Finite Element Simulations on Failure Behaviors of Granular Materials with Microstructures Using a Micromechanics-Based Cosserat Elastoplastic Model

This paper presents a micromechanics-based Cosserat continuum model for microstructured granular materials.By utilizing this model,the macroscopic constitutive parameters of granular materials with different microstructures are expressed as sums of microstructural information.The microstructures under consideration can be classified into three categories:a medium-dense microstructure,a dense microstructure consisting of one-sized particles,and a dense microstructure consisting of two-sized particles.Subsequently,the Cosserat elastoplastic model,along with its finite element formulation,is derived using the extended Drucker-Prager yield criteria.To investigate failure behaviors,numerical simulations of granular materials with different microstructures are conducted using the ABAQUS User Element(UEL)interface.It demonstrates the capacity of the proposed model to simulate the phenomena of strain-softening and strain localization.The study investigates the influence of microscopic parameters,including contact stiffness parameters and characteristic length,on the failure behaviors of granularmaterials withmicrostructures.Additionally,the study examines themesh independence of the presented model and establishes its relationship with the characteristic length.A comparison is made between finite element simulations and discrete element simulations for a medium-dense microstructure,revealing a good agreement in results during the elastic stage.Somemacroscopic parameters describing plasticity are shown to be partially related to microscopic factors such as confining pressure and size of the representative volume element.

Development and validation of a new prognostic model for patients with acute-on-chronic liver failure in intensive care unit

BACKGROUND Cirrhotic patients with acute-on-chronic liver failure(ACLF)in the intensive care unit(ICU)have a poor but variable prognoses.Accurate prognosis evaluation can guide the rational management of patients with ACLF.However,existing prognostic scores for ACLF in the ICU environment lack sufficient accuracy.AIM To develop a new prognostic model for patients with ACLF in ICU.METHODS Data from 938 ACLF patients in the Medical Information Mart for Intensive Care(MIMIC)database were used to develop a new prognostic model(MIMIC ACLF)for ACLF.Discrimination,calibration and clinical utility of MIMIC ACLF were assessed by area under receiver operating characteristic curve(AUROC),calibration curve and decision curve analysis(DCA),respectively.MIMIC ACLF was then externally validated in a multiple-center cohort,the Electronic Intensive Care Collaborative Research Database and a single-center cohort from the Second Hospital of Hebei Medical University in China.RESULTS The MIMIC ACLF score was determined using nine variables:ln(age)×2.2+ln(white blood cell count)×0.22-ln(mean arterial pressure)×2.7+respiratory failure×0.6+renal failure×0.51+cerebral failure×0.31+ln(total bilirubin)×0.44+ln(internationalized normal ratio)×0.59+ln(serum potassium)×0.59.In MIMIC cohort,the AUROC(0.81/0.79)for MIMIC ACLF for 28/90-day ACLF mortality were significantly greater than those of Chronic Liver Failure Consortium ACLF(0.76/0.74),Model for End-stage Liver Disease(MELD;0.73/0.71)and MELD-Na(0.72/0.70)(all P<0.001).The consistency between actual and predicted 28/90-day survival rates of patients according to MIMIC ACLF score was excellent and superior to that of existing scores.The net benefit of MIMIC ACLF was greater than that achieved using existing scores within the 50%threshold probability.The superior predictive accuracy and clinical utility of MIMIC ACLF were validated in the external cohorts.CONCLUSION We developed and validated a new prognostic model with satisfactory accuracy for cirrhotic patients with ACLF hospitalized in the ICU.The model-based risk stratification and online calculator might facilitate the rational management of patients with ACLF.

Predicting depression in patients with heart failure based on a stacking model

BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.

Numerical three-dimensional modeling of earthen dam piping failure

A physically-based numerical three-dimensional earthen dam piping failure model is developed for homogeneous and zoned soil dams.This model is an erosion model,coupled with force/moment equilibrium analyses.Orifice flow and two-dimensional(2D)shallow water equations(SWE)are solved to simulate dam break flows at different breaching stages.Erosion rates of different soils with different construction compaction efforts are calculated using corresponding erosion formulae.The dam's real shape,soil properties,and surrounding area are programmed.Large outer 2D-SWE grids are used to control upstream and downstream hydraulic conditions and control the boundary conditions of orifice flow,and inner 2D-SWE flow is used to scour soil and perform force/moment equilibrium analyses.This model is validated using the European Commission IMPACT(Investigation of Extreme Flood Processes and Uncertainty)Test#5 in Norway,Teton Dam failure in Idaho,USA,and Quail Creek Dike failure in Utah,USA.All calculated peak outflows are within 10%errors of observed values.Simulation results show that,for a V-shaped dam like Teton Dam,a piping breach location at the abutment tends to result in a smaller peak breach outflow than the piping breach location at the dam's center;and if Teton Dam had broken from its center for internal erosion,a peak outflow of 117851 m'/s,which is 81%larger than the peak outflow of 65120 m3/s released from its right abutment,would have been released from Teton Dam.A lower piping inlet elevation tends to cause a faster/earlier piping breach than a higher piping inlet elevation.

Development and validation of a predictive model for acute-onchronic liver failure after transjugular intrahepatic portosystemic shunt

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a cause of acute-onchronic liver failure(ACLF).AIM To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model.METHODS In total,379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort,and 123 patients from Nanfang Hospital were included in the external validation cohort.Univariate and multivariate logistic regression analyses were performed to identify independent predictors.The prediction model was established based on the Akaike information criterion.Internal and external validation were conducted to assess the performance of the model.RESULTS Age and total bilirubin(TBil)were independent risk factors for the incidence of ACLF within 1 year after TIPS.We developed a prediction model comprising age,TBil,and serum sodium,which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort.CONCLUSION Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis.Our model showed satisfying predictive value.

Construction of A Prediction Model for Atrial Fibrillation in Patients with Dilated Cardiomyopathy and Heart Failure

Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.

Three-dimensional limit variation analysis on the ultimate pullout capacity of the anchor cables based on the Hoek-Brown failure criterion

Only simplified two-dimensional model and a single failure mode are adopted to calculate the ultimate pullout capacity(UPC)of anchor cables in most previous research.This study focuses on a more comprehensive combination failure mode that consists of bond failure of an anchorage body and failure of an anchored rock mass.The three-dimensional ultimate pullout capacity of the anchor cables is calculated based on the Hoek-Brown failure criterion and variation analysis method.The numerical solution for the curvilinear function in fracture plane is obtained based on the finite difference theory,which more accurately reflects the failure state of the anchor cable,as opposed to that being assumed in advance.The results reveal that relying solely on a single failure mode for UPC calculations has limitations,as changes in parameter values not only directly impact the UPC value but also can alter the failure model and thus the calculation method.

Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

Numerical investigation of hydro-morphodynamic characteristics of a cascading failure of landslide dams

A cascading failure of landslide dams caused by strong earthquakes or torrential rains in mountainous river valleys can pose great threats to people’s lives,properties,and infrastructures.In this study,based on the three-dimensional Reynoldsaveraged Navier-Stokes equations(RANS),the renormalization group(RNG)k-εturbulence model,suspended and bed load transport equations,and the instability discriminant formula of dam breach side slope,and the explicit finite volume method(FVM),a detailed numerical simulation model for calculating the hydro-morphodynamic characteristics of cascading dam breach process has been developed.The developed numerical model can simulate the breach hydrograph and the dam breach morphology evolution during the cascading failure process of landslide dams.A model test of the breaches of two cascading landslide dams has been used as the validation case.The comparison of the calculated and measured results indicates that the breach hydrograph and the breach morphology evolution process of the upstream and downstream dams are generally consistent with each other,and the relative errors of the key breaching parameters,i.e.,the peak breach flow and the time to peak of each dam,are less than±5%.Further,the comparison of the breach hydrographs of the upstream and downstream dams shows that there is an amplification effect of the breach flood on the cascading landslide dam failures.Three key parameters,i.e.,the distance between the upstream and the downstream dams,the river channel slope,and the downstream dam height,have been used to study the flood amplification effect.The parameter sensitivity analyses show that the peak breach flow at the downstream dam decreases with increasing distance between the upstream and the downstream dams,and the downstream dam height.Further,the peak breach flow at the downstream dam first increases and then decreases with steepening of the river channel slope.When the flood caused by the upstream dam failure flows to the downstream dam,it can produce a surge wave that overtops and erodes the dam crest,resulting in a lowering of the dam crest elevation.This has an impact on the failure occurrence time and the peak breach flow of the downstream dam.The influence of the surge wave on the downstream dam failure process is related to the volume of water that overtops the dam crest and the erosion characteristics of dam material.Moreover,the cascading failure case of the Xiaogangjian and Lower Xiaogangjian landslide dams has also been used as the representative case for validating the model.In comparisons of the calculated and measured breach hydrographs and final breach morphologies,the relative errors of the key dam breaching parameters are all within±10%,which verify the rationality of the model is applicable to real-world cases.Overall,the numerical model developed in this study can provide important technical support for the risk assessment and emergency treatment of failures of cascading landslide dams.

