[Evaluation of the dynamics of detection of viable SARS-CoV-2 (Coronaviridae: Betacoronavirus: Sarbecovirus) in biological samples obtained from patients with COVID-19 in a health care setting, as one of the indicators of the infectivity of the virus].

INTRODUCTION: The study of the mechanisms of transmission of the SARS-CoV-2 virus is the basis for building a strategy for anti-epidemic measures in the context of the COVID-19 pandemic. Understanding in what time frame a patient can spread SARS-CoV-2 is just as important as knowing the transmission mechanisms themselves. This information is necessary to develop effective measures to prevent infection by breaking the chains of transmission of the virus. The aim of the work is to identify the infectious SARS-CoV-2 virus in patient samples in the course of the disease and to determine the duration of virus shedding in patients with varying severity of COVID-19. MATERIALS AND METHODS: In patients included in the study, biomaterial (nasopharyngeal swabs) was subjected to analysis by quantitative RT-PCR and virological determination of infectivity of the virus. RESULTS: We have determined the timeframe of maintaining the infectivity of the virus in patients hospitalized with severe and moderate COVID-19. Based on the results of the study, we made an analysis of the relationship between the amount of detected SARS-CoV-2 RNA and the infectivity of the virus in vitro in patients with COVID-19. The median time of the infectious virus shedding was 8 days. In addition, a comparative analysis of different protocols for the detection of the viral RNA in relation to the identification of the infectious virus was carried out. CONCLUSION: The obtained data make it possible to assess the dynamics of SARS-CoV-2 detection and viral load in patients with COVID-19 and indicate the significance of these parameters for the subsequent spread of the virus and the organization of preventive measures.

Evaluation with Roc Analysis, Logistics Regression Analysis and Other Statistical Tests Analysis of Retrospective Results of Biochemistry Laboratory Results, Analysised in Covid-19 Infected Serum or Plasma (Crp, Procalcitonin, Ferritin, D-Dimer, Fibrinogen, Ast, Alt, Ldh)

Objectives: A new type of coronavirus that emerged in Wuhan, China at the end of 2019, caused the Covid-19 (SARS-COV2) pandemic. Common cold symptoms are seen, but in more severe cases, pneumonia, Acute Respiratory Distress Syndrome (ARDS), coagulopathy, multi-organ failure are seen, and it causes death in the course of time. In this study, among the laboratory parameters followed in cases diagnosed with Covid-19 and followed in home isolation, service and intensive care unit;It is aimed to retrospectively evaluate CRP, procalcitonin, ferritin, D-Dimer, fibrinogen AST, ALT and LDH levels with ROC and other statistical analyzes in terms of predicting mortality in the treatment and follow-up of the disease. Materials-Methods: Between 01.04.2020 and 01.10.2020, the patients who applied to Necmettin Erbakan University Meram Medical Faculty Hospital with cold symptoms and were diagnosed with Covid-19 with RT-PCR positivity, were analyzed from Covid-19 infected serum and plasma. The results of the biomarkers were examined. Demographic data, vital signs and laboratory findings of the cases were compared. The results were statistically evaluated with the SPSS 22.0 package program. Result(s): 300 cases who received home isolation, service and supportive treatment in the intensive care unit were included in the study. Crp, Pct, D-dimer, ferritin, fibrinogen, LDH, AST and ALT values were found to be statistically significant. According to the results of ROC (Receiver Operating Characteristic) analysis performed to determine the predictive values of laboratory parameters that were significant as a result of univariate statistical analysis, Crp (0.890775), Pct (0.86795), D-dimer (0.856975), ferritin (0.836975), LDH (0.7829), fibrinogen (0.773925), AST (0.685925) and ALT (0.594025) were found. Conclusion(s): The high mutation ability of SARS-CoV-2 makes it difficult to control the pandemic. Therefore, early diagnosis of the disease has gained importance for the treatment of patients with high mortality risk. According to the ROC results we obtained in this study, it supports that CRP, Procalcitonin, Ferritin, D-dimer and LDH levels can be used as effective parameters in determining the prognosis and mortality risk in Covid-19 patients.

