Abstract Background The stage of CT images was rarely studied and the relationship between the severity of Coronavirus Disease 2019 (COVID-19) and CT images has not been studied based on systematic quantitative analysis currently. Purpose To investigate the staging duration and classification of CT images of patients with COVID-19 based on quantitative analysis. Materials and Methods This is an ambispective observational cohort study based on 125 patients with COVID-19 from Jan 23 to Feb 28, 2020. The stage of CT and pulmonary lesion size were quantitatively analyzed. The categorical regression analysis based on optimal scale (CATREG) was performed to evaluate the association of CT score, age, and gender with the clinical type. Results The CT images of 125 patients with COVID-19 (50.13 ± 16.91 years, 66 women) were analyzed in this study. Except for pre-early stage, the duration of early, progression-consolidation, and dissipation stage of CT images was 3.40 ± 2.31, 10.07 ± 4.91, and 20.60 ± 7.64 days, respectively. The median CT score was 5.00 (2.00-8.50) during the first 30 days, which reached a peak on the 11 th day. Significant differences were found between the median CT scores of different clinical types (P<0.05). Besides, the age was correlated with the clinical type (P<0.001), the CT scores of 0.00-11.50, 11.50-16.00, and 16.00-20.00 were separately correlated with the moderate, severe, and critical type with the output accuracy 69.60%. Conclusion The four-stage staging method based on quantitative analysis is consistent with the change rules of staging features and COVID-19. Quantitative study by scoring pulmonary lesion sizes accurately revealed the evolvement of pulmonary lesions and differences between different clinical types. Summary Quantitative study of the stage duration and classification of chest CT images can objectively reveal the relationship between Coronavirus Disease 2019 (COVID-19) and chest CT images. Key Results 1. A four-stage staging method was proposed. Except for pre-early stage, the duration of early, progression-consolidation, and dissipation stage of CT images was 3.40 ± 2.31, 10.07 ± 4.91, and 20.60 ± 7.64 days, respectively. 2. The severer the disease, the higher the median CT scores and their peak value. 3. The CT scores of 0.00-11.50, 11.50-16.00, and 16.00-20.00 were separately correlated with the moderate, severe, and critical type.
Background: As the spreading of the COVID-19 around the global, we investigated the characteristics and changes of symptoms in COVID-19 patients.Methods: This was an ambispective observational cohort study, and 133 confirmed COVID-19 patients were included and all symptoms over the course were analyzed qualitatively.The symptoms, their changes over the course in the cohort and in the different clinical types, etc. were illustrated.Differences in different periods and severities were analyzed through Chi square test, association with severity was analyzed through LASSO binomial logistic regression analysis.Inter-correlation and classification of symptoms were completed.Major symptoms were screened and their changes were illustrated.Results: A total of 43 symptoms with frequencies as 6067 in this cohort.Differences of symptoms in different stages and clinical types were significant.Expectoration, shortness of breath, dyspnea, diarrhea, poor appetite were positively but vomiting, waist discomfort, pharyngeal discomfort, acid reflux were negatively correlated with the combined-severe and critical type; dyspnea was correlated with the critical type.The 17 major symptoms were identified.The average daily frequency of symptoms per case was decreased continuously before the transition into the severe type and increased immediately one day before the transition and then decreased.It was decreased continuously before the transition date of the critical type and increased from the transition into the critical type to the next day and decreased thereafter.Dyspnea (P<0.001),shortness of breath (P<0.01) and chest distress (P<0.05) were correlated with death and their corresponding coefficient was 0.393, 0.258, 0.214, respectively. Conclusion:The symptoms of COVID-19 patients mainly related to upper respiratory tract infection, cardiopulmonary function, and digestive system.The mild type and the early stage in other types mainly related to upper respiratory tract infection.The cardiopulmonary function and digestive system associated symptoms were found in all other types and stages.Dyspnea was correlated with critical type and dyspnea, shortness of breath, and chest distress were correlated with death.Respiratory dysfunction (or incompleteness) associated symptoms were the characteristic symptoms.The changes of symptoms did not synchronously with the changes of severity before the transition into the severe or critical type.
