Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.

Background: Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment. Methods: We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes. Results: Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set. Conclusions: We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Comparative transcriptomic analyzes of human lung epithelial cells infected with wild-type SARS-CoV-2 and its variant with a 12-bp missing in the E gene.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused a global outbreak of coronavirus disease 2019 (COVID-19) pandemic. To elucidate the mechanism of SARS-CoV-2 replication and immunogenicity, we performed a comparative transcriptome profile of mRNA and long non-coding RNAs (lncRNAs) in human lung epithelial cells infected with the SARS-CoV-2 wild-type strain (8X) and the variant with a 12-bp deletion in the E gene (F8). In total, 3,966 differentially expressed genes (DEGs) and 110 differentially expressed lncRNA (DE-lncRNA) candidates were identified. Of these, 94 DEGs and 32 DE-lncRNAs were found between samples infected with F8 and 8X. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes revealed that pathways such as the TNF signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved. Furthermore, we constructed a lncRNA-protein-coding gene co-expression interaction network. The KEGG analysis of the co-expressed genes showed that these differentially expressed lncRNAs were enriched in pathways related to the immune response, which might explain the different replication and immunogenicity properties of the 8X and F8 strains. These results provide a useful resource for studying the pathogenesis of SARS-CoV-2 variants.

Comparative transcriptomic analyzes of human lung epithelial cells infected with wild-type SARS-CoV-2 and its variant with a 12-bp missing in the E gene

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused a global outbreak of coronavirus disease 2019 (COVID-19) pandemic. To elucidate the mechanism of SARS-CoV-2 replication and immunogenicity, we performed a comparative transcriptome profile of mRNA and long non-coding RNAs (lncRNAs) in human lung epithelial cells infected with the SARS-CoV-2 wild-type strain (8X) and the variant with a 12-bp deletion in the E gene (F8). In total, 3,966 differentially expressed genes (DEGs) and 110 differentially expressed lncRNA (DE-lncRNA) candidates were identified. Of these, 94 DEGs and 32 DE-lncRNAs were found between samples infected with F8 and 8X. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes revealed that pathways such as the TNF signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved. Furthermore, we constructed a lncRNA-protein-coding gene co-expression interaction network. The KEGG analysis of the co-expressed genes showed that these differentially expressed lncRNAs were enriched in pathways related to the immune response, which might explain the different replication and immunogenicity properties of the 8X and F8 strains. These results provide a useful resource for studying the pathogenesis of SARS-CoV-2 variants.

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

Sequencing on an imported case in China of COVID-19 Delta variant emerging from India in a cargo ship in Zhoushan, China.

A cluster of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections was found in a cargo ship under repair in Zhoushan, China. Twelve of 20 crew members were identified as SARS-CoV-2 positive. We analyzed four sequences and identified them all in the Delta branch emerging from India with 7-8 amino acid mutation sites in the spike protein.

SARS-CoV-2 viral shedding characteristics and potential evidence for the priority for faecal specimen testing in diagnosis

This study aimed to identify the specimen type that has high positivity and its proper sampling time for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing to promote diagnostic efficiency. All SARS-CoV-2-infected patients with a laboratory-confirmed diagnosis in Zhoushan City were followed up for viral shedding in respiratory tract specimens and faecal samples. Positivity was analysed both qualitatively and quantitatively by proper statistical approaches with strong testing power. Viral shedding in respiratory tract and faecal specimens was prolonged to 45 and 40 days after the last exposure, respectively. The overall positive rate in respiratory tract specimens was low and relatively unstable, being higher in the early-to-mid stage than in the mid-to-late stage of the disease course. Compared with respiratory tract specimens, faecal samples had a higher viral load, higher overall positive rate, and more stable positivity in different disease courses and varied symptomatic status. Faecal specimens have the potential ability to surpass respiratory tract specimens in virus detection. Testing of faecal specimens in diagnosis, especially for identifying asymptomatic carriers, is recommended. Simultaneously, testing respiratory tract specimens at the early-to-mid stage is better than testing at the mid-to-late stage of the disease course. A relatively small sample size was noted, and statistical approaches were used to address it. Information was missing for both specimen types at different stages of the disease course due to censored data. Our research extends the observed viral shedding in both specimen types and highlights the importance of faecal specimen testing in SARS-CoV-2 diagnosis. Healthcare workers, patients, and the general public may all benefit from our study findings. Disposal of sewage from hospitals and residential areas should be performed cautiously because the virus sheds in faeces and can last for a long time.

LI-RADS ancillary features on contrast-enhanced ultrasonography.

The Liver Imaging Reporting and Data System (LI-RADS) was created to standardize liver imaging in patients at high risk for hepatocellular carcinoma (HCC), and it uses a diagnostic algorithm to assign categories that reflect the relative probability of HCC, non-HCC malignancies, or benign focal liver lesions. In addition to major imaging features, ancillary features (AFs) are used by radiologists to refine the categorization of liver nodules. In the present document, we discuss and explain the application of AFs currently defined within the LI-RADS guidelines. We also explore possible additional AFs visible on contrast-enhanced ultrasonography (CEUS). Finally, we summarize the management of CEUS LI-RADS features, including the role of current and potential future AFs.

Transmission of COVID-19 in the terminal stages of the incubation period: A familial cluster.

We report a familial cluster of 2019 novel coronavirus disease (COVID-19) to assess its potential transmission during the incubation period. The first patient in this familial cluster was identified during the presymptomatic period, as a close contact of a confirmed patient. Five family members had close contact with this first patient during his incubation period, with four of them confirmed positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the subsequent sampling tests.

Detection of Novel Coronavirus by RT-PCR in Stool Specimen from Asymptomatic Child, China.

We report an asymptomatic child who was positive for a coronavirus by reverse transcription PCR in a stool specimen 17 days after the last virus exposure. The child was virus positive in stool specimens for at least an additional 9 days. Respiratory tract specimens were negative by reverse transcription PCR.

Potential Presymptomatic Transmission of SARS-CoV-2, Zhejiang Province, China, 2020.

We report a 2-family cluster of persons infected with severe acute respiratory syndrome coronavirus 2 in the city of Zhoushan, Zhejiang Province, China, during January 2020. The infections resulted from contact with an infected but potentially presymptomatic traveler from the city of Wuhan in Hubei Province.