OBJECTIVE: To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis. METHODS: A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort. RESULTS: A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO₂; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ ² = 6.729, P = 0.566), the validation group (χ ² = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good. CONCLUSIONS OASIS, 24-hour SpO₂, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.
Humans ; Sepsis/complications* ; Retrospective Studies ; Nomograms ; Aged ; Prognosis ; Risk Factors ; Liver Diseases/mortality* ; Intensive Care Units ; ROC Curve ; Male ; Female ; Logistic Models

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

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 identify and validate novel biomarkers for the early diagnosis of sepsis-associated acute kidney injury (SA-AKI) and precise continuous renal replacement therapy (CRRT) using proteomics. METHODS: A prospective multicenter cohort study was conducted. Patients with sepsis admitted to five hospitals in Taizhou City of Zhejiang Province from April 2019 to December 2021 were continuously enrolled, based on the occurrence of acute kidney injury (AKI). Sepsis patients were divided into SA-AKI group and non-SA-AKI group, and healthy individuals who underwent physical examinations during the same period were used as control (NC group). Peripheral blood samples from participants were collected for protein mass spectrometry analysis. Differentially expressed proteins were identified, and functional enrichment analysis was conducted on these proteins. The levels of target proteins were detected by enzyme linked immunosorbent assay (ELISA), and the predictive value of target protein for SA-AKI were evaluated by receiver operator characteristic curve (ROC curve). Additionally, sepsis patients and healthy individuals were selected from one hospital to externally verify the expression level of the target protein and its predictive value for SA-AKI, as well as the accuracy of CRRT treatment. RESULTS: A total of 37 patients with sepsis (including 19 with AKI and 18 without AKI) and 31 healthy individuals were enrolled for proteomic analysis. Seven proteins were identified with significantly differential expression between the SA-AKI group and non-SA-AKI group: namely cystatin C (CST3), β ₂-microglobulin (β ₂M), insulin-like growth factor-binding protein 4 (IGFBP4), complement factor I (CFI), complement factor D (CFD), CD59, and glycoprotein prostaglandin D2 synthase (PTGDS). Functional enrichment analysis revealed that these proteins were involved in immune response, complement activation, coagulation cascade, and neutrophil degranulation. ELISA results demonstrated specific expression of each target protein in the SA-AKI group. Additionally, 65 patients with sepsis (38 with AKI and 27 without AKI) and 20 healthy individuals were selected for external validation of the 7 target proteins. ELISA results showed that there were statistically significant differences in the expression levels of CST3, β ₂M, IGFBP4, CFD, and CD59 between the SA-AKI group and non-SA-AKI group. ROC curve analysis indicated that the area under the curve (AUC) values of CST3, β ₂M, IGFBP4, CFD, and CD59 for predicting SA-AKI were 0.788, 0.723, 0.723, 0.795, and 0.836, respectively, all exceeding 0.7. Further analysis of patients who underwent CRRT or not revealed that IGFBP4 had a good predictive value, with an AUC of 0.84. CONCLUSIONS Based on proteomic analysis, CST3, β ₂M, IGFBP4, CFD, and CD59 may serve as potential biomarkers for the diagnosis of SA-AKI, among which IGFBP4 might be a potential biomarker for predicting the need for CRRT in SA-AKI patients. However, further clinical validation is required.
Humans ; Sepsis/complications* ; Acute Kidney Injury/blood* ; Proteomics ; Prospective Studies ; Biomarkers/blood* ; Male ; Female ; beta 2-Microglobulin/blood* ; Middle Aged ; Cystatin C/blood* ; Aged