Abstract Background As the spreading of the COVID-19 around the global, we investigated the characteristics and changes of symptoms in COVID-19 patients. Methods This was an ambispective observational cohort study, and 133 confirmed COVID-19 patients were included and all symptoms over the course were analyzed qualitatively. The symptoms, their changes over the course in the cohort and in the different clinical types, etc. were illustrated. Differences in different periods and severities were analyzed through Chi square test, association with severity was analyzed through LASSO binomial logistic regression analysis. Inter-correlation and classification of symptoms were completed. Major symptoms were screened and their changes were illustrated. Results A total of 43 symptoms with frequencies as 6067 in this cohort. Differences of symptoms in different stages and clinical types were significant. Expectoration, shortness of breath, dyspnea, diarrhea, poor appetite were positively but vomiting, waist discomfort, pharyngeal discomfort, acid reflux were negatively correlated with the combined-severe and critical type; dyspnea was correlated with the critical type. The 17 major symptoms were identified. The average daily frequency of symptoms per case was decreased continuously before the transition into the severe type and increased immediately one day before the transition and then decreased. It was decreased continuously before the transition date of the critical type and increased from the transition into the critical type to the next day and decreased thereafter. Dyspnea ( P <0.001), shortness of breath ( P <0.01) and chest distress ( P <0.05) were correlated with death and their corresponding coefficient was 0.393, 0.258, 0.214, respectively. Conclusion The symptoms of COVID-19 patients mainly related to upper respiratory tract infection, cardiopulmonary function, and digestive system. The mild type and the early stage in other types mainly related to upper respiratory tract infection. The cardiopulmonary function and digestive system associated symptoms were found in all other types and stages. Dyspnea was correlated with critical type and dyspnea, shortness of breath, and chest distress were correlated with death. Respiratory dysfunction (or incompleteness) associated symptoms were the characteristic symptoms. The changes of symptoms did not synchronously with the changes of severity before the transition into the severe or critical type.
Abstract Background The pandemic of coronavirus disease 2019 (COVID-19) has become the first concern in international affairs as the novel coronavirus (SARS-CoV-2) is spreading all over the world at a terrific speed. The accuracy of early diagnosis is critical in the control of the spread of the virus. Although the real-time RT-PCR detection of the virus nucleic acid is the current golden diagnostic standard, it has high false negative rate when only apply single test. Objective Summarize the baseline characteristics and laboratory examination results of hospitalized COVID-19 patients. Analyze the factors that could interfere with the early diagnosis quantitatively to support the timely confirmation of the disease. Methods All suspected patients with COVID-19 were included in our study until Feb 9 th , 2020. The last day of follow-up was Mar 20 th , 2020. Throat swab real-time RT-PCR test was used to confirm SARS-CoV-2 infection. The difference between the epidemiological profile and first laboratory examination results of COVID-19 patients and non-COVID-19 patients were compared and analyzed by multiple logistic regression. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to assess the potential diagnostic value in factors, which had statistical differences in regression analysis. Results In total, 315 hospitalized patients were included. Among them, 108 were confirmed as COVID-19 patients and 207 were non-COVID-19 patients. Two groups of patients have significance in comparing age, contact history, leukocyte count, lymphocyte count, C-reactive protein, erythrocyte sedimentation rate (p<0.10). Multiple logistic regression analysis showed age, contact history and decreasing lymphocyte count could be used as individual factor that has diagnostic value (p<0.05). The AUC of first RT-PCR test was 0.84 (95% CI 0.73-0.89), AUC of cumulative two times of RT-PCR tests was 0.92 (95% CI 0.88-0.96) and 0.96 (95% CI 0.93-0.99) for cumulative three times of RT-PCR tests. Ninety-six patients showed typical pneumonia radiological features in first CT scan, AUC was 0.74 (95% CI 0.60-0.73). The AUC of patients’ age, contact history with confirmed people and the decreased lymphocytes were 0.66 (95% CI 0.60-0.73), 0.67 (95% CI 0.61-0.73), 0.62 (95% CI 0.56-0.69), respectively. Taking chest CT scan diagnosis together with patients age and decreasing lymphocytes, AUC would be 0.86 (95% CI 0.82-0.90). The age threshold to predict COVID-19 was 41.5 years, with a diagnostic sensitivity of 0.70 (95% CI 0.61-0.79) and a specificity of 0.59 (95% CI 0.52-0.66). Positive and negative likelihood ratios were 1.71 and 0.50, respectively. Threshold of lymphocyte count to diagnose COVID-19 was 1.53×10 9 /L, with a diagnostic sensitivity of 0.82 (95% CI 0.73-0.88) and a specificity of 0.50 (95% CI 0.43-0.57). Positive and negative likelihood ratios were 1.64 and 0.37, respectively. Conclusion Single RT-PCR test has relatively high false negative rate. When first RT-PCR test show negative result in suspected patients, the chest CT scan, contact history, age and lymphocyte count should be used combinedly to assess the possibility of SARS-CoV-2 infection.
