Mesenchymal stem cells microvesicles (MSC-MVs) stabilize endothelial barrier function in acute lung injury (ALI); however, the detailed mechanism remains to be further defined. Hepatocyte growth factor (HGF), which is derived from MSC-MVs, might have a key role in the restoration of endothelial barrier function by MSC-MVs. MSCs with lentiviral vector-mediated HGF gene knockdown (siHGF-MSC) were generated. A co-culture model of pulmonary microvascular endothelial cells and MSC-MVs collected from MSCs or siHGF-MSCs after 24 h of hypoxic culture was utilized. Then, endothelial paracellular and transcellular permeabilities were detected. VE-cadherin, and occludin protein expression in the endothelial cells was measured using Western blot. Endothelial cell proliferation was analysed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay. Endothelial cell apoptosis was analysed using TUNEL assay. Finally, IL-6 and IL-10 production was determined via an enzyme-linked immunosorbent assay (ELISA). Treatment with MSC-MVs significantly decreased LPS-induced endothelial paracellular and transcellular permeabilities, and the effect was significantly inhibited after HGF gene knockdown in MSC-MVs. Furthermore, treatment with MSC-MVs increased the expression of the endothelial intercellular junction proteins VE-cadherin and occludin. Treatment with MSC-MVs also decreased endothelial apoptosis and induced endothelial cell proliferation. Finally, the treatment reduced IL-6 production and increased IL-10 production in the conditioned media of endothelial cells. However, the effects of the treatment with MSC-MVs were inhibited after HGF gene knockdown. MSC-MVs protect the barrier functions of pulmonary microvascular endothelial cells, which can be partly attributed to the presence of HGF in the MSC-MVs.

Introduction: Mesenchymal stem cells (MSCs) exert immunomodulatory functions by inducing the development and differentiation of naive T cells into T cells with an anti-inflammatory regulatory T cell (Treg) phenotype. Our previous study showed that hepatocyte growth factor (HGF) secreted by MSCs had immunomodulatory effects in the context of lipopolysaccharide (LPS) stimulation. We hypothesized that HGF is a key factor in the MSC-mediated regulation of the T helper 17 (Th17) cell/regulatory T (Treg) cell balance. Methods: We investigated the effects of MSCs on the differentiation of CD4+ T cells and the functions of Th17/Treg cells in response to LPS stimulation by performing in vitro coculture experiments. MSCs were added to the upper chambers of cell culture inserts, and CD4+ T cells were plated in the lower chambers, followed by treatment with LPS or an anti-HGF antibody. Th17 (CD4+CD3+RORrt+) and Treg (CD4+CD25+Foxp3+) cell frequencies were analysed by flow cytometry, and the expression of Th17 cell- and Treg cell-related cytokines in the CD4+ T cells or culture medium was measured by quantitative PCR (qPCR) and enzyme-linked immunosorbent assay (ELISA), respectively. Neutrophil functions were determined by flow cytometry after a coculture with Th17/Treg cells. Results: The percentage of CD4+CD25+Foxp3+ cells was significantly increased in the CD4+ T cell population, while the percentage of CD4+CD3+RORrt+ cells was significantly decreased after MSC coculture. However, the MSC-induced effect was significantly inhibited by the anti-HGF antibody (p < 0.05). Furthermore, MSCs significantly inhibited the CD4+ T cell expression of IL-17 and IL-6 but increased the expression of IL-10 (p < 0.05 or p < 0.01); these effects were inhibited by the anti-HGF antibody (p < 0.05). In addition, CD4+ T cells cocultured with MSCs significantly inhibited neutrophil phagocytic and oxidative burst activities (p < 0.05 or p < 0.01); however, these MSC-induced effects were inhibited by the anti-HGF antibody (p < 0.05). Conclusion: These data suggested that MSCs induced the conversion of fully differentiated Th17 cells into functional Treg cells and thereby modulated the Th17/Treg cell balance in the CD4+ T cell population, which was partly attributed to HGF secreted by the MSCs.

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.

Background: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI are crucial. Objective: The aim of this study was to develop and validate an interpretable machine learning (ML) model that predicts persistent SA-AKI and to compare its diagnostic performance with that of C-C motif chemokine ligand 14 (CCL14) in a prospective cohort. Methods: The study used 4 retrospective cohorts and 1 prospective cohort for model derivation and validation. The derivation cohort used the MIMIC-IV database, which was randomly split into 2 parts (80% for model construction and 20% for internal validation). External validation was conducted using subsets of the MIMIC-III dataset and e-ICU dataset, and retrospective cohorts from the intensive care unit (ICU) of Northern Jiangsu People’s Hospital. Prospective data from the same ICU were used for validation and comparison with urinary CCL14 biomarker measurements. Acute kidney injury (AKI) was defined based on serum creatinine and urine output, using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Routine clinical data within the first 24 hours of ICU admission were collected, and 8 ML algorithms were used to construct the prediction model. Multiple evaluation metrics, including area under the receiver operating characteristic curve (AUC), were used to compare predictive performance. Feature importance was ranked using Shapley Additive Explanations (SHAP), and the final model was explained accordingly. In addition, the model was developed into a web-based application using the Streamlit framework to facilitate its clinical application. Results: A total of 46,097 patients with sepsis from multiple cohorts were enrolled for analysis. Among 17,928 patients with sepsis in the derivation cohort, 8081 patients (45.1%) showed progression to persistent SA-AKI. Among the 8 ML models, the gradient boosting machine (GBM) model demonstrated superior discriminative ability. Following feature importance ranking, a final interpretable GBM model comprising 12 features (AKI stage, ΔCreatinine, urine output, furosemide dose, BMI, Sequential Organ Failure Assessment score, kidney replacement therapy, mechanical ventilation, lactate, blood urea nitrogen, prothrombin time, and age) was established. The final model accurately predicted the occurrence of persistent SA-AKI in both internal (AUC=0.870) and external validation cohorts (MIMIC-III subset: AUC=0.891; e-ICU dataset: AUC=0.932; Northern Jiangsu People’s Hospital retrospective cohort: AUC=0.983). In the prospective cohort, the GBM model outperformed urinary CCL14 in predicting persistent SA-AKI (GBM AUC=0.852 vs CCL14 AUC=0.821). The model has been transformed into an online clinical tool to facilitate its application in clinical settings. Conclusions: The interpretable GBM model was shown to successfully and accurately predict the occurrence of persistent SA-AKI, demonstrating good predictive ability in both internal and external validation cohorts. Furthermore, the model was demonstrated to outperform the biomarker CCL14 in prospective cohort validation.

Background: The combination therapy of hydrocortisone, vitamin C, and thiamine has been proposed as a potential treatment in patients with sepsis and septic shock. However, subsequent trials have reported conflicting results in relation to survival outcomes. Hence, we performed this randomized controlled trial (RCT) to evaluate the efficacy and safety of early combination therapy among adult patients with septic shock. Methods: This single-center, double-blind RCT enrolled adult patients with diagnosis of septic shock within 12 h from Northern Jiangsu People's Hospital between February 2019 and June 2021. Recruited patients were randomized 1:1 to receive intervention (hydrocortisone 200 mg daily, vitamin C 2 g every 6 h, and thiamine 200 mg every 12 h) or placebo (0.9% saline) for 5 days or until ICU discharge. The primary endpoint was 90-day mortality. The secondary endpoints included mortality at day 28, ICU discharge, and hospital discharge; shock reversal; 72-h Delta SOFA score; ICU-free days, vasopressor-free days, and ventilator support -free days up to day 28; ICU length of stay (LOS) and hospital LOS. Results: Among 426 patients randomized, a total of 408 patients with septic shock were included in the per-protocol (PP) analysis, of which 203 were assigned to the intervention group and 205 to the placebo group. In the PP population, the primary outcome of 90-day mortality was 39.9% (81/203) and 39.0% (80/205) in the intervention and the placebo groups, respectively, and was not significantly different (P = 0.86). There was no significant difference between two groups in 28-day mortality (36.5% vs. 36.1%, P = 0.94) or the ICU mortality (31.5% vs. 28.8%, P = 0.55) and hospital mortality (34.5% vs. 33.2%, P = 0.78). No other secondary outcomes showed significant differences between two groups, including shock reversal, vasopressor-free days, and ICU LOS. Intention-to-treat analysis included all the 426 patients and confirmed these results (all P > 0.05). Conclusion: Among adult patients with septic shock, early use of hydrocortisone, vitamin C, and thiamine combination therapy compared with placebo did not confer survival benefits. Trial registration ClinicalTrials.gov: NCT03872011, registration date: March 12, 2019. Graphic

Restrictive red blood cell transfusion strategies remain controversial in patients undergoing cardiac surgery. We performed a meta-analysis to assess the prognostic benefits of restrictive red blood cell transfusion strategies in patients undergoing cardiac surgery. We identified randomized clinical trials through the 9th of December 2017 that investigated a restrictive red blood cell transfusion strategy versus a liberal transfusion strategy in patients undergoing cardiac surgery. Individual patient data from each study were collected. Meta-analyses were performed for the primary and secondary outcomes. The risk of bias was assessed using the Cochrane Risk of Bias Tool. A trial sequential analysis (TSA)-adjusted random-effects model was used to pool the results from the included studies for the primary outcomes. Seven trials involving a total of 8886 patients were included. The TSA evaluations suggested that this meta-analysis could draw firm negative results, and the data were sufficient. There was no evidence that the risk of 30-day mortality differed between the patients assigned to a restrictive blood cell transfusion strategy and a liberal transfusion strategy (odds ratio (OR) 0.98; 95% confidence interval (CI) 0.77 to 1.24; p = 0.87). Furthermore, the study suggested that the restrictive transfusion strategy was not associated with significant increases in pulmonary morbidity (OR 1.09; 95% CI 0.88 to 1.34; p = 0.44), postoperative infection (OR 1.11; 95% CI 0.95 to 1.3; p = 0.58), acute kidney injury (OR 1.03; 95% CI 0.92 to 1.14; p = 0.71), acute myocardial infarction (OR 1.01; 95% CI 0.80 to 1.27; p = 0.78), or cerebrovascular accidents (OR 0.97; 95% CI 0.72 to 1.30; p = 0.66). Our meta-analysis demonstrates that the restrictive red blood cell transfusion strategy was not inferior to the liberal strategy with respect to 30-day mortality, pulmonary morbidity, postoperative infection, cerebrovascular accidents, acute kidney injury, or acute myocardial infarction, and fewer red blood cells were transfused.