A Deep Net Model for Covid-19 Detection Based on ROC Analysis Using Radiographs

The European Centre of Disease Prevention & Control's analytical statistics show that the new corona virus (Covid-19) is rapidly spreading amongst millions of people & causing the deaths of thousands of them. Despite the daily increase in cases, there are still a finite quantity of Covid-19 test kits available. The use of an automatic recognition system is crucial for the diagnosis and control of Covid-19. Three important Inception-ResNetV2, InceptionV3, & ResNet50 models of convolutional neural networks are utilized to detect the Corona Virus in lung X-ray radiography. The ResNet50 version has the best result & accuracy rate of the present system. As compared to the current models, a novel procedures and ensuring on the CNN model delivers better specific, sensitivities, and precision. By using confusion matrix and ROC assessment, fivefold validation data is utilized to analyze the current models and compare them to the proposed system. © 2023 IEEE.

COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs. MATERIALS AND METHODS: Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values. RESULTS: A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78). CONCLUSION: Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

Prediction of fatal outcome in patients with confirm COVID-19

SARS-CoV-2, the new coronavirus underlying the development of the COVID-19 pandemic, has led to a sharp increase in the burden on healthcare systems, high mortality and significant difficulties in organizing medical care. The aim of the study was to conduct a systematic analysis of factors affecting the course of infectious disease in patients with diagnosed COVID-19 hospitalized. In order to predict the course of the disease and determine the indications for more aggressive treatment, many different clinical and biological markers have been proposed, however, clinical and laboratory assessment of the condition is not always simple and can clearly predict the development of a severe course. Technologies based on artificial intelligence (AI) have played a significant role in predicting the development of the disease. One of the main requirements during a pandemic is an accurate prediction of the required resources and likely outcomes. In the present study, a machine learning (ML) approach is proposed to predict the fatal outcome in patients with an established diagnosis of COVID-19 based on the patient’s medical history and clinical, laboratory and instrumental data obtained in the first 72 hours of the patient’s stay in the hospital. A machine learning algorithm for predicting the lethal outcome in patients with COVID-19 during 72 hours of hospitalization demonstrated high sensitivity (0.816) and specificity (0.865). Given the serious concerns about limited resources, including ventilators, during the COVID-19 pandemic, accurately predicting patients who are likely to require artificial ventilation can help provide important recommendations regarding patient triage and resource allocation among hospitalized patients. In addition, early detection of such persons may allow for routine ventilation procedures, reducing some of the known risks associated with emergency intubation. Thus, this algorithm can help improve patient care, reduce patient mortality and minimize the burden on doctors during the COVID-19 pandemic. © 2022, ITMO University. All rights reserved.

Firms' solidity before an exogenous shock: Covid-19 pandemic in Italy.

In this paper we study the structural robustness of the Italian business system, using the Covid-19 pandemic as an exogenous event to test it. To this aim, we use the ROC (Receiver Operating Characteristics) methodology, quite new for economics, to classify Italian firms according to their economic solidity, obtaining a taxonomy based on a wide set of characteristics. Our results show that the number of "Solid" firms is less than one-fifth of all Italian enterprises but they represent the lion's share in terms of employment and value added. "Fragile" and "At Risk" firms, albeit much less relevant for the creation of value added, account for over one-third of total employment, so they may be a worrisome issue for policymakers. Solidity conditions have clearly both a size and sector-related dimension: At Risk and Fragile conditions prevail among firms of smaller economic size (a broad definition of firm size) and among those operating in Construction and Other services. Finally, we find that factors such as firms' performance, and internal and external organization, although significant, play a less relevant role than economic size and digitalization/innovation in determining Italian firms' resilience to exogenous shocks such as the Covid-19 one.

Sequential Organ Failure Assessment (SOFA) Score and Mortality Prediction in Patients With Severe Respiratory Distress Secondary to COVID-19.

Background This study looks at the validity of the sequential organ failure assessment score (SOFA) in detecting mortality in patients with Coronavirus disease of 2019 (COVID-19) pneumonia. Also, it is looking to determine the optimal SOFA score that will discriminate between mortality and survival. Methods It is a retrospective chart review of the patients admitted to Henry Ford Hospital from March 2020 to December 2020 with COVID-19 pneumonia who developed severe respiratory distress. We collected the following information; patient demographics (age, sex, body mass index), co-morbidities (history of diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease, coronary artery disease, or cancer), SOFA scores (the ratio of arterial oxygen tension (PaO2) to the fraction of inspired oxygen, Glasgow Coma Scale (GCS) score, mean arterial pressure, serum creatinine level, bilirubin level, and platelet count) as well as inpatient mortality. Results There were 320 patients; out of these, 111 were intubated. The receiver operating characteristic (ROC) curve for SOFA at the moment of inclusion in the study had an area under the curve of 0.883. The optimal point for discrimination between mortality and survival is SOFA of 5. A SOFA score of less than two is associated with 100% survival, while a score of more than 11 is associated with 100% mortality. Conclusions SOFA score in COVID-19 patients with severe respiratory distress strongly correlates with the initial SOFA score. It is a valuable tool for predicting mortality in COVID-19 patients.

