronavirus disease has sparked considerable alarm and has had a significant impact on people’s lives. Also, misinformation on COVID-19 is frequently spread on social media. This project aims to use Machine learning algorithms to recognize fraudulent news. For this, we use seven essential algorithms, namely Logistic regression, Naïve Bayes, Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbours (KNN), Decision tree, and Random forest. We compared the results of all the algorithms stated above and found that neural networks and random forest achieved the highest accuracy of 83%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.
Subject(s)
COVID-19 , COVID-19 Vaccines , Fever , Humans , Prospective Studies , SARS-CoV-2ABSTRACT
We propose an efficient method for simulating a cryo-Electron Tomography (cryo-ET) image of a target macromolecule with several neighbor macromolecules packed to achieve a realistic crowded cytoplasm content. The simulated results are subtomograms with corresponding noise-free 3D density maps and pre-specified labels (PDB ID, center locations, and orientations) to assist bioimage analysis. They can serve as benchmark datasets for testing developing cryo-ET analysis algorithms and as training datasets with readily available ground truth labels for learning neural network models. The COVID-19 pandemic has sparked a global health crisis that severely impacting lives worldwide. As an important application, we simulated the scene of SARS-CoV-2 interacting with the host cell. The simulated cryo-ET images clearly showed the binding domain of the virus and the host cell to facilitate the research of SARS-CoV-2' infection. We also trained two different classification models to demonstrate that our simulated cryo-ET data is able to assist the cryo-ET analysis task and to validate the performance between different methods. © 2020 IEEE.
ABSTRACT
Introduction: COVID 19 is a pandemic disease caused by novel coronavirus called SARS-CoV- 2. End Stage Renal Disease patients are at high risk for developing severe manifestations of the disease often associated with high morbidity and mortality. Excessive and uncontrolled immune response is thought to be one of the important underlying mechanism for severity of the disease. We present 3 ESRD patients with underlying vasculitides who were admitted with respiratory distress due to COVID 19. Case Description: All 3 patients presented with shortness of breath and had typical features of COVID 19 including hypoxia, fever;extensive bilateral interstitial infiltrates on Chest X rays, lymphocytopenia, elevated LDH;and ferritin. The first patient is a 37-year-old Hispanic male with ANCA -PR3 related vasculitis resulting in ESRD, on hemodialysis. He had been treated with Cyclophosphamide and prednisone induction therapy and is on maintenance prednisone. The second patient is a 40-year-old male with ESRD secondary to crescentic IgA nephropathy. He had been treated with cyclophosphamide and prednisone induction. The third patient is a 43-year-old female with SLE;ESRD secondary to lupus nephritis. She had been treated with cyclophosphamide and prednisone induction and is on maintenance prednisone. All 3 patients recovered in the hospital with oxygen supplementation and did not require NIV or intubation. Discussion: We hypothesize that due to residual immunosuppressive action of cyclophosphamide, the inflammatory response in these patients was probably blunted. And this could have led to better outcome in these patients. Due to lots of unknowns related to COVID 19, further prospective/retrospective studies should be done looking at outcomes of COVID 19 in patients who had received cyclophosmide previously.
ABSTRACT
OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.