Sepsis is a life-threatening organ dysfunction syndrome caused by dysregulated host responses to infection. Not only does sepsis pose a serious hazard to human health, but it also imposes a substantial economic burden on the healthcare system. The cornerstones of current treatment for sepsis remain source control, fluid resuscitation, and rapid administration of antibiotics, etc. To date, no drugs have been approved for treating sepsis, and most clinical trials of potential therapies have failed to reduce mortality. The immune response caused by the pathogen is complex, resulting in a dysregulated innate and adaptive immune response that, if not promptly controlled, can lead to excessive inflammation, immunosuppression, and failure to re-establish immune homeostasis. The impaired immune response in patients with sepsis and the potential immunotherapy to modulate the immune response causing excessive inflammation or enhancing immunity suggest the importance of demonstrating individualized therapy. Here, we review the immune dysfunction caused by sepsis, where immune cell production, effector cell function, and survival are directly affected during sepsis. In addition, we discuss potential immunotherapy in septic patients and highlight the need for precise treatment according to clinical and immune stratification.

Background: An uncontrolled inflammatory response is a critical pathophysiological feature of sepsis. Mesenchymal stem cells (MSCs) induce macrophage phenotype polarization and reduce inflammation in sepsis. MSC-secreted transforming growth factor beta (TGF-β) participated in the immune modulatory function of MSCs. However, the underlying mechanism of MSC-secreted TGF-β was not fully elucidated in regulation macrophage M2-like polarization. Methods: The paracrine effects of MSCs on macrophage polarization were studied using a co-culture protocol with LPS-stimulated RAW264.7 cells/mouse peritoneal macrophages and MSCs. The effect of TGF-β in the co-culture system was blocked by the TGF-β receptor inhibitor. To determine the role of MSC-secreted TGF-β, we used recombinant TGF-β to culture with LPS-stimulated RAW264.7 cells. In addition, we employed antibody microarray analysis to determine the mechanisms of MSC secreted TGF-β on LPS-stimulated RAW264.7 cell/mouse peritoneal macrophage M2-like polarization. Furthermore, we used an Akt inhibitor and a FoxO1 inhibitor to inhibit the Akt/FoxO1 pathway. The nuclear translocation of FoxO1 was detected by Western blot. Results: MSCs induced LPS-stimulated RAW264.7 cell/mouse peritoneal macrophage polarization towards the M2-like phenotype and significantly reduced pro-inflammatory cytokine levels via paracrine, which was inhibited by TGF-β receptor inhibitor. Furthermore, we found that MSC-secreted TGF-β enhanced the macrophage phagocytic ability. The antibody microarray analysis and Western blot verified that TGF-β treatment activated the Akt/FoxO1 pathway in LPS-stimulated macrophages, TGF-β-induced FoxO1 nuclear translocation and obviously expressed in the cytoplasm, the effects of TGF-β regulatory effects on LPS-stimulated macrophage were inhibited by pre-treatment with Akt inhibitor and FoxO1 inhibitor. Conclusions: TGF-β secreted by MSCs could skew LPS-stimulated macrophage polarization towards the M2-like phenotype, reduce inflammatory reactions, and improve the phagocytic ability via the Akt/FoxO1 pathway, providing potential therapeutic strategies for sepsis.

Background: Mesenchymal stem cells (MSCs) have been shown to alleviate acute lung injury (ALI) via paracrine hepatocyte growth factor (HGF) and to induce the differentiation of dendritic cells (DCs) into tolerogenic dendritic cells (DCregs) and participate in the immune response. However, whether MSCs induce the production of DCregs by secreting HGF to alleviate early ALI remains unclear. We observed that the protective effect of mouse bone marrow-derived MSCs against lipopolysaccharide (LPS)-induced ALI was achieved by inducing mature DCs (mDCs) to differentiate into DCregs, and its mechanism is related to the activation of the HGF/Akt pathway. Methods: MSCs or MSCs with overexpression or knockdown of HGF were cocultured with DCs derived from mouse bone marrow using a Transwell system for 3 days. Moreover, we used MSCs or MSCs with overexpression or knockdown of HGF to treat LPS-induced ALI mice for 24 h. Flow cytometry was performed to measure the phagocytosis, accumulation, and maturation of DCs, as well as proliferation of T cells. Lung injury was estimated by lung wet weight to body weight ratio (LWW/BW) and histopathological analysis. Furthermore, we used the Akt inhibitor MK-2206 in a coculture system to elucidate the role of the HGF/Akt pathway in regulating the differentiation of DCs into regulatory DCs and relieving lung injury in early ALI mice. Results: Immature DCs (imDCs) were induced to mature after 24 h of LPS (50 ng/ml) stimulation. MSCs or HGF induced the differentiation of mDCs into regulatory DCs characterized by low expression of MHCII, CD86, and CD40 molecules, strong phagocytic function, and the ability to inhibit T cell proliferation. The effect of MSCs on DCregs was enhanced with the increase in HGF secretion and was weakened with the decrease in HGF secretion. DCregs induced by recombinant HGF were attenuated by the Akt inhibitor MK-2206. Lung DC aggregation and mDC ratio increased in LPS-induced ALI mice, while treatment with MSCs decreased lung DC aggregation and maturation and alleviated lung pathological injury. High expression of the HGF gene enhanced the above effect of MSCs, while decreased expression of HGF weakened the above effect of MSCs. Conclusions: MSCs alleviate early ALI via paracrine HGF by inducing mDCs to differentiate into regulatory DCs. Furthermore, the mechanism of HGF-induced differentiation of mDCs into DCregs is related to the activation of the Akt pathway.