Abstract Background To characterize C-reactive protein (CRP) changes features from patients with coronavirus disease 2019 (COVID-19) and to quantify the correlation between CRP value and clinical classification. Methods This was a bidirectional observational cohort study. All laboratory confirmed COVID-19 patients hospitalized in Xiangyang No.1 People’s Hospital were included. Patients’ general information, clinical type, CRP value and outcome were collected. Patients were grouped according to the age, clinical type and outcome, and their CRP were compared. The CRP value, age gender, and clinical type were used to build a categorical regression model to investigate the association between CRP and clinical type. Results The 131 patients aged 50.13±17.13 years old. There were 4 mild, 88 moderate, 21 severe and 18 critical cases. Statistical significance of CRP median exists between different clinical types and ages. There were 10 deaths and 121 cases have been discharged. The CRP in death group dramatically increased continuously until died, while increased firstly and decreased later in the survivor and survivor in critical type. The categorical regression model also showed that CRP and age had significant coefficient. During the first 15 days from symptom onset, the maximum of CRP ranged between 0.47-53.37 mg/L were related to mild combined with moderate type, ranged 53.84-107.08 mg/L were related to severe type, and 107.42-150.00 mg/L were related to the critical type. Conclusions CRP showed different distribution feature and existed differences in various ages, clinical types and outcomes of COVID-19 patients. The features corresponded with disease progression.
Abstract Bone marrow mesenchymal stem cells (BMSC) can ameliorate ischemic injury of various tissues. However, the molecular mechanisms involved remain to be clarified. In this study, we intend to investigate the effects of BMSC‐derived conditioned medium (BMSC‐CM) on hypoxia/reoxygenation (H/R)‐induced injury of H9c2 myocardial cells, and the potential mechanisms. Cell injury was determined through level of cell viability, lactate dehydrogenase (LDH) release, total intracellular reactive oxygen species (ROS), mitochondrial membrane potential (Δψm), and cell apoptosis. Autophagic activity of cells was detected through levels of the autophagy‐associated proteins and autophagic flux. Results showed that BMSC‐CM alleviated H/R‐induced injury in H9c2 cells, as demonstrated by increased cell viability and Δψm, decreased ROS production, LDH release, and cell apoptosis. Furthermore, the H/R treatment induced a decrease in autophagic activity and an increase in Notch2 signaling activation in H9c2 cells. In the presence of BMSC‐CM, the autophagic activity impaired by the H/R treatment was upregulated with decreased phosphorylation of mTOR, and the activation of Notch2 signaling was downregulated. These effects of BMSC‐CM could be replicated by Notch signaling inhibitor. In contrast, inhibitors of cell autophagy including chloroquine (CQ) and 3‐methyladenine, diminished the protective effects of BMSC‐CM. Taken together results, our study showed that BMSC‐CM could protect H9c2 cells from H/R‐induced injury potentially through regulating Notch2/mTOR/autophagy signaling. These findings may provide a novel insight into the mechanisms of BMSC‐CM in therapy of myocardial ischemia/reperfusion injury as well as other ischemic diseases.
