Background: Using a toxin-induced nonhuman primate model of acute liver failure (ALF), we previously reported that peripheral infusion of human umbilical cord mesenchymal stem cells (hUC-MSCs) strongly suppresses the activation of circulating monocytes and interleukin-6 (IL-6) production, thereby disrupting the development of a cytokine storm and improving the prognosis of monkeys. MSCs are considered to play a therapeutic role under different stresses by adaptively producing specific factors, prompting us to investigate the factors that hUC-MSCs produce in response to high serum levels of IL-6, which plays a critical role in initiating and accelerating ALF. Methods: We stimulated hUC-MSCs with IL-6, and the hUC-MSC-derived exosomes were deeply sequenced. The miRNAs in the exosomes that have potential to suppress IL-6-associated signaling pathway were screened, and the role of one of the most possible miRNAs was tested in the mouse model of inflammatory liver injury. Result: We determined that miR-455-3p, which is secreted through exosomes and potentially targets PI3K signaling, was highly produced by hUC-MSCs with IL-6 stimulation. The miR-455-3p-enriched exosomes could inhibit the activation and cytokine production of macrophages challenged with lipopolysaccharide (LPS) both in vivo and in vitro. In a chemical liver injury mouse model, enforced expression of miR-455-3p could attenuate macrophage infiltration and local liver damage and reduce the serum levels of inflammatory factors, thereby improving liver histology and systemic disorder. Conclusions: miR-455-3p-enriched exosomes derived from hUC-MSCs are a promising therapy for acute inflammatory liver injury.

Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19. A cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping. The median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801–0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful. We established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.

Purpose: We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. Patients and Methods: This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. Results: A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P< 0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). Conclusion: Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance.

The aim of the study was to explore a novel risk score to predict diagnosis with COVID‐19 among all suspected patients at admission. This was a retrospective, multicenter, and observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. Finally, 336 confirmed COVID‐19 patients and 139 control patients were included. We found nine independent risk factors for diagnosis with COVID‐19 at admission to hospital: epidemiological exposure histories (OR:13.32; 95%CI, 6.39‐27.75), weakness/fatigue (OR:4.51, 95%CI, 1.70‐11.96), heart rate less than 100 beat/minutes (OR:3.80, 95%CI, 2.00‐7.22), bilateral pneumonia (OR:3.60, 95%CI, 1.83‐7.10), neutrophil count less than equal to 6.3 × 10⁹/L (OR: 6.77, 95%CI, 2.52‐18.19), eosinophil count less than equal to 0.02 × 10⁹/L (OR:3.14, 95%CI, 1.58‐6.22), glucose more than equal to 6 mmol/L (OR:2.43, 95%CI, 1.04‐5.66), D‐dimer ≥ 0.5 mg/L (OR:3.49, 95%CI, 1.22‐9.96), and C‐reactive protein less than 5 mg/L (OR:3.83, 95%CI, 1.86‐7.92). As for the performance of this risk score, a cut‐off value of 20 (specificity: 0.866; sensitivity: 0.813) was identified to predict COVID‐19 according to reciever operator characteristic curve and the area under the curve was 0.921 (95%CI: 0.896‐0.945; P < .01). We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID‐19 among suspected patients.

Background: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). Methods: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. Results: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. Conclusion: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.