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This study tested whether combined therapy with melatonin and apoptotic adipose-derived mesenchymal stem cells (A-ADMSCs) offered additional benefit in ameliorating sepsis-induced acute kidney injury. Adult male Sprague-Dawley rats (n = 65) were randomized equally into five groups: Sham controls (SC), sepsis induced by cecal-ligation and puncture (CLP), CLP-melatonin, CLP-A-ADMSC, and CLP-melatonin-A-ADMSC. Circulating TNF-α level at post-CLP 6 hr was highest in CLP and lowest in SC groups, higher in CLP-melatonin than in CLP-A-ADMSC and CLP-melatonin-A-ADMSC groups (all P < 0.001). Immune reactivity as reflected in the number of splenic helper-, cytoxic-, and regulatory-T cells at post-CLP 72 hr exhibited the same pattern as that of circulating TNF-α among all groups (P < 0.001). The histological scoring of kidney injury and the number of F4/80+ and CD14+ cells in kidney were highest in CLP and lowest in SC groups, higher in CLP-melatonin than in CLP-A-ADMSC and CLP-melatonin-A-ADMSC groups, and higher in CLP-A-ADMSC than in CLP-melatonin-A-ADMSC groups (all P < 0.001). Changes in protein expressions of inflammatory (RANTES, TNF-1α, NF-κB, MMP-9, MIP-1, IL-1β), apoptotic (cleaved caspase 3 and PARP, mitochondrial Bax), fibrotic (Smad3, TGF-β) markers, reactive-oxygen-species (NOX-1, NOX-2), and oxidative stress displayed a pattern identical to that of kidney injury score among the five groups (all P < 0.001). Expressions of antioxidants (GR+, GPx+, HO-1, NQO-1+) were lowest in SC group and highest in CLP-melatonin-A-ADMSC group, lower in CLP than in CLP-melatonin and CLP-A-ADMSC groups, and lower in CLP-melatonin- than in CLP-A-ADMSC-tretaed animals (all P < 0.001). In conclusion, combined treatment with melatonin and A-ADMSC was superior to A-ADMSC alone in protecting the kidneys from sepsis-induced injury., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Abstract Background Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. Methods Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. Results A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p

Background: Some studies have reported to use some predictors before extracorporeal membrane oxygenation (ECMO) initiation to predict the acute kidney injury (AKI) risk. However, injury during the ECMO operation and the response of patients to ECMO may significantly influence the prognosis, and they are unpredictable before ECMO initiation. This study aims to develop a potential model based clinical characteristics at the 2-hour time point during ECMO for the early prediction of AKI in patients receiving ECMO. Methods: 139 patients who underwent ECMO were enrolled in this study. The clinical characteristics and the laboratory examinations at 2-hour time point during ECMO were recorded. The least absolute shrinkage and selection operator (LASSO) regression method was performed to select predictors, and logistic regression and a nomogram were used to establish the prediction model. The area under curve (AUC) of the receiver operating characteristic and calibration curve were used to analyze the discrimination and calibration of the model. K-fold cross-validation method was performed to validate the accuracy of this model. Results: Among the 139 patients receiving ECMO, 106 participants (76.26%) developed AKI. Four predictive variables including ECMO model, serum creatinine (Scr-2h), uric acid(UA-2h), and serum lactate (Lac-2h) at the 2-hour time point during ECMO were filtered from 39 clinical parameters by LASSO regression. These four predictors were incorporated to develop a model for predicting AKI risk using logistic regression. The AUC of the model was 0.905 (0.845–0.965), corresponding to 81.1% sensitivity, 90.9% specificity and 83.5% accuracy. Moreover, this model showed good consistency between observed and predicted probability based on the calibration curve (P > 0.05). The validation performed by K-fold cross-validation method showed that the accuracy was 0.874 ± 0.006 in training sets, 0.827 ± 0.053 in test sets, indicating a good capability for AKI risk prediction. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. Conclusion: The nomogram incorporating Scr-2h,Lac-2h, UA-2h, and ECMO model may facilitate the individualized prediction of the AKI risk among patients undergoing ECMO.