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Introduction Without a comprehensive postmortem investigation it is impossible to determine the cause of death among the SARS-CoV-2-suspected and-positive patients. We present two cases to discuss the postmortem detectability of SARS-CoV-2 virus and RNA stability in biological samples. Outline of cases Case No. 1: a 40-year-old man on whom the autopsy was performed four days after death. The body was stored at 4°C. Bilateral pneumonia was confirmed grossly and histopathologicaly. Molecular testing was positive for IgM antibodies, but negative for SARS-CoV-2 RNA. Case No. 2: a 28-year-old profes-sional basketball player who suffered from SARS-CoV-2 about a month earlier. The autopsy was performed two days after death. The body was stored at 15°C. Gross autopsy findings revealed advanced putrefactive changes and an enlarged heart, with visible fibrotic focuses. The histopathological finding corresponded to the sudden cardiovascular death due to the cardiac dysrhythmia most probably formed in one of the fibrotic focuses. Tests for SARS-CoV-2 RNA and antibodies (IgM, IgG) were positive in the analyzed samples. Conclusion This report suggests that SARS-CoV-2 virus can be isolated in the biological samples even after a long post-mortem prolongation of molecular analyses. We emphasize the necessity of wider studies that will define the infectiveness and biological stability of the virus in postmortem tissues. © 2023, Serbia Medical Society. All rights reserved.
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The appearance of COVID-19 changed the lifestyle of many people as it spread rapidly around the world, causing concern to the entire health system due to the high number of infected and leading to a general confinement, changing the lifestyle and eating habits of many people causing diabetes, which is a disease caused by the high level of glucose in the blood, which can generate serious problems in the health of the person since it has no cure, this progressive disease is controlled or monitored by conventional glucometer equipment that generates pain in patients because they require blood samples to measure glucose, worse for those diabetics who must have the measurement several times a day. In view of this problem, this article will make a portable blood glucose meter system for the self-monitoring of diabetic patients and determine the blood sugar level to visualize it by means of a screen, with this system the measurement will be made without pain and will show the value of the glucose level accurately, Helping diabetic patients who perform monitoring several times a day. Through the development of l system, it was observed that it works in the best way with an efficiency of 96.97% in the measurement of glucose, when comparing with others equipment glucometers obtained a relative error of 2.99%, being an error accepted to approach the real value. © 2023 IEEE.
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The main objective of this study was to investigate the dynamic of SARS-CoV-2 viral excretion in rectal swab (RS), saliva, and nasopharyngeal swab (NS) samples from symptomatic patients and asymptomatic contacts. In addition, in order to evaluate the replication potential of SARS-CoV-2 in the gastrointestinal (GI) tract and the excretion of infectious SARS-CoV-2 from feces, we investigated the presence of subgenomic nucleoprotein gene (N) mRNA (sgN) in RS samples and cytopathic effects in Vero cell culture. A prospective cohort study was performed to collect samples from symptomatic patients and contacts in Rio de Janeiro, Brazil, from May to October 2020. One hundred and seventy-six patients had samples collected at home visits and/or during the follow up, resulting in a total of 1633 RS, saliva, or NS samples. SARS-CoV-2 RNA was detected in 130 (73.9%) patients who had at least one sample that tested positive for SARS-CoV-2. The presence of replicating SARS-CoV-2 in RS samples, measured by the detection of sgN mRNA, was successfully achieved in 19.4% (6/31) of samples, whilst infectious SARS-CoV-2, measured by the generation of cytopathic effects in cell culture, was identified in only one RS sample. Although rare, our results demonstrated the replication capacity of SARS-CoV-2 in the GI tract, and infectious viruses in one RS sample. There is still a gap in the knowledge regarding SARS-CoV-2 fecal-oral transmission. Additional studies are warranted to investigate fecal or wastewater exposure as a risk factor for transmission in human populations.
Subject(s)
COVID-19 , Communicable Diseases , Humans , COVID-19/diagnosis , COVID-19/epidemiology , SARS-CoV-2/genetics , RNA, Viral/genetics , Brazil/epidemiology , Prospective StudiesABSTRACT
This project aims to devise an alternative for Coronavirus detection using various audio signals. The aim is to create a machine-learning model assisted by speech processing techniques that can be trained to distinguish symptomatic and asymptomatic Coronavirus cases. Here the features exclusive to the vocal cord of a person is used for covid detection. The procedure is to train the classifier using a data set containing data of people of various ages both infected and disease-free, including patients with comorbidities. We presented a machine learning-based Coronavirus classifier model that can separate Coronavirus positive or negative patients from cough, breathing, and speech recordings. The model was trained and evaluated using several machine learning classifiers such as Random Forest Classifier, Logistic Regression (LR), Decision Tree Classifier, k-nearest Neighbour (KNN), Naive Bayes Classifier, Linear Discriminant Analysis, and a neural network. This project helps track COVID-19 patients at a low cost using a non-contactable procedure and reduces the workload on testing centers. © 2023 IEEE.
