This study aims to investigate the mechanism by which resveratrol(RES) alleviates cerebral vascular endothelial damage in sepsis-associated encephalopathy(SAE) through network pharmacology and animal experiments. By using network pharmacology, the study identified common targets and genes associated with RES and SAE and constructed a protein-protein interaction( PPI) network. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed to pinpoint key signaling pathways, followed by molecular docking validation. In the animal experiments, a cecum ligation and puncture(CLP) method was employed to induce SAE in mice. The mice were randomly assigned to the sham group, CLP group, and medium-dose and high-dose groups of RES. The sham group underwent open surgery without CLP, and the CLP group received an intraperitoneal injection of 0. 9% sodium chloride solution after surgery. The medium-dose and high-dose groups of RES were injected intraperitoneally with 40 mg·kg-1 and 60 mg·kg~(-1) of RES after modeling, respectively, and samples were collected 12 hours later. Neurological function scores were assessed, and the wet-dry weight ratio of brain tissue was detected. Serum superoxide dismutase(SOD), catalase( CAT) activity, and malondialdehyde( MDA) content were measured by oxidative stress kit. Histopathological changes in brain tissue were examined using hematoxylin-eosin(HE) staining. Transmission electron microscopy was employed to evaluate tight cell junctions and mitochondrial ultrastructure changes in cerebral vascular endothelium. Western blot analysis was performed to detect the expression of zonula occludens1( ZO-1), occludin, claudins-5, optic atrophy 1( OPA1), mitofusin 2(Mfn2), dynamin-related protein 1(Drp1), fission 1(Fis1), and hypoxia-inducible factor-1α(HIF-1α). Network pharmacology identified 76 intersecting targets for RES and SAE, with the top five core targets being EGFR, PTGS2, ESR1, HIF-1α, and APP. GO enrichment analysis showed that RES participated in the SAE mechanism through oxidative stress reaction. KEGG enrichment analysis indicated that RES participated in SAE therapy through HIF-1α, Rap1, and other signaling pathways. Molecular docking results showed favorable docking activity between RES and key targets such as HIF-1α. Animal experiment results demonstrated that compared to the sham group, the CLP group exhibited reduced nervous reflexes, decreased water content in brain tissue, as well as serum SOD and CAT activity, and increased MDA content. In addition, the CLP group exhibited disrupted tight junctions in cerebral vascular endothelium and abnormal mitochondrial morphology. The protein expression levels of Drp1, Fis1, and HIF-1α in brain tissue were increased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were decreased. In contrast, the medium-dose and high-dose groups of RES showed improved neurological function, increased water content in brain tissue and SOD and CAT activity, and decreased MDA content. Cell morphology in brain tissue, tight junctions between endothelial cells, and mitochondrial structure were improved. The protein expressions of Drp1, Fis1, and HIF-1α were decreased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were increased. This study suggested that RES could ameliorate cerebrovascular endothelial barrier function and maintain mitochondrial homeostasis by inhibiting oxidative stress after SAE damage, potentially through modulation of the HIF-1α signaling pathway.
Animals ; Mice ; Network Pharmacology ; Resveratrol/administration & dosage* ; Male ; Sepsis-Associated Encephalopathy/genetics* ; Signal Transduction/drug effects* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Endothelium, Vascular/metabolism* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Humans ; Sepsis/complications* ; Oxidative Stress/drug effects*

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, referring to a diffuse brain dysfunction caused by sepsis in the absence of direct central nervous system (CNS) infection. SAE occurs in up to 70% of patients with sepsis. Globally, the annual incidence of sepsis ranges from 30.0 to 48.9 million cases, resulting in approximately 11 million deaths per year, which accounts for 20% of all global mortalities. SAE is identified as an independent risk factor contributing to the increased mortality rate among these patients. Early diagnosis of SAE and related cerebral protection interventions hold significant clinical importance. Currently, the main indicators of brain function for sepsis patients include Glasgow coma score (GCS), confusion assessment method for the intensive care unit (CAM-ICU), electroencephalogram (EEG), brain CT or magnetic resonance imaging (MRI) and other related imaging changes, which have the problems of low sensitivity, poor specificity, and non-objective evaluation of the results of the diagnosis of SAE. This article focuses on the latest progress in the pathogenesis of SAE and systematically reviews potential biomarkers related to the onset of SAE from multiple aspects, including inflammatory markers, endothelial and neuronal injury markers, and metabolic markers. This will provide new insights for the clinical diagnosis and treatment of SAE.
Humans ; Sepsis-Associated Encephalopathy/therapy* ; Biomarkers ; Sepsis/complications* ; Magnetic Resonance Imaging ; Electroencephalography ; Brain Diseases/etiology*

