BACKGROUND. Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, and is now becoming a global threat. We aimed to delineate and compare the immunological features of severe and moderate COVID-19. METHODS. In this retrospective study, the clinical and immunological characteristics of 21 patients (17 male and 4 female) with COVID-19 were analyzed. These patients were classified as severe (11 cases) and moderate (10 cases) according to the guidelines released by the National Health Commission of China. RESULTS. The median age of severe and moderate cases was 61.0 and 52.0 years, respectively. Common clinical manifestations included fever, cough, and fatigue. Compared with moderate cases, severe cases more frequently had dyspnea, lymphopenia, and hypoalbuminemia, with higher levels of alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer as well as markedly higher levels of IL-2R, IL-6, IL-10, and TNF-α. Absolute numbers of T lymphocytes, CD4⁺ T cells, and CD8⁺ T cells decreased in nearly all the patients, and were markedly lower in severe cases (294.0, 177.5, and 89.0 × 10⁶/L, respectively) than moderate cases (640.5, 381.5, and 254.0 × 10⁶/L, respectively). The expression of IFN-γ by CD4⁺ T cells tended to be lower in severe cases (14.1%) than in moderate cases (22.8%). CONCLUSION. The SARS-CoV-2 infection may affect primarily T lymphocytes, particularly CD4⁺ and CD8⁺ T cells, resulting in a decrease in numbers as well as IFN-γ production by CD4⁺ T cells. These potential immunological markers may be of importance because of their correlation with disease severity in COVID-19. TRIAL REGISTRATION. This is a retrospective observational study without a trial registration number. FUNDING. This work is funded by grants from Tongji Hospital for the Pilot Scheme Project, and partly supported by the Chinese National Thirteenth Five Years Project in Science and Technology for Infectious Disease (2017ZX10202201).

The sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.

Background: Since late December, 2019, an outbreak of pneumonia cases caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, and continued to spread throughout China and across the globe. To date, few data on immunologic features of Coronavirus Disease 2019 (COVID-19) have been reported. Methods: In this single-centre retrospective study, a total of 21 patients with pneumonia who were laboratory-confirmed to be infected with SARS-CoV-2 in Wuhan Tongji hospital were included from Dec 19, 2019 to Jan 27, 2020. The immunologic characteristics as well as their clinical, laboratory, radiological features were compared between 11 severe cases and 10 moderate cases. Results: Of the 21 patients with COVID-19, only 4 (19%) had a history of exposure to the Huanan seafood market. 7 (33.3%) patients had underlying conditions. The average age of severe and moderate cases was 63.9 and 51.4 years, 10 (90.9%) severe cases and 7 (70.0%) moderate cases were male. Common clinical manifestations including fever (100%, 100%), cough (70%, 90%), fatigue (100%, 70%) and myalgia (50%, 30%) in severe cases and moderate cases. PaO2/FiO2 ratio was significantly lower in severe cases (122.9) than moderate cases (366.2). Lymphocyte counts were significantly lower in severe cases (0.7 × 10□/L) than moderate cases (1.1 × 10□/L). Alanine aminotransferase, lactate dehydrogenase levels, high-sensitivity C-reactive protein and ferritin were significantly higher in severe cases (41.4 U/L, 567.2 U/L, 135.2 mg/L and 1734.4 ug/L) than moderate cases (17.6 U/L, 234.4 U/L, 51.4 mg/L and 880.2 ug /L). IL-2R, TNF-α and IL-10 concentrations on admission were significantly higher in severe cases (1202.4 pg/mL, 10.9 pg/mL and 10.9 pg/mL) than moderate cases (441.7 pg/mL, 7.5 pg/mL and 6.6 pg/mL). Absolute number of total T lymphocytes, CD4 ⁺ T cells and CD8 ⁺ T cells decreased in nearly all the patients, and were significantly lower in severe cases (332.5, 185.6 and 124.3 × 10 ⁶ /L) than moderate cases (676.5, 359.2 and 272.0 × 10 ⁶ /L). The expressions of IFN-γ by CD4 ⁺ T cells tended to be lower in severe cases (14.6%) than moderate cases (23.6%). Conclusion: The SARS-CoV-2 infection may affect primarily T lymphocytes, particularly CD4 ⁺ T cells, resulting in significant decrease in number as well as IFN-γ production, which may be associated with disease severity. Together with clinical characteristics, early immunologic indicators including diminished T lymphocytes and elevated cytokines may serve as potential markers for prognosis in COVID-19.