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SARS-CoV-2 has been detected both in air and on surfaces, but questions remain about the patient-specific and environmental factors affecting virus transmission to the environment. Additionally, more detailed information on viral findings in the air is needed. In this cross-sectional study, we present results from 259 air and 252 surface samples from the surroundings of 23 hospitalized and eight home-treated COVID-19 patients between July 2020 and March 2021 and compare the results between the measured environments and patient factors. In four cases, positive environmental samples were detected even after the patients had developed a neutralizing IgG response. SARS-CoV-2 RNA was detected in multiple particle sizes and different air samplers. Appropriate infection control against airborne and surface transmission routes is needed in both environments, even after antibody production has begun. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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Uniform practices and quality control methods are needed to detect and quantify airborne viruses across sampling and analysis platforms. We compared detection of airborne SARSCoV-2 RNA in residences of individuals with COVID-19 using two commonly used criteria: environmental (at least one SARS-CoV-2-specific gene and internal control amplified by PCR with Ct ≤ 40) and clinical (at least two SARS-CoV-2-specific genes and internal control amplified with Ct ≤ 37). 24-hr total aerosol samples were collected in a self-isolation room and an additional room without manipulating subjects' behavior/activities. Under the environmental criterion, 7/16 samples in primary rooms and 7/15 samples in secondary rooms were positive. Comparable but lower positive sample proportions were observed using the more rigorous clinical criterion: 6/16 primary rooms and 5/15 secondary rooms. A consensus SARS-CoV-2 environmental sampling and analysis framework is needed for comparisons between studies. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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Objective To compare the performance of two qPCR instruments in detecting SARS-CoV-2 virus in the nasopharyngeal swab samples of suspected COVID-19 isolated individuals in Jinghu district Wuhu city.Methods A total of 151 nasopharyngeal swab samples were collected from individuals with suspected COVID-19isolated during January 2021 and July 2022 at a quarantine site in the Jinghu district. Nucleic acid of SARS-CoV-2virus was quantified parallelly using ABIQ5 real-time fluorescence quantitative analyzer（Q5 analyzer） and Bole CFX96 fluorescence quantitative PCR analyzer（Bole analyzer） in the laboratory. Q5 analyzer was used as the reference instrument, while Bole analyzer was used as an experimental instrument. The detection results of N gene, ORF1ab fragment and CT value of the two RT-PCR machines were analyzed and compared using paired four grid test, Spearman test and paired sample t-test in SPSS 22 statistical software. Results The results of 151samples for different target genes tested by two instruments were in good agreement（N gene: Kappa=1, P＜0. 05;ORF1ab fragment: Kappa=0. 972, P＜0. 05）. The inter-batch repeatability rates were 4. 01% and 3. 04%for N gene and ORF fragment of the same batch positive quality controls by Q5 analyzer, and were 4. 90% and 3. 57% by Bole analyzer. The intra batch repeatability rates of the two instruments at different hole locations were similar, and CV values were less than 3%. The results of 23 positive samples showed that the differences in CT values of N gene（29. 38±7. 22） and ORF1ab（30. 83±6. 27） detected by Q5 analyzer were statistically significant（t=2. 765, P＜0. 05）, while the differences in CT values of N gene（29. 58±7. 27） and ORF1ab（30. 77±8. 02） detected by Bole analyzer were not statistically significant（t=1. 753, P＞0. 05）. The correlation coefficients of CT values of different target genes detected by the two instruments were rN=0. 960 and rORF=0. 865, showing correlated CT values（P＜0. 05）. Conclusion The CT values of N gene and ORF1ab fragment of SARS-CoV-2 virus detected by the two instruments have strong correlation and agreement, indicating that either of the instrument can be used for laboratory sample detection and analysis. The repeatability of Q5 analyzer is better than that of Bole analyzer. The detection stability of ORF fragments of both instruments is better than that of N gene, and the detection sensitivity of Q5 analyzer for N gene is higher than that for ORF fragment. The sample tubes should be placed in the middle of the PCR machine in order to reduce the system error.
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Infectious uveitis is a vision-threatening condition that requires prompt clinical diagnosis and proper treatment. However, rapid and proper diagnosis in infectious uveitis remains challenging. Several examination tests, including polymerase chain reaction (PCR) tests, are transitioning from laboratory-based basic research-level tests to bedside clinical tests, and recently tests have changed to where they can be performed right next to clinicians. In this review, we introduce an updated overview of recent studies that are representative of the current trends in clinical microbiological techniques including PCR tests for infectious uveitis.
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Communicable Diseases , Eye Infections, Bacterial , Uveitis , Humans , Eye , Polymerase Chain Reaction/methods , Uveitis/diagnosis , Uveitis/microbiology , Communicable Diseases/diagnosis , Vision DisordersABSTRACT
Rivers are our country's lifeline;however, we have done enough destruction to them which leads to deterioration in water quality. Fortunately, COVID-19 lockdown has brought new life to nature. This encouraged us to outline present review article which discusses pilot impacts of lockdown on six Indian rivers. Few rivers including Ganga showed major improvement at few sites in the assessed parameters such as pH, BOD, DO, FC, etc. The Ganga water at Haridwar and Rishikesh was investigated `fit for drinking' (Class A) while at Kanpur was found fit for `outdoor bathing' (Class B). These improvements can be attributed to strict restriction on human activities during lockdown as there were no or minimum industrial discharge, tourism activities, mass bathing and commercial events near rivers. However, after upliftment of lockdown, these activities will return to their previous state and most likely pollutants will eventually reappear in the water bodies. So, in this review we have reviewed government's existing water pollution control schemes, analysed their limitations and recommended several scopes for improvement. Further research directions in this area have also been highlighted. We believe that plans and actions described in the article, if implemented, will lead to fruitful outcomes in managing water resources.
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Herein, we have proposed a straightforward and label-free electrochemical immunosensing strategy supported on a glassy carbon electrode (GCE) modified with a biocompatible and conducting biopolymer functionalized molybdenum disulfide-reduced graphene oxide (CS-MoS2/rGO) nanohybrid to investigate the SARS-CoV-2 virus. CS-MoS2/rGO nanohybrid-based immunosensor employs recombinant SARS-CoV-2 Spike RBD protein (rSP) that specifically identifies antibodies against the SARS-CoV-2 virus via differential pulse voltammetry (DPV). The antigen-antibody interaction diminishes the current responses of the immunosensor. The obtained results indicate that the fabricated immunosensor is extraordinarily capable of highly sensitive and specific detection of the corresponding SARS-CoV-2 antibodies with a LOD of 2.38 zg mL-1 in phosphate buffer saline (PBS) samples over a broad linear range between 10 zg mL-1-100 ng mL-1. In addition, the proposed immunosensor can detect attomolar concentrations in spiked human serum samples. The performance of this immunosensor is assessed using actual serum samples from COVID-19-infected patients. The proposed immunosensor can accurately and substantially differentiate between (+) positive and (-) negative samples. As a result, the nanohybrid can provide insight into the conception of Point-of-Care Testing (POCT) platforms for cutting-edge infectious disease diagnostic methods.
Subject(s)
Biosensing Techniques , COVID-19 , Graphite , Metal Nanoparticles , Humans , Molybdenum , Biosensing Techniques/methods , COVID-19/diagnosis , Immunoassay/methods , SARS-CoV-2 , Electrochemical Techniques/methodsABSTRACT
The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.