Introduction: The aim of this study was to examine whether albumin reduced mortality when employed for the resuscitation of adult patients with severe sepsis and septic shock compared with crystalloid by meta-analysis.Methods: We searched for and gathered data from MEDLINE, Elsevier, Cochrane Central Register of Controlled Trials and Web of Science databases. Studies were eligible if they compared the effects of albumin versus crystalloid therapy on mortality in adult patients with severe sepsis and septic shock. Two reviewers extracted data independently. Disagreements were resolved by discussion with other two reviewers until a consensus was achieved. Data including mortality, sample size of the patients with severe sepsis, sample size of the patients with septic shock and resuscitation endpoints were extracted. Data were analyzed by the methods recommended by the Cochrane Collaboration Review Manager 4.2 software.Results: A total of 5,534 records were identified through the initial search. Five studies compared albumin with crystalloid. In total, 3,658 severe sepsis and 2,180 septic shock patients were included in the meta-analysis. The heterogeneity was determined to be non-significant (P = 0.86, I² = 0%). Compared with crystalloid, a trend toward reduced 90-day mortality was observed in severe sepsis patients resuscitated with albumin (odds ratio (OR) 0.88; 95% CI, 0.76 to 1.01;P = 0.08). However, the use of albumin for resuscitation significantly decreased 90-day mortality in septic shock patients (OR 0.81; 95% CI, 0.67 to 0.97;P = 0.03). Compared with saline, the use of albumin for resuscitation slightly improved outcome in severe sepsis patients (OR 0.81; 95% CI, 0.64 to 1.08;P = 0.09).Conclusions: In this meta-analysis, a trend toward reduced 90-day mortality was observed in severe sepsis patients resuscitated with albumin compared with crystalloid and saline. Moreover, the 90-day mortality of patients with septic shock decreased significantly.

To measure the ability of a new bioscore to diagnose sepsis in a general critical care population. The study was done at an intensive care unit (ICU) from April to December 2012. Demographic and clinical patient information were recorded on admission to the ICU with blood samples taken for C-reactive protein (CRP), procalcitonin (PCT), interleukin-6, white blood cell count, as well as body temperature, age and the sepsis-related organ failure (SOFA) score. These parameters were used to create a scoring system. The scoring system then underwent analysis by univariate analysis and multivariate logistic regression analysis to identify which of these clinical parameters were statistically different in septic versus non-septic patients. The bioscore was then tested in a receiver operator characteristic curve to determine statistical significance of the scoring systems ability to predict sepsis. Finally, a bioscore cutoff value was defined to provide a level for sepsis diagnosis. Three hundred patients were enrolled, of which 107 patients were septic and 193 patients were non-septic. Univariate logistic regression showed that age, gender, CRP, PCT and SOFA were risk factors for occurrence of sepsis. Multivariate analysis revealed CRP (AUC 0.729, 95 % CI 0.671–0.787, P < 0.001), PCT (AUC 0.711, 95 % CI 0.652–0.770, P < 0.001) and SOFA (AUC 0.670, 95 % CI 0.607–0.733, P < 0.001) to be statistically significant. The combination of these values in the bioscore had an AUC of 0.790 (95 % CI 0.739–0.834, P < 0.001). A bioscore of ≥2.65 was considered to be statistically significant in making a positive diagnosis of sepsis. This bioscore using CRP, PCT and SOFA score may potentially be used in the future to help identify septic patients earlier, improving their access to timely treatment modalities.

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.