Sepsis-associated encephalopathy (SAE) is one of the complications of sepsis, causes cognitive dysfunction ranging from mild attention deficits to progression into coma, which severely impairs patients' ability to live and mental health, and increases the long-term disability and mortality rates. Although the clinical attention to SAE has been increasing in recent years, effective interventions to improve cognitive dysfunction in sepsis survivors are still in the preclinical stage. The pathogenesis of SAE is numerous and complex, and mitochondrial dysfunction, as one of the key pathogenic mechanisms, plays a role in the cognitive development process through oxidative stress imbalance, energy metabolism disorders, and activation of apoptosis signaling pathway. The present review systematically integrates the recent studies on mitochondrial dysfunction in the development of cognitive disorders. This review systematically integrates the cutting-edge research results in recent years, discusses the mitochondrial structural disruption, mitochondrial kinetic abnormalities, respiratory chain dysfunction, and comprehensively comprehends the research progress of mitochondria-targeted antioxidant, mitochondrial autophagy activator, mitochondrial biosynthesis modifier and other novel intervention strategies in improving cognitive function of SAE patients, with the aim of providing theoretical basis for the breakthrough of the current status of clinical treatment of SAE and the targeting of mitochondria for treatment. The aim is to provide theoretical basis for breaking through the status of SAE clinical treatment and targeting mitochondrial therapy.
Humans ; Sepsis-Associated Encephalopathy/metabolism* ; Mitochondria/metabolism* ; Sepsis/complications* ; Oxidative Stress ; Cognitive Dysfunction ; Autophagy

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Sepsis-associated encephalopathy(SAE) caused by infections outside the central nervous system always presents extensive brain damage.It is common in clinical practice and associated with a poor prognosis.There are problems in the assessing and diagnosing of SAE.Many factors,such as sedation and mechanical ventilation,make it difficult to assess SAE,while electrophysiological examination may play a role in the assessment.We reviewed the studies of electrophysiological techniques such as electroencephalography and somatosensory evoked potentials for monitoring SAE,hoping to provide certain evidence for the clinical evaluation and diagnosis of SAE.
Humans ; Sepsis-Associated Encephalopathy/complications* ; Sepsis/diagnosis* ; Electroencephalography

Objective: To explore the value of cerebral hypoxic-ischemic injury markers in the early diagnosis of sepsis associated encephalopathy (SAE) in burn patients with sepsis. Methods: A retrospective case series study was conducted. From October 2018 to May 2021, 41 burn patients with sepsis who were admitted to Zhengzhou First People's Hospital met the inclusion criteria, including 23 males and 18 females, aged 18-65 (35±3) years. According to whether SAE occurred during hospitalization, the patients were divided into SAE group (21 cases) and non-SAE group (20 cases). The gender, age, deep partial-thickness burn area, full-thickness burn area, and acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) scores of patients were compared between the two groups. The serum levels of central nervous system specific protein S100β and neuron specific enolase (NSE) at 12, 24, and 48 h after sepsis diagnosis (hereinafter referred to as after diagnosis), the serum levels of interleukin-6 (IL-6), IL-10, tumor necrosis factor α (TNF-α), Tau protein, adrenocorticotropic hormone (ACTH), and cortisol at 12, 24, 48, 72, 120, and 168 h after diagnosis, and the mean blood flow velocity of middle cerebral artery (VmMCA), pulsatility index, and cerebral blood flow index (CBFi) on 1, 3, and 7 d after diagnosis of patients in the two groups were counted. Data were statistically analyzed with chi-square test, analysis of variance for repeated measurement, independent sample t test, and Bonferroni correction. The independent variables to predict the occurrence of SAE was screened by multi-factor logistic regression analysis. The receiver operating characteristic (ROC) curve was drawn for predicting the occurrence of SAE in burn patients with sepsis, and the area under the curve (AUC), the best threshold, and the sensitivity and specificity under the best threshold were calculated. Results: The gender, age, deep partial-thickness burn area, full-thickness burn area, and APACHE Ⅱ score of patients in the two groups were all similar (χ²=0.02, with t values of 0.71, 1.59, 0.91, and 1.07, respectively, P>0.05). At 12, 24, and 48 h after diagnosis, the serum levels of S100β and NSE of patients in SAE group were all significantly higher than those in non-SAE group (with t values of 37.74, 77.84, 44.16, 22.51, 38.76, and 29.31, respectively, P<0.01). At 12, 24, 48, 72, 120, and 168 h after diagnosis, the serum levels of IL-10, Tau protein, and ACTH of patients in SAE group were all significantly higher than those in non-SAE group (with t values of 10.68, 13.50, 10.59, 8.09, 7.17, 4.71, 5.51, 3.20, 3.61, 3.58, 3.28, 4.21, 5.91, 5.66, 4.98, 4.69, 4.78, and 2.97, respectively, P<0.01). At 12, 24, 48, 72, and 120 h after diagnosis, the serum levels of IL-6 and TNF-α of patients in SAE group were all significantly higher than those in non-SAE group (with t values of 8.56, 7.32, 2.08, 2.53, 3.37, 4.44, 5.36, 5.35, 6.85, and 5.15, respectively, P<0.05 or P<0.01). At 12, 24, and 48 h after diagnosis, the serum level of cortisol of patients in SAE group was significantly higher than that in non-SAE group (with t values of 5.44, 5.46, and 3.55, respectively, P<0.01). On 1 d after diagnosis, the VmMCA and CBFi of patients in SAE group were significantly lower than those in non-SAE group (with t values of 2.94 and 2.67, respectively, P<0.05). On 1, 3, and 7 d after diagnosis, the pulsatile index of patients in SAE group was significantly higher than that in non-SAE group (with t values of 2.56, 3.20, and 3.12, respectively, P<0.05 or P<0.01). Serum IL-6 at 12 h after diagnosis, serum Tau protein at 24 h after diagnosis, serum ACTH at 24 h after diagnosis, and serum cortisol at 24 h after diagnosis were the independent risk factors for SAE complicated in burn patients with sepsis (with odds ratios of 2.42, 1.38, 4.29, and 4.19, 95% confidence interval of 1.76-3.82, 1.06-2.45, 1.37-6.68, and 3.32-8.79, respectively, P<0.01). For 41 burn patients with sepsis, the AUC of ROC of serum IL-6 at 12 h after diagnosis for predicting SAE was 0.92 (95% confidence interval was 0.84-1.00), the best threshold was 157 pg/mL, the sensitivity was 81%, and the specificity was 89%. The AUC of ROC of serum Tau protein at 24 h after diagnosis for predicting SAE was 0.92 (95% confidence interval was 0.82-1.00), the best threshold was 6.4 pg/mL, the sensitivity was 97%, and the specificity was 99%. The AUC of ROC of serum ACTH at 24 h after diagnosis for predicting SAE was 0.96 (95% confidence interval was 0.89-1.00), the best threshold was 14.7 pg/mL, the sensitivity was 90%, and the specificity was 94%. The AUC of ROC of serum cortisol at 24 h after diagnosis for predicting SAE was 0.93 (95% confidence interval was 0.86-1.00), the best threshold was 89 nmol/L, the sensitivity was 94%, and the specificity was 97%. Conclusions: Serum Tau protein, ACTH, and cortisol have high clinical diagnostic value for SAE complicated in burn patients with sepsis.
Adolescent ; Adult ; Aged ; Burns/complications* ; Early Diagnosis ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; ROC Curve ; Retrospective Studies ; Sepsis/diagnosis* ; Sepsis-Associated Encephalopathy ; Young Adult

BACKGROUNDBrain dysfunction is a frequent complication of sepsis, usually defined as sepsis-associated encephalopathy (SAE). Although the Notch signaling pathway has been proven to be involved in both ischemia and neuronal proliferation, its role in SAE is still unknown. Here, the effect of the Notch signaling pathway involved γ-secretase inhibitor DAPT on SAE in septic rats was investigated in a cecal ligation and puncture (CLP) model.

METHODSFifty-nine Sprague-Dawley rats were randomly divided into four groups, with the septic group receiving the CLP operation. Twenty-four hours after CLP or sham treatment, rats were sacrificed and their hippocampus was harvested for Western blot analysis. TNF-α expression was determined using an enzyme-linked immunosorbent assay (ELISA) kit. Neuronal apoptosis was assessed by TUNEL staining, and neuronal cell death was detected by H&E staining. Finally, a novel object recognition experiment was used to evaluate memory impairment.

RESULTSOur data showed that sepsis can increase the expression of hippocampal Notch receptor intracellular domain (NICD) and poly (adenosine diphosphate [ADP]-ribose) polymerase-1 (PARP-1), as well as the inflammatory response, neuronal apoptosis, neuronal death, and memory dysfunction in rats. The γ-secretase inhibitor N-[N-(3,5-difluorophenacetyl)-1-alanyl]-S-phenylglycine t-butyl ester (DAPT) can significantly decrease the level of NICD and PARP-1, reduce hippocampal neuronal apoptosis and death, attenuate TNF-α release and rescue cognitive impairment caused by CLP.

CONCLUSIONThe neuroprotective effect of DAPT on neuronal death and memory impairment in septic rats, which could be a new therapeutic approach for treating SAE in the future.

Amyloid Precursor Protein Secretases ; antagonists & inhibitors ; Animals ; Apoptosis ; drug effects ; Dipeptides ; therapeutic use ; Hippocampus ; drug effects ; metabolism ; Male ; Neurons ; cytology ; drug effects ; Neuroprotective Agents ; Poly (ADP-Ribose) Polymerase-1 ; Poly(ADP-ribose) Polymerases ; metabolism ; Rats ; Rats, Sprague-Dawley ; Receptors, Notch ; metabolism ; Sepsis ; complications ; Sepsis-Associated Encephalopathy ; drug therapy ; enzymology ; Signal Transduction ; drug effects