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Introduction: The most widely used marker for the diagnosis of invasive aspergillosis (IA) is the detection of galactomannan by ELISA. This study describes the evaluation of the results obtained by Euroimmun Aspergillus antigen ELISA (EIA-GM-E) in serum samples and bronchoalveolar lavage fluid (BAL) from patients at risk of IA, and compares these results with those obtained by Bio-Rad Galactomannan EIA (EIA-GM-BR). Method(s): Anonymous retrospective case-control comparative study in 64 serum samples and 28 BAL from 51 patients. Result(s): Overall agreement of the results of the two assays was observed in 72 of 92 samples (78.3%). The sensitivity of EIA-GM-BR and EIA-GM-E in serum samples was 88.9% and 43.2%, respectively, and 100% and 88.9% for BAL. The specificity of EIA-GM-BR and EIA-GM-E in serum samples was 91.9% for both assays, and 68.4% and 84.2% in BAL. There were no statistically significant differences in the results of both assays. Conclusion(s): Both methods show good results for the discrimination of patients with IA when BAL is tested, or serum in case of EIA-GM-BR.Copyright © 2021 Sociedad Espanola de Enfermedades Infecciosas y Microbiologia Clinica
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With the proliferation of COVID-19 cases, it has become indispensable to conceive of innovative solutions to abate the mortality count due to the pandemic. With a steep rise in daily cases, it is a known fact that the current testing capacity is a major hindrance in providing the right healthcare for the individuals. The common methods of detection include swab tests, blood test results, CT scan images, and using cough sounds paired with AI. The unavailability of data for the application of deep learning techniques has proved to be a major issue in the development of deep learning-enabled solutions. In this work, a novel solution of a screening device that is capable of collecting audio samples and utilizing deep learning techniques to predict the probability of an individual to be diagnosed with COVID-19 is proposed. The model is trained on public datasets, which is to be manually examined and processed. Audio features are extracted to create a dataset for the model which will be developed using the TensorFlow framework. The trained model is deployed on an ARM CortexM4 based nRF52840 microcontroller using the lite version of the model. The in-built PDM-based microphone is to be used to capture the audio samples. The captured audio sample will be used as an input for the model for screening. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This research endeavor is focused on identifying patients with the Covid-19 virus via the use of a novel voice recognition technique that makes use of a Support Vector Machine (abbreviated as 'SVM') and compares its accuracy with that of 'K-Nearest Neighbor' (abbreviated as 'KNN'). When it comes to speech recognition, the SVM method is regarded to be group 1, and the KNN method is considered to be group 2, and both groups have a total of 20 samples. The outcomes of these data were analyzed using statistical analysis using a'independent sample T-test,' which has a margin of error of 5% and a pretest power of 80%. At a significance of 0.042 (p 0.05), KNN obtains an accuracy of 87.5% whereas SVM achieves an accuracy of 96.5%. As compared to KNN, the prediction accuracy of Covid-19 employing SVM in novel voice recognition achieves much higher levels of accuracy. © 2023 IEEE.
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Representative school data on SARS-CoV-2 past-infection are scarce, and differences between pupils and staff remain ambiguous. We performed a nation-wide prospective seroprevalence study among pupils and staff over time and in relation to determinants of infection using Poisson regression and generalised estimating equations. A cluster random sample was selected with allocation by region and sociodemographic (SES) background. Surveys and saliva samples were collected in December 2020, March, and June 2021, and also in October and December 2021 for primary pupils. We recruited 885 primary and 569 secondary pupils and 799 staff in 84 schools. Cumulative seroprevalence (95% CI) among primary pupils increased from 11.0% (7.6; 15.9) at baseline to 60.4% (53.4; 68.3) in December 2021. Group estimates were similar at baseline; however, in June they were significantly higher among primary staff (38.9% (32.5; 46.4)) compared to pupils and secondary staff (24.2% (20.3; 28.8)). Infections were asymptomatic in 48-56% of pupils and 28% of staff. Seropositivity was associated with individual SES in pupils, and with school level, school SES and language network in staff in June. Associations with behavioural characteristics were inconsistent. Seroconversion rates increased two- to four-fold after self-reported high-risk contacts, especially with adults. Seroprevalence studies using non-invasive sampling can inform public health management.
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COVID-19 , SARS-CoV-2 , Saliva , Adult , Humans , COVID-19/epidemiology , Prospective Studies , Schools , Seroepidemiologic Studies , Saliva/virologyABSTRACT
Introduction: The ongoing 2019 coronavirus disease pandemic (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants, is a global public health threat. Early diagnosis and identification of SARS-CoV-2 and its variants plays a critical role in COVID-19 prevention and control. Currently, the most widely used technique to detect SARS-CoV-2 is quantitative reverse transcription real-time quantitative PCR (RT-qPCR), which takes nearly 1 hour and should be performed by experienced personnel to ensure the accuracy of results. Therefore, the development of a nucleic acid detection kit with higher sensitivity, faster detection and greater accuracy is important. Methods: Here, we optimized the system components and reaction conditions of our previous detection approach by using RT-RAA and Cas12b. Results: We developed a Cas12b-assisted one-pot detection platform (CDetection.v2) that allows rapid detection of SARS-CoV-2 in 30 minutes. This platform was able to detect up to 5,000 copies/ml of SARS-CoV-2 without cross-reactivity with other viruses. Moreover, the sensitivity of this CRISPR system was comparable to that of RT-qPCR when tested on 120 clinical samples. Discussion: The CDetection.v2 provides a novel one-pot detection approach based on the integration of RT-RAA and CRISPR/Cas12b for detecting SARS-CoV-2 and screening of large-scale clinical samples, offering a more efficient strategy for detecting various types of viruses.
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We have designed, fabricated, and tested a MEMS-based impedance biosensor for accurate and rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) using of clinical samples. The device consists of focusing region that concentrate low quantities of the virus present in the samples to a detectable threshold, trap region hat maximize the captured virus, and detection region to detect the virus with high selectivity and sensitivity, using an array of interdigitated electrodes (IDE) coated with a specific antibody. Changes in the impedance value due to the binding of the SARS-COV-2 antigen to the antibody will indicate positive or negative result. The device was able to detect inactivated SARS-COV-2 antigen present in phosphate buffer saline (PBS) with a concentration as low as 50 TCID50/ml in 30 minutes. In addition, the biosensor was able to detect SARS-COV-2 in clinical samples (swabs) with a sensitivity of 84 TCID50/ml, also in 30 minutes. © 2023 IEEE.
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We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more than 200 geographical areas since early April 2020 and recorded 6% and 11% two-week, out-of-sample median mean absolute percentage error on predicting cases and deaths, respectively. DELPHI compares favorably with other top COVID-19 epidemiological models and predicted in 2020 the large-scale epidemics in many areas, including the United States, United Kingdom, and Russia, months in advance. We further illustrate two downstream applications of DELPHI, enabled by the model's flexible parametric formulation of the effect of government interventions. First, we quantify the impact of government interventions on the pandemic's spread. We predict, that in the absence of any interventions, more than 14 million individuals would have perished by May 17, 2020, whereas 280,000 deaths could have been avoided if interventions around the world had started one week earlier. Furthermore, we find that mass gathering restrictions and school closings were associated with the largest average reductions in infection rates at 29.9±6.9% and 17.3±6.7% , respectively. The most stringent policy, stay at home, was associated with an average reduction in infection rate by 74.4±3.7% from baseline across countries that implemented it. In the second application, we demonstrate how DELPHI can predict future COVID-19 incidence under alternative governmental policies and discuss how Janssen Pharmaceuticals used such analyses to select the locations of its Phase III trial for its leading single-dose vaccine candidate Ad26.Cov2.S.
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The COVID-19 pandemic is accompanied by intensive attempts to build mathematical models to predict it. For this, various models are used, both traditional differential equations and machine learning models. Classical epidemiological compartment models contain parameters that are difficult to measure. Their results are used to model various scenarios, but it is difficult to obtain a reliable forecast with their help. Machine learning models, on the other hand, do not use prior assumptions, and their inferences are based only on training samples. This usually results in more reliable forecasts. In both the first and second cases, it is necessary not only to estimate the forecast error, but to compare the prediction accuracy of different models by checking the error homogeneity also. An additional factor complicating the problem is the small size of available samples in some cases. This forces one to resort to resampling methods. The article describes the Klyushin-Petunin test for testing the homogeneity of samples with ties in a multi-sample design and compares it with the traditional Anderson-Darling, Kruskal-Wallis and Friedman tests using the example of three methods for predicting the COVID-19 epidemic in the basis of epidemic data in Germany, Japan, South Korea and Ukraine. © 2022 IEEE.
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A venture creation programme (VCP) is an academic programme in which students' creation of a new entrepreneurial venture is a central vehicle for learning. A VCP puts students in the role of entrepreneurs with real opportunities and challenges. The entrepreneurial journey is a bumpy ride, and COVID-19 has added significant challenges for entrepreneurs, including students in VCPs. Previous research emphasises how entrepreneurial learning occurs through handling entrepreneurial challenges. The purpose of the present paper is to investigate the role of COVID-19induced challenges in VCP students' learning. We applied fuzzy-set qualitative comparative analysis (fsQCA) to data from students in a technology-oriented VCP in Scandinavia, collected in April 2021. FsQCA offers the opportunity to investigate complex logic combinations of factors that explain an outcome and is particularly suited for small samples. Multi-item measures assessed (1) the progress of students' ventures, (2) entrepreneurial learning and (3) perceived challenges from COVID-19. We also asked whether students had entered or exited an entrepreneurial project and whether these projects were run by a team or only the individual student. We found that COVID-19-induced challenges impeded VCP students' learning and that students' individual progress was important for learning during crisis situations. Thus, entrepreneurship educators should help students get 'back on the horse-which means being involved in new entrepreneurial projects-if their challenges lead them into stagnation and inactivity. Progress, both in students' ventures and for students as individuals, should be nurtured by entrepreneurship educators. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.