Inflammation plays an important role during sepsis, and excessive inflammation can result in organ damage, chronic inflammation, fibrosis, and scarring. The study aimed to investigate the specific mechanism of emodin by constructing in vivo and in vitro septic lung injury models via inhibition and reduction of NF-kB and high mobility group box 1 (HMGB1) pathways. A cecal ligation and puncture (CLP) model was built for adult male Sprague-Dawley rats. Concentrations of TNF-α, IL-1β, and IL-6 in bronchoalveolar lavage fluid were determined using commercially available ELISA kits. Hematoxylin and eosin staining was used for the right lung inferior lobes. Myeloperoxidase (MPO) activity of the lung tissue was detected by using the MPO kit. Murine alveolar epithelial cell line (MLE-12) cells were used for flow cytometry and Western blot to analyze the apoptosis rate and protein expression. Emodin significantly decreased CLP-induced cell apoptosis, upregulated expression of sirtuin 1 (SIRT1), and inhibited p-p65/p65 and HMGB1. In lipopolysaccharide (LPS) treated cell model, emodin treatment markedly decreased LPS-induced release of IL-1, IL-6, and tumor necrosis factor (TNF)-α, inhibited LPS-induced cell apoptosis and suppressed protein levels of P-P65/P65 and HMGB1. However, science of SIRT1 reversed the above effects by treatment of emodin. In summarize, this study found that emodin can alleviate sepsis-induced lung injury in vivo and in vitro through regulation of SIRT1.
Background: No predictive models are currently available to predict poor prognosis in patients with severe heatstroke. We aimed to establish a predictive model to help clinicians identify the risk of death and customize individualized treatment. Methods: The medical records and data of 115 patients with severe heatstroke hospitalized in the intensive care unit of Changzhou No. 2 People's Hospital between June 2013 and September 2019 were retrospectively analyzed for modeling. Furthermore, data of 84 patients with severe heatstroke treated at Jintan No. 1 People's Hospital from June 2013 to 2021 were retrospectively analyzed for external verification of the model. We analyzed the hematological parameters of the patients recorded within 24 h of admission, which included routine blood tests, liver function, renal function, coagulation routine, and myocardial enzyme levels. Risk factors related to death in patients with severe heatstroke were screened using Least Absolute Shrinkage and Selection Operator regression. The independent variable risk ratio for death was investigated using the Cox univariate and multivariate regression analyses. The nomogram was subsequently used to establish a suitable prediction model. A receiver operating characteristic curve was drawn to evaluate the predictive power of the prediction model and the Acute Physiology and Chronic Health Evaluation (APACHE II) score. In addition, decision curve analysis was established to assess the clinical net benefit. The advantages and disadvantages of both models were evaluated using the integrated discrimination improvement and Net Reclassification Index. A calibration curve was constructed to assess predictive power and actual conditions. The external data sets were used to verify the predictive accuracy of the model. Results: All independent variables screened by Least Absolute Shrinkage and Selection Operator regression were independent risk factors for death in patients with severe heatstroke, which included neutrophil/lymphocyte ratio, platelet (PLT), troponin I, creatine kinase myocardial band, lactate dehydrogenase, human serum albumin, D-dimer, and APACHE-II scores. On days 10 and 30, the integrated discrimination improvement of the prediction model established was 0.311 and 0.364 times higher than that of the APACHE-II score, respectively; and the continuous Net Reclassification Index was 0.568 and 0.482 times higher than that of APACHE-II, respectively. Furthermore, we established that the area under the curve (AUC) of the prediction model was 0.905 and 0.918 on days 10 and 30, respectively. Decision curve analysis revealed that the AUC of this model was 7.67% and 10.67% on days 10 and 30, respectively. The calibration curve showed that the predicted conditions suitably fit the actual requirements. External data verification showed that the AUC on day 10 indicated by the prediction model was 0.908 (95% confidence interval, 82.2-99.4), and the AUC on day 30 was 0.930 (95% confidence interval, 0.860-0.999). Conclusion: The survival rate of pa...
Our findings suggested that STIM1/Orai1 overexpression could affect the cell permeability and the expression of partial podocyte-associated proteins, which may ultimately result in podocyte injury.
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