Clinical status of patients 1 year after hospital discharge following recovery from COVID-19: a prospective cohort study.

BACKGROUND: The long-term clinical status of coronavirus disease 2019 (COVID-19) in recovered patients remains largely unknown. This prospective cohort study evaluated clinical status of COVID-19 and explored the associated risk factors. METHODS: At the outpatient visit, patients underwent routine blood tests, physical examinations, pulmonary function tests, 6-min walk test, high-resolution computed tomography (CT) of the chest, and extrapulmonary organ function tests. RESULTS: 230 patients were analyzed. Half (52.7%) reported at least one symptom, most commonly fatigue (20.3%) and sleep difficulties (15.8%). Anxiety (8.2%), depression (11.3%), post-traumatic symptoms (10.3%), and sleep disorders (26.3%) were also reported. Diffusion impairments were found in 35.4% of the patients. Abnormal chest CT scans were present in 63.5% of the patients, mainly reticulation and ground-glass opacities. Further, a persistent decline in kidney function was observed after discharge. SARS-CoV-2-specific antibodies of IgA, IgG, and IgM were positive in 56.4%, 96.3%, and 15.2% of patients, respectively. Multivariable logistic regression showed that disease severity, age, and sex were closely related to patient recovery. CONCLUSIONS: One year after hospital discharge, patients recovered from COVID-19 continued to experience both pulmonary and extrapulmonary dysfunction. While paying attention to pulmonary manifestations of COVID-19, follow-up studies on extrapulmonary manifestations should be strengthened.

Identifying Small-Molecule Inhibitors of SARS-CoV-2 RNA-Dependent RNA Polymerase by Establishing a Fluorometric Assay.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2), a member of the coronavirus family, appeared in 2019 and has caused the largest global public health and economic emergency in recent history, affecting almost all sectors of society. SARS-CoV-2 is a single-stranded positive-sense RNA virus that relies on RNA-dependent RNA polymerase (RdRp) activity in viral transcription and replication. Due to its high sequence and structural conservation in coronavirus and new SARS-CoV-2 variants, RdRp has been recognized as the key therapeutic target to design novel antiviral strategies. Nucleotide analogs (NAs), such as remdesivir, is the most promising class of RdRp inhibitors to be used in the treatment of COVID-19. However, the presence of exonucleases in SARS-CoV-2 caused a great challenge to NAs; the excision of incorporated NAs will lead to viral resistance to this group of inhibitors. Here, we expressed active RdRp protein in both a eukaryotic expression system of baculovirus-infected insect cells and a prokaryotic expression system of Escherichia coli cells. Nsp7 and nsp8 of the functional RdRp holoenzyme were generated in E. coli. An in vitro RdRp activity assay has been established with a reconstituted nsp12/nsp7/nsp8 complex and biotin-labeled self-priming RNAs, and the activity of the RdRp complex was determined by detecting binding and extension of RNAs. Moreover, to meet the needs of high-throughput drug screening, we developed a fluorometric approach based on dsRNA quantification to assess the catalytic activity of the RdRp complex, which is also suitable for testing in 96-well plates. We demonstrated that the active triphosphate form of remdesivir (RTP) and several reported non-nucleotide analog viral polymerase inhibitors blocked the RdRp in the in vitro RdRp activity assay and high-throughput screening model. This high-throughput screening model has been applied to a custom synthetic chemical and natural product library of thousands of compounds for screening SARS-CoV-2 RdRp inhibitors. Our efficient RdRp inhibitor discovery system provides a powerful platform for the screening, validation, and evaluation of novel antiviral molecules targeting SARS-CoV-2 RdRp, particularly for non-nucleotide antivirals drugs (NNAs).

A convolutional neural network-based COVID-19 detection method using chest CT images.

Background: High-throughput population screening for the novel coronavirus disease (COVID-19) is critical to controlling disease transmission. Convolutional neural networks (CNNs) are a cutting-edge technology in the field of computer vision and may prove more effective than humans in medical diagnosis based on computed tomography (CT) images. Chest CT images can show pulmonary abnormalities in patients with COVID-19. Methods: In this study, CT image preprocessing are firstly performed using fuzzy c-means (FCM) algorithm to extracted the region of the pulmonary parenchyma. Through multiscale transformation, the preprocessed image is subjected to multi scale transformation and RGB (red, green, blue) space construction. After then, the performances of GoogLeNet and ResNet, as the most advanced CNN architectures, were compared in COVID-19 detection. In addition, transfer learning (TL) was employed to solve overfitting problems caused by limited CT samples. Finally, the performance of the models were evaluated and compared using the accuracy, recall rate, and F1 score. Results: Our results showed that the ResNet-50 method based on TL (ResNet-50-TL) obtained the highest diagnostic accuracy, with a rate of 82.7% and a recall rate of 79.1% for COVID-19. These results showed that applying deep learning technology to COVID-19 screening based on chest CT images is a very promising approach. This study inspired us to work towards developing an automatic diagnostic system that can quickly and accurately screen large numbers of people with COVID-19. Conclusions: We tested a deep learning algorithm to accurately detect COVID-19 and differentiate between healthy control samples, COVID-19 samples, and common pneumonia samples. We found that TL can significantly increase accuracy when the sample size is limited.