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

NEWS2, 4C Mortality Score, COVID-GRAM, Sequential Organ Failure Assessment Quick scales as outcomes assessment tools for severe COVID-19 (pilot retrospective cohort study).

To study the predictive ability of the NEWS2, 4C Mortality Score, COVID-GRAM and qSOFA scales in predicting clinical outcomes in patients with severe coronavirus disease 2019 (COVID-19) hospitalized in a multidisciplinary hospital. Material and methods. The pilot retrospective cohort study used data from 90 patients (52 -- intensive care unit subgroup, 38 -- general unit subgroup) with a confirmed diagnosis of COVID-19 hospitalized in the O. M. Filatov City Clinical Hospital № 15 (Moscow) from January to March 2021. Results. The probability of a positive outcome of the disease significantly negatively correlates with the patient's age (R=-0,514;p=0,0002). The best correlation with the COVID-19 outcome had a 4C Mortality Score (R=0,836;p=0,0001). Logistic regression revealed a significant dependence of the "outcome" and "age" parameters with the greatest accuracy in the form of age subgroups according to the World Health Organization classification with odds ratio (OR) of 4,29 (p=0,0001). As a result of ROC analysis, the best predictive ability of disease outcomes was shown for the 4C Mortality Score (area under curve (AUC)=0,878;95% confidence interval (CI): 0,782- 0,975 (p=0,00001)) and COVID-GRAM (AUC=0,807;95% CI: 0,720- 0,895 (p=0,00001));taking into account the division of patients into age subgroups, optimal predictive tools were obtained: in subgroups 18-44 years old and 45-59 years old -- the 4С Mortality Score (AUC=0,892, 95% CI: 0,762-0,980 (p=0,002) and AUC=0,853, 95% CI: 0,784-0,961 (p=0,0014), respectively);in the subgroup 60-74 years old -- the COVID-GRAM (AUC=0,833, 95% CI: 0,682-0,990 (p=0,038));in subgroups 75-90 years and >90 years -- NEWS2 (AUC=0,958, 95% CI: 0,807-1,0 (p=0,002) and AUC=0,818, 95% CI: 0,713-0,996 (p=0,006), respectively). ROC analysis showed that the age of 70 years is the threshold value, above which the probability of an unfavorable COVID-19 outcome increases significantly (OR=11,63;95% CI: 9,72- 12,06 (p=0,0052)). Conclusion. The pilot study showed the significance of predicting the hospitalization outcome of patients with severe COVID-19. The 4C Mortality Score and COVID-GRAM scales had the best predictive accuracy. The specificity and sensitivity of the scores depended on the age of a patient. The age of 70 years was the threshold value at which the risk of an adverse outcome increased significantly. Based on the data obtained, it is planned to study the problem of predicting the disease course, taking into account the severity of COVID-19. (English) [ FROM AUTHOR] Цель. Исследовать прогностическую способность шкал NEWS2, 4C Mortality Score, COVID-GRAM и qSOFA в предсказании клиниче- ских исходов у пациентов с тяжелой формой COVID-19 (COrona VIrus Disease 2019), госпитализированных в многопрофильный стационар. Материал и методы. Ð’ пилотном ретроспективном когортном исследовании использованы данные 90 больных (52 пациен- та -- подгруппа отделения реанимации и интенсивной терапии, 38 пациентов -- подгруппа коечного отделения) с подтвержден- ным диагнозом COVID-19, госпитализированных в ГКБ â„– 15 им. О. Ðœ. Филатова (г. Москва) в период с января по март 2021г. Результаты. Вероятность положительного исхода заболевания, зна- чимо отрицательно коррелирует с возрастом пациента (R=-0,514;Ñ€=0,0002). Наилучшую корреляцию с исходом COVID-19 имеет оцен- ка по шкале 4С Mortality Score (R=0,836;Ñ€=0,0001). Логистический регрессионный анализ выявил значимую зависимость параметров "исход" и "возраст" с наибольшей точностью в виде возрастных под- групп по классификации Всемирной организации здравоохранения с отношением шансов (ОШ)=4,29 (Ñ€=0,0001). Ð’ результате ROC- анализа лучшая предсказательная способность исходов заболевания показана для шкал 4С Mortality Score (AUC -- area under curve (пло- щадь под кривой)=0,878;95% доверительный интервал (ДИ): 0,782- 0,975 (p=0,00001) и COVID-GRAM (AUC=0,807;95% ДИ: 0,720-0,895 (p=0,00001);с учетом разделения пациентов на возрастные подгруп- пы получены оптимальные предиктивные инструменты: в подгруп- пах 18-44 лет и 45-59 лет -- шкала 4С Mortality Score -- AUC=0,892, 95% ДИ: 0,762-0,980 (Ñ€=0,002) и AUC=0,853, 95% ДИ: 0,784-0,961 (Ñ€=0,0014), соответственно;в подгруппе 60-74 лет -- шкала COVIDGRAM -- AUC=0,833, 95% ДИ: 0,682-0,990 (Ñ€=0,038);в подгруппах 75-90 лет и >90 лет -- шкала NEWS2 -- AUC=0,958, 95% ДИ: 0,807-1,0 (Ñ€=0,002) и AUC=0,818, 95% ДИ: 0,713-0,996 (Ñ€=0,006), соответствен- но. Совместное использование шкал 4С Mortality Score и COVIDGRAM снижало их прогностическую ценность -- AUC=0,784, 95% ДИ: 0,689-0,814 (Ñ€=0,008). С помощью ROC-анализа показано, что воз- раст 70 лет является пороговым значением, при превышении кото- рого значимо увеличивается вероятность неблагоприятного исхода COVID-19: ОШ=11,63;95% ДИ: 9,72-12,06 (Ñ€=0,0052). Заключение. Результаты пилотного исследования показали до- стоверность прогнозирования исхода госпитализации пациен- тов с тяжелой формой COVID-19. Наилучшей предсказательной точностью обладали шкалы 4С Mortality Score и COVID-GRAM. Специфичность и чувствительность оценок по шкалам зависела от возрастапациента. ВозрРст 70 лет являлся пороговым значением, при достижении которого риск неблагоприятного исхода значимо увеличивался. На основе данных проведенного пилотного исследо- вания запланировано изучение проблемы прогнозирования тече- ния заболевания с учетом степени тяжести COVID-19. (Russian) [ FROM AUTHOR] Copyright of Cardiovascular Therapy & Prevention is the property of Silicea-Poligraf LLC 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.)

Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis.

INTRODUCTION: Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients. METHODS: Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene-miRNA regulatory network, and target gene-TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes. RESULTS: A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism-cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values. CONCLUSION: This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

Impact of Acute Confusional State in Patients With COVID-19 and a Predictive Score.

BACKGROUND: Acute confusional state (ACS) in COVID-19 is shown to be associated with poor clinical outcomes. METHODS: We assessed the impact of ACS - defined as a documented deterioration of mental status from baseline on the alertness and orientation to time, place, and person - on inpatient mortality and the need for intensive care unit (ICU) transfer in inpatient admissions with active COVID-19 infection in a single-center retrospective cohort of inpatient admissions from a designated COVID-19 tertiary care center using an electronic health record system. Furthermore, we developed and validated a neurological history and symptom-based predictive score of developing ACS. RESULTS: Thirty seven out of 245 (15%) patients demonstrated ACS. Nineteen (51%) patients had multifactorial ACS, followed by 11 (30%) patients because of hypoxemia. ACS patients were significantly older (80 [70-85] years vs 50.5 [38-69] years, p < 0.001) and demonstrated more frequent history of dementia (43% vs 9%, p < 0.001) and epilepsy (16% vs 2%, p = 0.001). ACS patients observed significantly higher in-hospital mortality (45.9% vs 1.9%, aOR [adjusted odds ratio]: 15.7, 95% CI = 3.6-68.0, p < 0.001) and need for ICU transfer (64.9% vs 35.1%, aOR: 2.7, 95% CI = 1.2-6.1, p = 0.015). In patients who survived hospitalization, ACS was associated with longer hospital stay (6 [3.5-10.5] days vs 3 [2-7] day, p = 0.012) and numerically longer ICU stay (6 [4-10] days vs 3 [2-6] days, p = 0.078). A score to predict ACS demonstrated 75.68% sensitivity and 81.73% specificity at a cutoff of ≥3. CONCLUSION: A high prevalence of ACS was found in patients with COVID-19 in our study cohort. Patients with ACS demonstrated increased mortality and need for ICU care. An internally validated score to predict ACS demonstrated high sensitivity and specificity in our cohort.