Pulmonary and Renal Long COVID at Two-year Revisit (preprint)

More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.

Eleven Routine Clinical Features Predict COVID-19 Severity Uncovered by Machine Learning of Longitudinal Measurements (preprint)

Background: Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. In this study, we aim to establish a model for COVID-19 severity prediction and depict dynamic changes of key clinical features over 7 weeks.Methods: In our retrospective study, a total of 841 patients have been screened with the SARS-CoV-2 nucleic acid test, of which 144 patients were virus RNA (COVID-19) positive, resulting in a data matrix containing of 3,065 readings for 124 types of measurements from 17 categories. We built a support vector machine model assisted with genetic algorithm for feature selection based on the longitudinal measurement. 25 patients as a test cohort were included from an independent hospital.Findings: A panel of 11 routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving an accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved an accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. This study presents a practical model for timely severity prediction for COVID-19, which is freely available at a webserver https://guomics.shinyapps.io/covidAI/.Interpretation: The model provided a classifier composed of 11 routine clinical features which are widely available during COVID-19 management which could predict the severity and may guide the medical care of COVID-19 patients.Funding: This work is supported by grants from Tencent Foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).Declaration of Interests: NAEthics Approval Statement: This study was approved by the Medical Ethics Committee of Taizhou Hospital, Shaoxing People’s Hospital and Westlake University, Zhejiang province of China, and informed consent was obtained from each enrolled subject.

Eleven Routine Clinical Features Predict COVID-19 Severity (preprint)

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

Recapitulation of SARS-CoV-2 Infection and Cholangiocyte Damage with Human Liver Organoids (preprint)

The newly emerged pandemic coronavirus, SARS-CoV-2, has posed a significant public health threat worldwide. However, the mode of virus transmission and tissue tropism is not well established yet. Recent findings of substantial liver damage in patients and ACE2+ cholangiocytes in healthy liver tissues prompted us to hypothesize that human liver ductal organoids could serve as a model to determine the susceptibility and mechanisms underlining the liver damage upon SARS-CoV-2 infection. By single-cell RNA sequencing, we found that long-term liver ductal organoid culture preserved the human specific ACE2+ population of cholangiocytes. Moreover, human liver ductal organoids were permissive to SARS-CoV-2 infection and support robust replication. Notably, virus infection impaired the barrier and bile acid transporting functions of cholangiocytes through dysregulation of genes involved in tight junction formation and bile acid transportation, which could explain the bile acid accumulation and consequent liver damage in patients. These results indicate that control of liver damage caused directly by viral infection should be valued in treating COVID-19 patients. Our findings also provide an application of human organoids in investigating the tropism and pathogenesis of SARS-CoV-2, which would facilitate novel drug discovery.