Abstract Objectives To study the features of creatine-kinase (CK) in COVID-19 patients with different ages, clinical types and outcomes and quantify the relationship between CK value and clinical type. Methods All laboratory confirmed COVID-19 patients hospitalized in Xiangyang No.1 People’s Hospital were included. Patients’ general information, clinical type, all CK values and outcome were collected. Results The peak median value of CK in cases aged ≥ 71 years old (appeared at T2) was higher than that in cases aged ≤ 70 years old. There was statistical difference between the two groups ( P =0.001). Similarly, the peak in critical cases (appeared at T2) was higher than moderate and severe types, and significant difference were existed among moderate, severe, and critical types ( P =0.000). Moreover, the peak value in death group (appeared at T2) was higher than those in survival group. Significant difference was also found between them ( P =0.000). According to the optimal scale regression model, the CK value ( P =0.000) and age ( P =0.000) were associated with the clinical type. Conclusions Difference of the CK in different ages, clinical types, and outcomes were significant. The results of the optimal scale regression model are helpful to judge the clinical type of COVID-19 patients.
Background: There is an enormous risk to public health worldwide from the Coronavirus disease 2019 (COVID-19), and previous observational studies have reported that gut microbiota is associated with an increased risk of COVID-19. However, whether gut microbiota was causally associated with COVID-19 remains unclear. Methods: Mendelian randomization (MR) analysis was applied to elucidate the causal relationship between gut microbiota and COVID-19 leveraging a large-scale dataset with gut microbiota (N = 18,340) and COVID-19 Host Genetics Initiative (HGI) GWAS data. Inverse-variance weighted (IVW) was used to estimate causal effects. Sensitivity analyses included leave-one-out analysis and Cochran’s Q test. Results: Seven kinds of gut microbiota were causally associated with an increased risk of COVID-19, and eleven kinds of gut microbiota were causally associated with a lower risk of COVID-19. In addition, this study also indicated that the diversity of gut microbiota was decreased with the aggravation of COVID-19 (susceptibility, hospitalization, and severity). No heterogeneity or pleiotropy was detected for significant estimates. Conclusion: In this study, we found that eighteen kinds of gut microbiota were causally associated with COVID-19, which provides a theoretical basis for guiding clinical work. More attention should be paid to the monitoring of the gut microbiome to identify more risk predictors and potentially beneficial taxa in the prevention and treatment of COVID-19.
Introduction: The study aimed at screening indicators with differential diagnosis values and investigating the characteristics of laboratory tests in COVID-19 patients. Methodology: All the laboratory tests from COVID-19 patients and non-COVID-19 patients in this cohort were included. Test values from the groups during the course, days 1-7, and days 8-14 were analyzed. Mann-Whitney U test, univariate logistic regression analysis, and multivariate regression analysis were performed. Regression models were established to verify the diagnostic performance of indicators. Results: 302 laboratory tests were included in this cohort, and 115 indicators were analyzed; the values of 61 indicators had significant differences (p < 0.05) between groups, and 23 indicators were independent risk factors of COVID-19. During days 1-7, the values of 40 indicators had significant differences (p < 0.05) between groups, while 20 indicators were independent risk factors of COVID-19. During days 8-14, the values of 45 indicators had significant differences (p < 0.05) between groups, and 23 indicators were independent risk factors of COVID-19. About 10, 12, and 12 indicators showed significant differences (p < 0.05) in multivariate regression analysis in different courses respectively, and the diagnostic performance of the model from them was 74.9%, 80.3%, and 80.8% separately. Conclusions: The indicators obtained through systematic screening have preferable differential diagnosis values. Compared with non-COVID-19 patients, the screened indicators indicated that COVID-19 patients had more severe inflammatory responses, organ damage, electrolyte and metabolism disturbance, and coagulation disorders. This screening approach could find valuable indicators from a large number of laboratory test indicators.
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