OBJECTIVE: To investigate the predictive value of sphinor kinase 1 (sphk1), D-lactic acid, and intestinal fatty acid binding protein (I-FABP) for gastrointestinal injury in patients with sepsis. METHODS: A prospective observational study was conducted. Sixty-eight patients with sepsis and gastrointestinal dysfunction admitted to the department of critical care medicine of the First Affiliated Hospital of Baotou Medical College Inner Mongolia University of Science and Technology from May 2024 to March 2025 were enrolled (sepsis group), and they were divided into acute gastrointestinal injury (AGI) I-IV groups according to the definition and grading criteria of AGI proposed by the European Society of Intensive Care Medicine in 2012. Twenty non-sepsis patients without AGI admitted to the intensive care unit during the same period were enrolled as the control group (non-sepsis group). Within 30 minutes of patient enrollment, plasma sphk1, D-lactic acid, and I-FABP levels were determined by enzyme linked immunosorbent assay (ELISA). General data such as gender, age were recorded, and levels of procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), lactic acid (Lac), and acute physiology and chronic health evaluation II (APACHEII), sequential organ failure assessment (SOFA) were measured. Spearman method was used to analyze the correlation between sphk1, I-FABP, D-lactic acid and other indicators. The receiver operator characteristic curve (ROC curve) was used to evaluate the predictive value of sphk1, D-lactic acid, I-FABP, APACHEII score, and SOFA score for gastrointestinal injury in patients with sepsis. RESULTS: Among the 68 sepsis patients, 13 were classified as AGI grade I, 16 as AGI grade II, 23 as AGI grade III, and 16 had AGI grade IV. There were no statistically significant differences in gender, age, and abdominal infection rate among the groups. The SOFA score and APACHEII score of the sepsis group were significantly higher than those of the non-sepsis group; and the APACHEII score of the AGI IV group was significantly higher than that of the AGI I and AGI II groups. The levels of sphk1, D-lactic acid, I-FABP, PCT, Lac and hs-CRP in the sepsis group were significantly higher than those in the non-sepsis group, and each indicator gradually increased with the increase of AGI grade. Correlation analysis showed that plasma sphk1, D-lactic acid, and I-FABP in patients with sepsis-induced gastrointestinal injury were positively correlated with PCT, Lac, APACHEII score, and AGI grade (all P < 0.05), and sphk1 was positively correlated with I-FABP and D-lactic acid (r values were 0.773 and 0.782, respectively, both P < 0.05). ROC curve analysis showed that sphk1, D-lactic acid, I-FABP, APACHEII score, and SOFA score had high predictive value for gastrointestinal injury in patients with sepsis, with area under the curve (AUC) of 0.996, 0.987, 0.976, 0.901, and 0.934 (all P < 0.05). When the optimal cut-off value of sphk1 was 60.46 ng/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of D-lactic acid was 1 454.3 μg/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of I-FABP was 0.91 ng/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of APACHEII score was 14.5, the sensitivity and specificity were 80.9% and 85.0%, respectively; when the optimal cut-off value of SOFA score was 3.5, the sensitivity and specificity were 85.3% and 95.0%, respectively. CONCLUSIONS The levels of plasma sphk1, I-FABP, and D-lactic acid were significantly elevated in patients with sepsis and gastrointestinal injury. These indicators can serve as sensitive and relatively specific serological markers for early prediction of intestinal mucosal damage.
Humans ; Lactic Acid/blood* ; Fatty Acid-Binding Proteins/blood* ; Sepsis/complications* ; Prospective Studies ; Male ; Female ; Middle Aged ; Predictive Value of Tests ; Adult ; Aged ; Gastrointestinal Diseases/blood* ; Prognosis

OBJECTIVE: To investigate the effects of liriodendrin on the intestinal flora and the ferroptosis signaling pathway in renal tissue of rats with sepsis-induced acute kidney injury (AKI). METHODS: Thirty male Sprague-Dawley (SD) rats were randomly divided into sham operation group (Sham group), sepsis model induced by cecal ligation and puncture group (CLP group), and liriodendrin intervention group (CLP+LIR group), with 10 rats in each group. The CLP+LIR group was given 0.2 mL of 100 mg/kg liriodendrin by gavage 2 hours before modeling; Sham group and CLP group were given the same volume of normal saline by gavage. The samples were collected after anesthesia 24 hours after modeling. The pathological changes of renal tissue were observed by hematoxylin-eosin (HE) staining. The levels of inflammatory factors such as tumor necrosis factor-α (TNF-α), interleukins (IL-1β, IL-6) were detected by enzyme linked immunosorbent assay (ELISA). The levels of renal function indicators such as creatinine (Cr), and urea nitrogen (UREA) in peripheral blood, and the content of malondialdehyde (MDA) and Fe²⁺ in renal tissue were detected. Western blotting was used to detect the expressions of nuclear factor E2-related factor 2 (Nrf2), glutathione peroxidase 4 (GPX4) and heme oxygenase-1 (HO-1) in renal tissues. The changes of intestinal flora were detected by 16S rDNA high-throughput sequencing. RESULTS: Compared with the Sham group, the CLP group showed significantly enlarged glomeruli, noticeable renal interstitial edema, disorganized kidney tissue, and significantly increased pathological scores. The contents of TNF-α, IL-1β, IL-6, Cr, and UREA in peripheral blood and the levels of MDA and Fe²⁺ in renal tissue were significantly increased. The protein expressions of Nrf2, GPX4, and HO-1 in renal tissue were significantly down-regulated. The species richness of intestinal flora decreased significantly, and the relative abundances of pathogenic bacteria such as Morganella, Citrobacter, Proteus, Klebsiella, Shigella, Aggregatibacter, and Enterococcus increased significantly, while the relative abundances of beneficial bacteria such as Butyricimonas, Veillonella, Prevotella, Lactobacillus, Bifidobacterium, and Ruminococcus decreased significantly. Compared with the CLP group, CLP+LIR group could significantly reduce the pathological damage of renal tissue, the pathological score significantly decreased (1.80±0.84 vs. 4.20±1.30, P < 0.05), and improve the composition of intestinal flora, reduce the relative abundances of pathogenic bacteria such as Proteus, Klebsiella, Shigella, Aggregatibacter, and Enterococcus, and significantly increase the relative abundances of Lactobacillus, Bifidobacterium, and Ruminococcus, significantly reduce the contents of TNF-α, IL-1β, IL-6, Cr, and UREA in peripheral blood and the levels of MDA and Fe²⁺ in renal tissue [blood TNF-α (ng/L): 191.31±7.23 vs. 254.90±47.89, blood IL-1β (ng/L): 11.15±4.04 vs. 23.06±1.67, blood IL-6 (ng/L): 163.20±17.83 vs. 267.69±20.92, blood Cr (μmol/L): 24.14±4.25 vs. 41.17±5.43, blood UREA (mmol/L): 4.59±0.90 vs. 8.01±1.07, renal MDA (μmol/g): 9.67±0.46 vs. 16.05±0.88, renal Fe²⁺ (mg/g): 0.71±0.07 vs. 0.93±0.04, all P < 0.05], and increase the protein expressions of Nrf2, GPX4, and HO-1 (Nrf2/GAPDH: 1.21±0.01 vs. 0.39±0.01, GPX4/GAPDH: 0.74±0.04 vs. 0.48±0.04, HO-1/GAPDH: 0.91±0.01 vs. 0.41±0.02, all P < 0.05). CONCLUSIONS Liriodendrin has an obvious protective effect on sepsis-induced AKI. The mechanism may involve regulating the intestinal flora, increasing the activation of the Nrf2/HO-1/GPX4 signaling pathway in renal tissue, and reducing ferroptosis.
Animals ; Acute Kidney Injury/microbiology* ; Rats, Sprague-Dawley ; Sepsis/complications* ; Male ; Ferroptosis/drug effects* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Signal Transduction ; Kidney/metabolism* ; Tumor Necrosis Factor-alpha/metabolism*