Acute respiratory distress syndrome (ARDS) is a common condition associated with critically ill patients, characterized by bilateral chest radiographical opacities with refractory hypoxemia due to noncardiogenic pulmonary edema. Despite significant advances, the mortality of ARDS remains unacceptably high, and there are still no effective targeted pharmacotherapeutic agents. With the outbreak of coronavirus disease 19 worldwide, the mortality of ARDS has increased correspondingly. Comprehending the pathophysiology and the underlying molecular mechanisms of ARDS may thus be essential to developing effective therapeutic strategies and reducing mortality. To facilitate further understanding of its pathogenesis and exploring novel therapeutics, this review provides comprehensive information of ARDS from pathophysiology to molecular mechanisms and presents targeted therapeutics. We first describe the pathogenesis and pathophysiology of ARDS that involve dysregulated inflammation, alveolar-capillary barrier dysfunction, impaired alveolar fluid clearance and oxidative stress. Next, we summarize the molecular mechanisms and signaling pathways related to the above four aspects of ARDS pathophysiology, along with the latest research progress. Finally, we discuss the emerging therapeutic strategies that show exciting promise in ARDS, including several pharmacologic therapies, microRNA-based therapies and mesenchymal stromal cell therapies, highlighting the pathophysiological basis and the influences on signal transduction pathways for their use.

Up to 10–20% of patients with coronavirus disease 2019 (COVID‐19) develop a severe pulmonary disease due to immune dysfunction and cytokine dysregulation. However, the extracellular proteomic characteristics in respiratory tract of these critical COVID‐19 patients still remain to be investigated. In the present study, we performed a quantitative proteomic analysis of the bronchoalveolar lavage fluid (BALF) from patients with critical COVID‐19 and from non‐COVID‐19 controls. Our study identified 358 differentially expressed BALF proteins (P < 0.05), among which 41 were significantly changed after using the Benjamini–Hochberg correction (q < 0.05). The up‐regulated signaling was found to be mainly involved in inflammatory signaling and response to oxidative stress. A series of increased extracellular factors including Tenascin‐C (TNC), Mucin‐1 (KL‐6 or MUC1), Lipocalin‐2 (LCN2), periostin (POSTN), Chitinase 3‐like 1 (CHI3L1 or YKL40), and S100A12, and the antigens including lymphocyte antigen 6D/E48 antigen (LY6D), CD9 antigen, CD177 antigen, and prostate stem cell antigen (PSCA) were identified, among which the proinflammatory factors TNC and KL‐6 were further validated in serum of another thirty‐nine COVID‐19 patients and healthy controls, showing high potentials of being biomarkers or therapeutic candidates for COVID‐19. This BALF proteome associated with COVID‐19 would also be a valuable resource for researches on anti‐inflammatory medication and understanding the molecular mechanisms of host response.Database: Proteomic raw data are available in ProteomeXchange (http://proteomecentral.proteomexchange.org) under the accession number PXD022085, and in iProX (www.iprox.org) under the accession number IPX0002429000.

Summary: Background: COVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. Methods: We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. Findings: The final model included age, lymphocyte count, lactate dehydrogenase and SpO ₂ as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. Interpretation: COVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate. Funding: This study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK. Research in context: Evidence before this study: Since the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date. Added value of this study: In our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO ₂ remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings. Implication of all the available evidence: After further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.