SCIGA: Software for large-scale, single-cell immunoglobulin repertoire analysis.

BACKGROUND: B-cell immunoglobulin repertoires with paired heavy and light chain can be determined by means of 10X single-cell V(D)J sequencing. Precise and quick analysis of 10X single-cell immunoglobulin repertoires remains a challenge owing to the high diversity of immunoglobulin repertoires and a lack of specialized software that can analyze such diverse data. FINDINGS: In this study, specialized software for 10X single-cell immunoglobulin repertoire analysis was developed. SCIGA (Single-Cell Immunoglobulin Repertoire Analysis) is an easy-to-use pipeline that performs read trimming, immunoglobulin sequence assembly and annotation, heavy and light chain pairing, statistical analysis, visualization, and multiple sample integration analysis, which is all achieved by using a 1-line command. Then SCIGA was used to profile the single-cell immunoglobulin repertoires of 9 patients with coronavirus disease 2019 (COVID-19). Four neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified from these repertoires. CONCLUSIONS: SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.

Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study.

BACKGROUND: Thousands of Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals Persistent follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis. METHODS: This study involves 462 laboratory-confirmed patients with COVID-19 who were admitted to Shenzhen Third People's Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 287 patients were followed up from 90 to 150 days after the onset of the disease, and lung function tests were conducted about three months after the onset. The risk factors affecting the persistence of pulmonary fibrosis were identified through regression analysis and the prediction model of the persistence of pulmonary fibrosis was established. RESULTS: Parenchymal bands, irregular interfaces, reticulation and traction bronchiectasis were the most common CT features in all COVID-19 patients. During the 0-30, 31-60, 61-90, 91-120 and > 120 days after onset, 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. More than half of the COVID-19 patients revealed abnormal conditions in lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups. CONCLUSIONS: Persistent pulmonary fibrosis was more likely to develop in patients with older age, higher BMI, severe/critical condition, fever, a longer viral clearance time, pre-existing disease and delayed hospitalization. Fibrosis developed in COVID-19 patients could be reversed in about a third of the patients after 120 days from onset. The pulmonary function of less than half of COVID-19 patients could turn to normal condition after three months from onset. An effective prediction model with an average area under the curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.

Analysis of pathological changes in the epithelium in COVID-19 patient airways.

Severe COVID-19 patient airways plugged by MUC5AC-containing mucus exhibit hyperplasia of goblet cells, and hypoplasia of multiciliated cells and club cells, as well as significantly reduced CC16 and MUC5B levels, and increased IL-13 levels https://bit.ly/2M2NcdO.

Thin-section computed tomography findings and longitudinal variations of the residual pulmonary sequelae after discharge in patients with COVID-19: a short-term follow-up study.

OBJECTIVES: This study analyzed and compared CT findings and longitudinal variations after discharge between severe and non-severe coronavirus disease (COVID-19) patients who had residual pulmonary sequelae at pre-discharge. METHODS: A total of 310 patients were included and stratified into severe and non-severe COVID-19 groups. Cross-sectional CT features across different time periods (T0: pre-discharge, T1: 1-4 weeks after discharge, T2: 5-8 weeks after discharge, T3: 9-12 weeks after discharge, T4: > 12 weeks after discharge) were compared, and the longitudinal variations of CT findings were analyzed and compared in both groups. RESULTS: The cumulative absorption rate of fibrosis-like findings in the severe and non-severe groups at T4 was 24.3% (17/70) and 52.0% (53/102), respectively. In both groups, ground-glass opacity (GGO) with consolidation showed a clear decreasing trend at T1, after which they maintained similar lower levels. The GGO in the severe group showed an increasing trend first at T1 and then decreasing at T4; however, the incidence decreased gradually in the non-severe group. Most fibrosis-like findings showed a tendency to decrease rapidly and then remained stable. Bronchial dilatation in the severe group persisted at an intermediate level. CONCLUSIONS: After discharge, the characteristics and changing trends of pulmonary sequelae caused by COVID-19 were significantly different between the two groups. Pulmonary sequelae were more serious and recovery was slower in patients with severe/critical disease than in patients with moderate disease. A portion of the fibrosis-like findings were completely absorbed in patients with moderate and severe/critical diseases. KEY POINTS: • Lung sequelae were more serious and recovery was slower in severe/critical COVID-19 patients. • Complete absorption of fibrosis-like findings after a short-term follow-up was observed in at least 17/70 (24.3%) of COVID-19 patients with severe/critical disease and 53/102 (52.0%) of COVID-19 patients with moderate disease. • The most common fibrosis-like findings was a parenchymal band; irregular interface was a nonspecific sign of COVID-19, and the percentage of bronchial dilatation in patients with severe/critical disease remained at a relatively stable medium level (range, 31.6 to 47.8%) at all stages.

Clinical characteristics of recovered COVID-19 patients with re-detectable positive RNA test.

BACKGROUND: The characteristics, significance and potential cause of positive SARS-CoV-2 diagnoses in recovered coronavirus disease 2019 (COVID-19) patients post discharge (re-detectable positive, RP) remained elusive. METHODS: A total of 262 COVID-19 patients discharged from January 23 to February 25, 2020 were enrolled into this study. RP and non-RP (NRP) patients were grouped according to disease severity, and the characterization at re-admission was analyzed. SARS-CoV-2 RNA and plasma antibody levels were measured, and all patients were followed up for at least 14 days, with a cutoff date of March 10, 2020. RESULTS: A total of 14.5% of RP patients were detected. These patients were characterized as young and displayed mild and moderate conditions compared to NRP patients while no severe patients were RP. RP patients displayed fewer symptoms but similar plasma antibody levels during their hospitalization compared to NRP patients. Upon hospital readmission, these patients showed no obvious symptoms or disease progression. All 21 close contacts of RP patients were tested negative for viral RNA and showed no suspicious symptoms. Eighteen out of 24 of RNA-negative samples detected by the commercial kit were tested positive for viral RNA using a hyper-sensitive method, suggesting that these patients were potential carriers of the virus after recovery from COVID-19. CONCLUSIONS: Our results indicated that young patients, with a mild diagnosis of COVID-19 are more likely to display RP status after discharge. These patients show no obvious symptoms or disease progression upon re-admission. More sensitive RNA detection methods are required to monitor these patients. Our findings provide information and evidence for the management of convalescent COVID-19 patients.

First case of COVID-19 complicated with fulminant myocarditis: a case report and insights.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been demonstrated to be the cause of pneumonia. Nevertheless, it has not been reported as the cause of acute myocarditis or fulminant myocarditis. CASE PRESENTATION: A 63-year-old male was admitted with pneumonia and cardiac symptoms. He was genetically confirmed as having COVID-19 according to sputum testing on the day of admission. He also had elevated troponin I (Trop I) level (up to 11.37 g/L) and diffuse myocardial dyskinesia along with a decreased left ventricular ejection fraction (LVEF) on echocardiography. The highest level of interleukin-6 was 272.40 pg/ml. Bedside chest radiographs showed typical ground-glass changes indicative of viral pneumonia. Laboratory test results for viruses that cause myocarditis were all negative. The patient conformed to the diagnostic criteria of the Chinese expert consensus statement for fulminant myocarditis. After receiving antiviral therapy and mechanical life support, Trop I was reduced to 0.10 g/L, and interleukin-6 was reduced to 7.63 pg/mL. Moreover, the LVEF of the patient gradually recovered to 68%. The patient died of aggravation of secondary infection on the 33rd day of hospitalization. CONCLUSION: COVID-19 patients may develop severe cardiac complications such as myocarditis and heart failure. This is the first report of COVID-19 complicated with fulminant myocarditis. The mechanism of cardiac pathology caused by COVID-19 needs further study.

Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury.

The outbreak of the 2019-nCoV infection began in December 2019 in Wuhan, Hubei province, and rapidly spread to many provinces in China as well as other countries. Here we report the epidemiological, clinical, laboratory, and radiological characteristics, as well as potential biomarkers for predicting disease severity in 2019-nCoV-infected patients in Shenzhen, China. All 12 cases of the 2019-nCoV-infected patients developed pneumonia and half of them developed acute respiratory distress syndrome (ARDS). The most common laboratory abnormalities were hypoalbuminemia, lymphopenia, decreased percentage of lymphocytes (LYM) and neutrophils (NEU), elevated C-reactive protein (CRP) and lactate dehydrogenase (LDH), and decreased CD8 count. The viral load of 2019-nCoV detected from patient respiratory tracts was positively linked to lung disease severity. ALB, LYM, LYM (%), LDH, NEU (%), and CRP were highly correlated to the acute lung injury. Age, viral load, lung injury score, and blood biochemistry indexes, albumin (ALB), CRP, LDH, LYM (%), LYM, and NEU (%), may be predictors of disease severity. Moreover, the Angiotensin II level in the plasma sample from 2019-nCoV infected patients was markedly elevated and linearly associated to viral load and lung injury. Our results suggest a number of potential diagnosis biomarkers and angiotensin receptor blocker (ARB) drugs for potential repurposing treatment of 2019-nCoV infection.