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

OBJECTIVE: Baicalein has been reported to have wide therapeutic effects that act through its anti-inflammatory activity. This study examines the effect and mechanism of baicalein on sepsis-induced cardiomyopathy (SIC). METHODS: A thorough screening of a small library of natural products, comprising 100 diverse compounds, was conducted to identify the most effective drug against lipopolysaccharide (LPS)-treated H9C2 cardiomyocytes. The core target proteins and their associated signaling pathways involved in baicalein's efficacy against LPS-induced myocardial injury were predicted by network pharmacology. RESULTS: Baicalein was identified as the most potent protective agent in LPS-exposed H9C2 cardiomyocytes. It exhibited a dose-dependent inhibitory effect on cell injury and inflammation. In the LPS-induced septic mouse model, baicalein demonstrated a significant capacity to mitigate LPS-triggered myocardial deficits, inflammatory responses, and ferroptosis. Network pharmacological analysis and experimental confirmation suggested that hypoxia-inducible factor 1 subunit α (HIF1-α) is likely to be the crucial factor in mediating the impact of baicalein against LPS-induced myocardial ferroptosis and injury. By combining microRNA (miRNA) screening in LPS-treated myocardium with miRNA prediction targeting HIF1-α, we found that miR-299b-5p may serve as a regulator of HIF1-α. The reduction in miR-299b-5p levels in LPS-treated myocardium, compared to the control group, was reversed by baicalein treatment. The reverse transcription quantitative polymerase chain reaction, Western blotting, and dual-luciferase reporter gene analyses together identified HIF1-α as the target of miR-299b-5p in cardiomyocytes. CONCLUSION Baicalein mitigates SIC at the miRNA level, suggesting the therapeutic potential of it in treating SIC through the regulation of miR-299b-5p/HIF1-α/ferroptosis pathway. Please cite this article as: Zhou WY, Du JK, Liu HH, Deng L, Ma K, Xiao J, Zhang S, Wang CN. Baicalein attenuates lipopolysaccharide-induced myocardial injury by inhibiting ferroptosis via miR-299b-5p/HIF1-α pathway. J Integr Med. 2025; 23(5):560-575.
Flavanones/pharmacology* ; Animals ; MicroRNAs/genetics* ; Lipopolysaccharides ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Ferroptosis/drug effects* ; Mice ; Myocytes, Cardiac/metabolism* ; Signal Transduction/drug effects* ; Rats ; Male ; Mice, Inbred C57BL ; Cardiomyopathies/etiology* ; Cell Line ; Sepsis/complications*

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness