Abstract:Objective To investigate the predictive value of standardized lactic acid load on acute kidney injury(AKI)after hip fracture surgery in the elderly.Methods A total of 100 elderly patients with hip fracture who underwent surgical treatment in our hospital from August 2023 to August 2025 were retrospectively included.Based on the occurrence of postoperative AKI,the patients were categorized into an AKI group comprising 26 cases and a non-AKI group with 74 cases.The general data,operation-related indicators and secondary outcome indicators of patients were collected.Logistic regression,both univariate and multivariate,was employed to assess the risk factors associated with postoperative AKI.The predictive performance of standardized lactic acid load for postoperative AKI was evaluated using the receiver operating characteristic(ROC)curve,and the secondary outcomes were compared according to the optimal cut-off value.Results Univariate analysis showed that preoperative Barthel index and albumin(ALB)level in AKI group were lower than those in non-AKI group,while serum creatinine(Scr)level and standardized lactic acid load were higher than those in non-AKI group(P<0.05).The multivariate Logistic regression analysis revealed that both preoperative Scr levels and standardized lactic acid load were independent risk factors for postoperative AKI.ROC curve analysis showed that the AUC of standardized lactate load for predicting postoperative AKI was 0.842,and the optimal cut-off value was 6.85 mmol·h/L,with a sensitivity of 60.0%and a specificity of 96.7%.Further grouping according to the cut-off value,the length of postoperative hospital stay,incidence of complications within 30 days and mortality in the high standardized lactate load group(n=32)were higher than those in the low standardized lactate load group(n=68)(P<0.05).Conclusions In elderly patients suffering from hip fracture,standardized lactate load emerges as a risk factor for postoperative AKI.It has a high predictive efficacy for postoperative AKI and can indicate postoperative adverse outcomes.It can be used as an important indicator for early clinical screening of high-risk patients.

Abstract:Sepsis-induced myocardial dysfunction(SIMD)is a critical independent risk factor for mortality in patients with sepsis,yet its complex pathogenesis and the lack of specific treatments remain major clinical challenges.Existing research reveals that the pathogenesis of SIMD is closely associated with a vicious cycle network formed by dysregulated immune recognition-dysfunctional metabolic reprogramming-activation of programmed cell death.This review synthesizes current knowledge on the molecular mechanisms underlying the interaction between immunometabolic disturbances and various forms of cell death in SIMD,focusing on the synergistic damaging effects of pyroptosis and ferroptosis.It also reviews preclinical evidence and translational prospects for novel therapeutic strategies targeting the nucleotide-binding oligomerization domain-like receptor protein 3(NLRP3)inflammasome,ferroptosis pathways,mitochondrial function,and sodium-glucose cotransporter 2(SGLT2)inhibitors.Furthermore,by integrating advances in novel circulating biomarkers such as soluble suppression of tumorigenicity 2(sST2)and growth differentiation factor-15(GDF-15),and imaging techniques like speckle tracking echocardiography(STE),this article explores future directions for precise phenotyping and personalized therapy of SIMD.Targeted intervention in the immunity-metabolism-cell death network offers a promising framework for overcoming the therapeutic impasse in SIMD,providing new theoretical foundations and potential clinical strategies.

Abstract:Objective This study aims to develop and validate a machine learning model integrating multimodal structured data for predicting in-hospital mortality in sepsis patients within an emergency department cohort.Methods The single-center retrospective cohort study included 177 patients with sepsis in the emergency department form The Third People's Hospital of Chengdu between September 2021 and August 2023.Multimodal clinical data were systematically collected,with missing values imputed using median for continuous variables and mode for categorical variables,with missing indicator variables incorporated.SOFA and qSOFA scores,along with LightGBM,XGBoost,and random forest models,were constructed.Model performance was assessed using AUROC,AUPRC,and Brier score,with SHAP analysis for interpretability.Results LightGBM demonstrated superior discrimination ability(AUROC0.893,95%CI 0.774-0.990),significantly outperforming SOFA(0.535)and qSOFA(0.586)scores(P<0.05).Random forest exhibited optimal performance in positive class prediction(AUPRC 0.687)and calibration(Brier score 0.098).SHAP analysis revealed key predictors:lactate level(>2.0 mmol/L,SHAP value+0.32),PaO/FiO2 ratio(<200 mmHg,SHAP value+0.28),vasopressor use(SHAP contribution+0.25),and age(risk increase of 0.015 per year).The synergistic effect of lactate and PaO/FiO2 ratio yielded a combined SHAP value of+0.60,significantly exceeding the sum of individual effects.Conclusions Machine learning models integrating multimodal data significantly improve the accuracy of in-hospital mortality prediction in sepsis compared to traditional scoring systems.The model's interpretability provides a practical tool for risk stratification,supporting precision decision-making in emergency care and demonstrating substantial translational potential.

Abstract:Objective To investigate the value of follistatin-like protein 1(FSTL-1)combined with albumin-bilirubin(ALBI)score in predicting the prognosis of patients with acute decompensated left heart failure in intensive care unit(ICU).Methods A total of 330 patients with acute decompensated left heart failure in ICU of Xuzhou Mining Group General Hospital from June 2022 to May 2025 were prospectively selected as the research objects.According to the admission and death of heart failure 3 months after discharge as the criteria for poor prognosis,the patients were divided into poor prognosis group and good prognosis group.The serum FSTL-1 level of the two groups was detected,and the ALBI score was calculated.Logistic regression equation was used to analyze the prognostic factors of patients with acute decompensated left heart failure in ICU.The receiver operating characteristic(ROC)curve was drawn to analyze the prognostic value of FSTL-1 combined with ALBI score in patients with acute decompensated left heart failure in ICU,and external verification was performed in another 105 patients with acute decompensated left heart failure from Xuzhou First People's Hospital.Results Three months after discharge,a total of 50 patients were admitted to hospital with heart failure again,and 25 patients died.The scores of NT-proBNP,cTnI,LAD,FSTL-1 and ALBI in the poor prognosis group were higher than those in the good prognosis group,and LVEF was lower than that in the good prognosis group(P<0.05).NT-proBNP,cTnI,LVEF,LAD,FSTL-1 and ALBI scores were all adverse prognostic factors in patients with acute decompensated left heart failure(P<0.05).The AUC of FSTL-1 and ALBI score in predicting the prognosis of patients was 0.743 and 0.767,which was not significantly different from that of NT-proBNP,cTnI,LVEF and LAD(P>0.05).The AUC of FSTL-1 combined with ALBI score in predicting the prognosis of patients with acute decompensated left heart failure was 0.855,which was significantly higher than that of single prediction(P<0.05).The AUC of the combined model of FSTL-1 and ALBI score in the external validation set was 0.856,and the accuracy was 83.81%.Conclusions The combination of FSTL-1 and ALBI score has high prognostic efficacy for acute decompensated left heart failure in ICU.The combination of the two can help to achieve early warning and guide the selection of individualized treatment strategies.

Abstract:Objective This study aims to systematically investigate the molecular link between the environmental toxin aflatoxin B1(AFB1)and sepsis by integrating bioinformatics and machine learning approaches,identify key overlapping genes and core biomarkers,and explore the potential mechanisms by which AFB1 influences the progression of sepsis.Methods Sepsis-related transcriptomic datasets were obtained from the GEO database,merged,and subjected to batch effect correction before being divided into training and test sets.Sepsis-associated genes were screened through differential expression analysis and weighted gene co-expression network analysis(WGCNA),and potential targets of AFB1 were predicted from multiple databases.The intersection of these gene sets yielded overlapping genes.Gene ontology(GO)and kyoto encyclopedia of genes and genomes(KEGG)enrichment analyses were performed on the overlapping genes,and a protein-protein interaction(PPI)network was constructed to identify core genes.Multiple machine learning algorithms were employed to develop a diagnostic model,which was validated on an independent dataset.SHAP interpretability analysis was employed to identify the key biomarker with the highest contribution.Molecular docking was performed to assess the binding potential between the screened core biomarker and AFB1,with binding energy calculated to evaluate affinity.Results A total of 55 overlapping genes between AFB1 and sepsis were identified,which were significantly enriched in immune and inflammatory response pathways.The PPI network revealed 20 core genes,including EGFR,TP53,and SRC.The machine learning model(glmBoost+StepGlm[both]demonstrated excellent diagnostic performance(AUC=0.974).SHAP analysis identified GAPDH as the biomarker with the highest diagnostic value(AUC=0.901).Molecular docking confirmed a strong binding potential between AFB1 and the GAPDH protein(binding energy=-10.5 kcal/mol).Conclusions This study suggests that AFB1 may increase susceptibility to sepsis by directly targeting key molecules such as GAPDH,thereby disrupting immune homeostasis.GAPDH was identified as a valuable biomarker,providing new insights into the role of environmental toxins in sepsis pathogenesis and offering potential targets for risk assessment and early warning.

Abstract:Objective This study aims to evaluate the prognostic value of global longitudinal strain(GLS)measured by speckle-tracking echocardiography in patients with sepsis,to compare it with conventional left ventricular ejection fraction(LVEF),and its predictive performance was further validated using machine learning approaches.Methods A prospective cohort of 119 sepsis patients admitted to the intensive care unit(ICU)underwent transthoracic echocardiography within 24 hours of admission for LVEF and GLS measurements.Patients were followed for 3 months.Clinical characteristics between survivors and non-survivors were compared using t-tests,analysis of variance,or rank-sum tests as appropriate.Multivariable Logistic regression was conducted to assess the independent predictive value of GLS and LVEF for mortality,and receiver operating characteristic(ROC)curves were constructed to compare predictive value.In addition,multiple machine learning algorithms,including random forest(RF),support vector machine(SVM),light gradient boosting machine(Light GBM),decision tree(DT),and k-nearest neighbors(KNN),were applied to validate the regression findings.Results In multivariable Logistic regression,septic shock was associated with a 25.625-fold increased risk of mortality compared with non-shock patients(95%CI 3.882-497.752,P=0.005),and this association remained stable after including EF in the model(OR=25.416,95%CI 3.862-489.217,P=0.005).Vasopressin use was consistently associated with elevated mortality risk across all models(Model 1:OR=11.803,95%CI 2.824-63.514,P=0.002;Model 3:OR=12.118,95%CI 2.860-67.301,P=0.002).Myocardial injury was also significantly associated with higher mortality(Model 1:OR=7.806,95%CI 1.449-72.705,P=0.034;Model 3:OR=8.586,95%CI 1.555-82.209,P=0.029).ROC curve analysis demonstrated that GLS exhibited superior predictive performance(AUC=0.911)compared with LVEF(AUC=0.906),and the combination of GLS and LVEF achieved the highest predictive accuracy(AUC=0.916).Among machine learning models,the KNN classifier incorporating both GLS and LVEF performed best,with an AUC of 0.968,a Kappa coefficient of 0.77,and balanced sensitivity(0.91)and specificity(0.91).Conclusions GLS is an independent predictor of 3-month mortality in sepsis patients,outperforming conventional LVEF.The combined application of GLS and LVEF further improves predictive performance,particularly when integrated into a KNN machine learning model,highlighting its potential for enhancing clinical risk stratification and guiding personalized management in sepsis.

Abstract:Objective To evaluate the predictive value of pH-corrected calcium concentration combined with rSIG index for coagulation dysfunction in children with multiple trauma.Methods Clinical data of 164 pediatric patients with multiple trauma admitted to the emergency department of our hospital from July 2020 to November 2025 were retrospectively collected.According to whether coagulation dysfunction occurred within 24 hours of admission,they were divided into coagulation dysfunction group(n=39)and non-coagulation dysfunction group(n=125).pH-corrected calcium concentration and rSIG index were calculated.Multivariate Logistic regression was used to analyze the independent influencing factors of coagulation dysfunction.The predictive value of pH-corrected calcium,rSIG alone and combined application was evaluated by receiver operating characteristic(ROC)curve.The difference of area under curve(AUC)was compared by Delong test.Spearman correlation analysis was used to evaluate the correlation between pH-corrected calcium,rSIG and coagulation indexes.Results The coagulation dysfunction group had significantly lower platelet count(PLT),fibrinogen,arterial pH,pH-corrected calcium,and rSIG index,and higher injury severity score(ISS)and lactate levels(P<0.05).Multivariate Logistic regression results showed that after adjusting for confounding factors such as coagulation indicators,the higher the ISS score[odds ratio(OR)=1.156],the lower the pH-corrected calcium(OR=0.683),and the lower the rSIG index(OR=0.714),the greater the risk of coagulation dysfunction in children with multiple trauma(P<0.05).The AUC of pH-corrected calcium and rSIG index in predicting coagulation dysfunction was 0.865(95%CI 0.798-0.932)and 0.808(95%0.715-0.900).The combination of the two had the highest predictive efficacy,and the AUC reached 0.919(95%CI 0.866-0.971),which was significantly higher than any single index.The results of Delong test showed that Z=3.117,3.256(P<0.05).The sensitivity of combined prediction was 90.4%,and the specificity was 85.6%.The level of pH-corrected calcium was negatively correlated with prothrombin time(PT)and activated partial thromboplastin time(APTT)(P<0.05),and positively correlated with fibrinogen and PLT(P<0.001).The rSIG index was negatively correlated with PT and APTT(P<0.05),and positively correlated with fibrinogen and PLT(P<0.001).Conclusions The combination of pH-corrected calcium and rSIG index demonstrates good predictive value for coagulation dysfunction in pediatric patients with multiple trauma.

Abstract:Objective To identify the risk factors and develop a DynNom-based dynamic nomogram for predicting consciousness disorders in patients with acute respiratory failure(ARF).Methods This retrospective study included a total of 133 patients with acute respiratory failure hospitalized from June 2022 to February 2025 classified as either having a consciousness disorder or the no consciousness disorder group based on the occurrence of consciousness disorders.Predictive factors were initially screened using LASSO regression,with significant risk factors further identified via multivariable logistic regression.A dynamic nomogram prediction model was then constructed using R software(version 4.2.3)and validated internally.An external validation cohort of 45 ARF patients(admitted March – August 2025)was used for independent assessment.Results The incidence of consciousness disorders was 33.8%(45/133).While baseline demographics(age,gender,comorbidities)were comparable between groups,the consciousness disorder group had significantly higher rates of malnutrition,severe ARF,renal insufficiency,sepsis,and hyponatremia(all P<0.05).Logistic regression analysis confirmed that malnutrition,severe acute respiratory failure,renal insufficiency,sepsis,and hyponatremia were independent risk factors for consciousness disorders in patients with acute respiratory failure(P<0.05).The area under the ROC curve of the nomogram model for predicting consciousness disorders in patients with acute respiratory failure was 0.795(95%CI 0.710-0.880);the predicted values of the calibration curve were basically consistent with the actual values;the decision curve showed that when the threshold probability was 13%-83%,the nomogram had a good benefit value for predicting consciousness disorders in patients with acute respiratory failure.The area under the ROC curve of the validation set was 0.741(95%CI 0.591-0.891),suggesting that the constructed nomogram model had good external predictive efficacy.Conclusions Malnutrition,severe acute respiratory failure,renal insufficiency,sepsis,and hyponatremia are independent risk factors for consciousness disorders in patients with acute respiratory failure.The developed DynNom-based dynamic nomogram provides acceptable predictive accuracy and may serve as a practical tool for individualized risk assessment in clinical settings.

Abstract:Objective To evaluate the predictive value of a combination of prothrombin time(PT),international normalized ratio(INR),and shock index(SI)for deep venous thrombosis(DVT)in patients with polytrauma.Methods In this retrospective case-control study,we analyzed the clinical data of 342 patients with polytrauma admitted between June 2022 and December 2024 to the General Hospital of Ningxia Medical University.Based on the presence of DVT,the patients were divided into the DVT group(61 cases)and the non-DVT group(281 cases).A comparison of the following indicators was made between the two groups:age,gender,BMI,underlying diseases,and injury sites;heart rate,respiratory rate,systolic blood pressure,diastolic blood pressure,SI,white blood cell count,lactic acid level,blood glucose level,PT,APTT,prothrombin activity,INR,platelet count,FIB level,and D-dimer within the first 24 hours after admission;ISS,GCS,and Caprini score within the first 24 hours after admission;conditions of blood transfusion,hemostasis,and deep vein catheterization within 24 hours after admission;and LOS in ICU.Univariate analysis and multivariate Logistic regression were employed to assess and pinpoint the factors influencing DVT following polytrauma.Additionally,ROC curves were generated to evaluate the predictive performance of these identified factors.Results The DVT group had significantly higher values in age,BMI,heart rate,respiratory rate,SI,lactic acid,blood glucose,PT,INR,D-dimer,ISS,Caprini score,the proportion of patients receiving tranexamic acid treatment within 24 hours post-injury,the proportion of patients undergoing deep vein catheterization,and LOS in ICU.In contrast,the prothrombin activity and GCS score were significantly lower in the DVT group than in the non-DVT group,with all differences reaching statistical significance(P＜0.05).The results of multivariate Logistic regression analysis showed that age(OR=1.037,95％CI 1.004-1.072,P=0.027),SI(OR=5.976,95％CI 1.514-23.584,P=0.011),INR(OR=1.104,95％CI 1.014-1.202,P=0.023),and GCS(OR=0.882,95％CI 0.799-0.974,P=0.013)were significantly correlated with the formation of DVT in patients with multiple injuries(P＜0.05).Age,SI,PT,INR,and GCS alone showed poor predictive performance of DVT formation in patients with multiple injuries(AUC＜0.70),while the combined prediction using PT,INR,and SI demonstrated good predictive value(AUC=0.838);its performance was close to that of the combined prediction using all the above indicators(AUC=0.847),with both sensitivity and specificity improved.Conclusions The combination of PT,INR,and SI upon admission shows good predictive value for DVT in polytrauma patients and may aid in early risk stratification.

Abstract:Acute spontaneous spinal epidural hematoma(ASSEH)is a rare spontaneous hemorrhagic emergency.Its insidious onset and atypical symptoms often lead to clinical misdiagnosis or missed diagnosis.Delayed diagnosis and treatment can result in irreversible neurological damage or even life-threatening consequences.We report a case of a young female patient who was admitted with"chest pain for 2 hours".She presented with unexplained back pain that progressively radiated to the precordial area,manifesting as intensifying sharp pain.The initial suspected diagnosis was acute myocardial infarction(AMI).During treatment,the patient developed numbness and weakness in both lower limbs,along with urinary and fecal incontinence.Based on electrocardiogram,CT/MRI,laboratory tests,and clinical manifestations,a definitive diagnosis of ASSEH was established.The patient improved after emergency surgery and was discharged.This case report discusses the clinical features,diagnostic process,and management of ASSEH,aiming to improve recognition of this condition among clinicians.

Abstract:Objective To explore the relationship between the triglyceride-to-high-density lipoprotein(TG/HDL)ratio and the 28-day mortality risk in non-diabetic sepsis patients,and to provide new indicators and evidence for the early clinical identification of patients at a high mortality risk.Methods A retrospective cohort study was conducted,enrolling a total of 578 non-diabetic sepsis cases admitted to the Department of Critical Care Medicine of the Second Hospital of Lanzhou University between January 2015 and November 2024,which were stratified into four groups based on the quartiles of TG/HDL levels(Q1,Q2,Q3,and Q4).Data were analyzed using appropriate statistical methods,including the t-test,non-parametric tests,x2 test,Logistic regression analysis,subgroup analysis,and Kaplan-Meier survival curve analysis.Results In the TG/HDL quartile grouping analysis,there were significant differences among the four groups in terms of age,BMI,heart rate,lymphocyte count,platelet count,multiple biochemical indicators(uric acid,urea nitrogen,creatinine),disease severity scores(APACHE Ⅱ,SOFA),select comorbidities(myocardial infarction,liver disease,kidney disease),treatment modality(CRRT),and 28-day mortality.Logistic regression analysis showed that compared with the Q1 group,in different adjusted models,the 28-day death risks of the Q2 and Q3 groups did not increase significantly;while the death risk of the Q4 group increased significantly(Model 1:OR=3.434,95％CI 2.115-5.575,P＜0.001;Model2:OR=4.655,95％CI 2.772-7.819,P＜0.001;Model3:OR=2.690,95％CI 1.360-5.320,P=0.004).The results of subgroup analysis showed that there was heterogeneity in the association between TG/HDL and 28-day mortality among subgroups of age,gender,hypertension status,and albumin level.The Kaplan-Meier survival curve further indicated that the cumulative survival rate of the Q4 group was significantly lower(P＜0.001).Conclusions The TG/HDL ratio is significantly associated with 28-day mortality in non-diabetic patients with sepsis.Elevated TG/HDL levels may represent a potential risk factor for mortality and could serve as a prognostic indicator in this patient population.However,further large-scale studies are required to validate these findings.

Abstract:Objective To analyze the influence of early emergency placement of transarticular external fixation on knee joint function and bone metabolism indicators in patients with Schatzker type Ⅵtibial plateau fractures.Methods A total of 164 patients with Schatzker type Ⅵ tibial plateau fractures admitted to Harbin Fifth Hospital from January 2020 to January 2024 were all treated with transarticular external fixation in the first stage.They were divided into an early emergency(within 12 h)placement group(n=82)and a delayed(12-72 h)placement group(n=82)according to the timing of first-stage fixation at a 1∶1 ratio.The surgical indicators,knee joint function at immediate postoperative,3 months and 12 months postoperative,pain stress at preoperative and 7 days postoperative,bone metabolism indicators at preoperative and 3 months postoperative,and complications during the 12 months postoperative period were compared between the two groups.Results The first-stage operation time,second-stage operation time,interval between the first and second stages,postoperative rehabilitation time,and fracture healing time in the early placement group were shorter than those in the delayed placement group(P＜0.05).The knee joint function scores in both groups showed an increasing trend at immediate postoperative,3 months and 12 months postoperative.At 3 months postoperative,the knee joint function score in the early placement group was higher than that in the delayed placement group(P＜0.05).Compared with preoperative,the levels of serum neuropeptide Y(NPY),substance P(SP),prostaglandin E2(PGE2),and bradykinin(BK)at 7 days postoperative were increased in both groups,but the levels in the early placement group were lower than those in the delayed placement group(P＜0.05).Compared with preoperative,the levels of serum alkaline phosphatase(ALP),bone gla protein(BGP),total type Ⅰ procollagen N-terminal propeptide(T-PINP),and β-Crosslaps at 3 months postoperative were increased in both groups,and the levels in the early placement group were higher than those in the delayed placement group(P＜0.05).12 months postoperative,the complication rate in the early placement group was lower than that in the delayed placement group[6.10％(5/82)vs 17.07％(14/82),P＜0.05].Conclusions Early placement of external fixation across the joint in the emergency department can improve the surgical-related indicators of patients with Schatzker type Ⅵ tibial plateau fractures,inhibit pain stress,improve knee joint function in the short term after surgery,regulate bone metabolism indicators,and reduce the incidence of complications.

Abstract:Objective To elucidate the key molecules and underlying mechanisms of hippocampal synaptic injury during the acute stage(6 hours after onset)of sepsis-associated encephalopathy(SAE)and the early neuronal injury pathway in sepsis-induced cognitive impairment(SICI).Methods Proteomic screening was performed in mouse hippocampal tissues 6 hours post-lipopolysaccharide(LPS)administration to determine candidate targets.An HT22 neuronal cell line with overexpression of interferon-induced protein with tetratricopeptide repeats 3(IFIT3)was established in vitro.Cells were divided into 6 groups:control,LPS,IFIT3 overexpression,LPS+IFIT3,Visomitin pretreatment,and CY-09 cotreatment.The levels of mitochondrial reactive oxygen species(mtROS),IL-1β release,and the expression of postsynaptic density protein 95(PSD95)and synaptophysin(SYN)were detected.Results IFIT3 expression was significantly up-regulated in hippocampus 6 hours after LPS exposure.Under inflammatory conditions,IFIT3 overexpression exacerbated mtROS accumulation,increased IL-1β release,and reduced PSD95 and SYN.Upstream clearance of mtROS by Visomitin markedly inhibited synaptic protein loss,showing a superior protective effect compared with the NOD-like receptor family pyrin domain containing 3(NLRP3)inflammasome inhibitor CY-09.Conclusions IFIT3 aggravates acute hippocampal synaptic injury during sepsis,a process closely linked to the activation of the mtROS-NLRP3 inflammasome cascade.These results indicate that early upstream targeting of mtROS may be more effective than isolated inhibition of NLRP3,providing experimental evidence for precise intervention against acute cognitive impairment in sepsis.

Abstract:Enterobacteriaceae bloodstream infection(E-BSI)is a common and severe systemic infectious disease.It has become a major global public health challenge because of its high incidence,multidrug resistance(including extended-spectrum beta-lactamases and carbapenemase-producing strains),and high mortality rate in immunosuppressed hosts.In recent years,immune checkpoint molecules(ICMs)have attracted widespread attention in infectious immunology as key regulators of the immune response.Existing studies show that abnormal expression of ICMs in E-BSI is closely linked to host immune exhaustion,uncontrolled infection,and poor prognosis.Although our understanding of their functions continues to deepen,many unknowns and controversies remain regarding specific molecular mechanisms,clinical manifestations,and therapeutic applications.This article systematically reviews the dynamic changes and regulatory mechanisms of ICMs in E-BSI,discusses their potential as therapeutic targets,summarizes key progress and challenges in fundamental research and clinical translation,and proposes future research directions to provide reference for the clinical treatment of E-BSI.

Abstract:While effective ventilation is crucial for cardiopulmonary resuscitation(CPR),the optimal airway management strategy during CPR remains a subject of debate.This review aims to systematically evaluate the advantages and limitations of various airway management techniques across different phases of CPR,along with key issues requiring urgent attention and resolution,framed within the sequential process of resuscitation.Based on current evidence,we propose phase-specific recommendations for optimal practice.The content encompasses airway management protocols for both basic life support(BLS)and advanced life support(ALS)phases during cardiac arrest,while also outlining priorities for future research.

Abstract:Objective To observe the effect of nicotinamide adenine dinucleotide(NAD+)on cerebral injury after cardiopulmonary resuscitation(CPR)in pigs and to preliminarily explore its potential mechanism.Methods Twenty-two domestic male white pigs were randomly divided into three groups:a Sham group(n=6),a CPR group(n=8),and an NAD+group(n=8).Animals in the Sham group only underwent tracheal intubation and vascular catheterization.Animals in the CPR and NAD+groups were subjected to ventricular fibrillation induced by electrical stimulation of the right ventricle for 10 minutes,followed by 6 minutes of CPR to establish the model.At 5 minutes after resuscitation,animals in the NAD+group received an intravenous infusion of NAD+(20 mg/kg dissolved in 20 ml of normal saline)via the femoral vein using a microinfusion pump over 30 minutes,while animals in the Sham and CPR groups received an equivalent volume of normal saline.Venous blood samples were collected at 1,2,4,and 24 hours after resuscitation.Serum levels of neuron-specific enolase(NSE)and S100β protein were measured using ELISA.Neurological deficit scores(NDS)were assessed at 24 hours post-resuscitation,after which the animals were euthanized,and cortical brain tissues were collected.Western blot was used to detect the expression of cleaved caspase-3(C-CAS3),phosphorylated cuproptosis-related protein ferredoxin 1(FDX1),dihydrolipoamide acetyltransferase(DLAT),and lipoic acid synthetase(LIAS).Intracellular copper ion levels were measured using a copper-specific assay kit and spectrophotometry.One-way analysis of variance(ANOVA)with Bonferroni post-hoc testing was used for comparisons inter-group comparisons.Results Baseline parameters did not differ significantly among the three groups.All animals in the Sham group survived,while two animals died in each of the CPR and NAD+groups.At 24 hours post-resuscitation,compared to the Sham group,both the CPR and NAD+groups showed significantly higher NDS and increased serum levels of NSE and S100β(all P＜0.05).Compared to the CPR group,the NAD+group exhibited lower NDS(283±55 vs.93±50,P＜0.05)and significantly reduced serum NSE and S100β concentrations at 2,4,and 24 hours(all P＜0.05).Compared to the Sham group,both the CPR and NAD+groups showed decreased expression of FDX1,DLAT,and LIAS,along with increased intracellular copper ion levels and apoptosis indicators in the cerebral cortex at 24 hours post-resuscitation.Compared to the CPR group,the NAD+group demonstrated increased expression of FDX1,DLAT,and LIAS,as well as reduced intracellular copper ion levels and apoptosis indicators in the brain tissue.Conclusions NAD+has a protective effect against cerebral injury after CPR in pigs,and its mechanism is associated with mitochondrial cuproptosis in brain cells following cerebral ischemia-reperfusion injury.

Abstract:Objective This study aimed to identify risk factors for mortality in Emergency Department(ED)patients with dysglycemia and develop a predictive model to support clinical decision-making.Methods A single-center retrospective cohort study was conducted,enrolling 5 869 ED patients with dysglycemia from Bao'an District People's Hospital in Shenzhen between 2013 and 2023.Using visit duration(season/time slot),resuscitation room interventions,and clinical characteristics as independent variables(X),and clinical outcomes as dependent variables(Y).Multivariate Logistic regression was used to identify mortality risk factors,and model performance was evaluated using receiver operating characteristic(ROC)curves.Results Among enrolled patients,hyperglycemia accounted for 5 702 cases(97.15％)and hypoglycemia for 167 cases(2.85％),with 76 in-hospital deaths(mortality rate 1.29％).The median resuscitation room length of stay was 5.11 hours(IQR 3.00-8.00),showing significant variation across diseases(Kruskal-Wallis H=56.23,P＜0.001).The prediction model was validated using 10-fold cross-validation,yielding a mean AUC of 0.790[95％confidence interval(CI)0.616-0.958],with a sensitivity of 61.8％and a specificity of 76.0％.Multivariate Logistic regression analysis indicated that age progression(OR=0.97),admission to the emergency resuscitation room(OR=0.14),use of vasoactive drugs(OR=0.11),and sodium bicarbonate correction of acidosis(OR=0.28)were significantly associated with an increased risk of mortality.The predictive weights of variables on clinical outcomes,calculated via the model_10cv_LR(X,Y)model,were as follows:cardiopulmonary resuscitation therapy(-0.724),mechanical ventilation therapy(-0.921),administration of vasoactive drugs(-1.960),correction of acidosis therapy(-1.216),intravenous fluid therapy(-0.103),whether admitted to the resuscitation room(-1.434),time to medical attention within 24 hours(-0.033),age(per one-year increase)(-0.032);insulin therapy(0.443).Conclusions The predictive model integrates patient characteristics,seasonal patterns,temporal factors,treatment measures,and systemic factors,demonstrating strong discriminatory power.It provides a quantitative tool for early identification of high-risk patients and can be applied to construct mortality risk prediction models for precision treatment.

Abstract:Sepsis,a systemic inflammatory response syndrome triggered by infection,often leads to multiple organ dysfunction.Acute lung injury(ALI)is a common and severe complication with high morbidity and mortality.Lactylation modification,an emerging post-translational modification,covalently attaches lactate groups to lysine residues of proteins via enzymatic reactions,modulating diverse cellular processes including gene expression and inflammation.Research indicates that lactylation modification can impact sepsis-induced ALI progression by regulating macrophage polarization,ferroptosis in lung tissue cells,and its interplay with acetylation modifications.These findings suggest that lactylation-related molecules may serve as potential therapeutic targets or diagnostic biomarkers for sepsis-associated ALI.This review synthesizes current research on the role of lactylation modification in the initiation and progression of sepsis-induced ALI,providing a comprehensive overview on its mechanistic involvement and to propose novel directions for optimizing treatment strategies and drug development.

Abstract:Objective This study aimed to develop a 28-day mortality risk prediction model for diabetic sepsis patients using machine learning,with the goal of optimizing treatment and improving clinical outcomes.Methods Using the MIMIC-Ⅳ 2.2 database,a screening strategy based on the Sepsis-3.0 criteria was implemented.Diabetic sepsis patients were selected using ICD-9 and ICD-10 diagnosis codes,and clinical data were extracted.Patients were grouped by survival status at 28 days.The Boruta algorithm(bi-parameter),Logistic regression,and Lasso regression were used to select features.Eight machine learning models-Logistic regression,gradient boosting machine(GBM),LightGBM,AdaBoost,CatBoost,k-nearest neighbors,neural networks,and support vector machines-were used to predict the 28-day mortality risk.Hyperparameter optimization and cross-validation were performed to assess the risk of model overfitting.The best model was selected based on performance metrics and further explained using SHAP analysis.Results A total of 9 235 diabetic sepsis patients were included in the study.Ten important feature variables were identified[APACHE Ⅱ score,age,heart rate,respiratory rate,SOFA score,blood lactate,Charlson comorbidity index,oxygen saturation,activated partial thromboplastin time(APTT),and pH value].After hyperparameter optimization and model performance evaluation,the GBM model demonstrated the best stability and predictive ability.The area under the curve(AUC)of the GBM model in the training and test sets were 0.825 and 0.771 respectively.Decision curve and calibration curve analysis showed that the GBM model provided significant net clinical benefit and stability.SHAP analysis revealed that the APACHE Ⅱ score,age,and heart rate had the greatest impact on the GBM model's predictive performance.The model is user-friendly and can quickly predict the 28-day mortality risk of diabetic sepsis patients.Conclusions The 28-day mortality risk prediction model for diabetic sepsis patients,constructed and validated using the GBM algorithm,shows good clinical applicability and interpretability of pathological mechanisms.It may enable precise stratification for early intervention in patients,optimizing clinical treatment strategies.

Abstract:Liver dysfunction is a common complication in patients with intracerebral hemorrhage(ICH)and can significantly affect prognosis.This article systematically reviews current research on ICH complicated by liver injury,summarizes the pathophysiological mechanisms through which ICH leads to liver dysfunction,and focuses on the potential mechanisms including neuroendocrine stress response,systemic inflammatory response,hepatic hypoperfusion,impaired drug metabolism,gut-liver axis dysfunction,brain-liver axis interactions,and genetic susceptibility.The aim is to provide a reference for comprehensive clinical management and future research.