Acute liver failure:A systematic review and network meta-analysis of optimal type of stem cells in animal models

BACKGROUND The therapeutic effects of various stem cells in acute liver failure(ALF)have been demonstrated in preclinical studies.However,the specific type of stem cells with the highest therapeutic potential has not been determined.AIM To validate the efficacy of stem cells in ALF model and to identify the most promising stem cells.METHODS A search was conducted on the PubMed,Web of Science,Embase,Scopus,and Cochrane databases from inception to May 3,2022,and updated on November 16,2022 to identify relevant studies.Two independent reviewers performed the literature search,identification,screening,quality assessment,and data extraction.RESULTS A total of 89 animal studies were included in the analysis.The results of traditional meta-analysis showed that stem cell therapy could significantly reduce the serum levels of alanine aminotransferase[weighted mean difference(WMD)=-181.05(-191.71,-170.39)],aspartate aminotransferase[WMD=-309.04(-328.45,-289.63)],tumor necrosis factor-alpha[WMD=-8.75(-9.93,-7.56)],and interleukin-6[WMD=-10.43(-12.11,-8.76)]in animal models of ALF.Further subgroup analysis and network meta-analysis showed that although mesenchymal stem cells are the current research hotspot,the effect of liver stem cells(LSCs)on improving liver function is significantly better than that of the other five types of stem cells.In addition,the ranking results showed that the possibility of LSCs improving liver function ranked first.This fully proves the great therapeutic potential of LSCs,which needs to be paid more attention in the future.CONCLUSION LSCs may have a higher therapeutic potential.Further high-quality animal experiments are needed to explore the most effective stem cells for ALF.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Reliability modeling of mutual DCFP considering failure physical dependency

Degradation and overstress failures occur in many electronic systems in which the operation load and environmental conditions are complex.The dependency of them called dependent competing failure process(DCFP),has been widely studied.Electronic system may experience mutual effects of degradation and shocks,they are considered to be interdependent.Both the degradation and the shock processes will decrease the limit of system and cause cumulative effect.Finally,the competition of hard and soft failure will cause the system failure.Based on the failure mechanism accumulation theory,this paper constructs the shock-degradation acceleration and the threshold descent model,and a system reliability model established by using these two models.The mutually DCFP effect of electronic system interaction has been decomposed into physical correlation of failure,including acceleration,accumulation and competition.As a case,a reliability of electronic system in aeronautical system has been analyzed with the proposed method.The method proposed is based on failure physical evaluation,and could provide important reference for quantitative evaluation and design improvement of the newly designed system in case of data deficiency.

Parameters Calibration of the GISSMO Failure Model for SUS301L-MT

With the development of the rail transit industry,more attention has been paid to the passive safety of rail vehicles.Structural damage is one of the main failure behaviors in a rail vehicle collision,but it has been paid little attention to in past research.In this paper,the quasi-static fracture experiments of SUS301L-MT under different stress states were carried out.The mechanical fracture properties of this material were studied,and the corresponding finite element simulation accuracy was improved to guide the design of vehicle crashworthiness.Through the tests,the fracture behavior of materials with wide stress triaxiality was obtained,and each specimen’s fracture locations and fracture strains were determined.Parameters of a generalized incremental stress state dependent damage model(GISSMO)of the material were calibrated,and the model’s accuracy was verified with test results from a 45°shear specimen.The GISSMO failure model accurately reflected the fracture characteristics of the material.The mesh dependency of this model was modified and discussed.The results show that the simulation agrees well with experimental data for the force-displacement curve after correction,but the strain distribution needs to be further studied and improved.

Refined mathematical model for the breaching of concrete-face sand-gravel dams due to overtopping failure

Overtopping is one of the main reasons for the breaching of concrete-face sand-gravel dams(CFSGDs).In this study,a refined mathematical model was established based on the characteristics of the overtopping breaching of CFSGDs.The model characteristics were as follows:(1)Based on the Renormailzation Group(RNG)k-εturbulence theory and volume of fluid(VOF)method,the turbulent characteristics of the dam-break flow were simulated,and the erosion surface of the water and soil was tracked;(2)In consideration of the influence of the change in the sediment content on the dam-break flow,the dam material transport equation,which could reflect the characteristics of particle settlement and entrainment motion,was used to simulate the erosion process of the sand gravels;(3)Based on the bending moment balance method,a failure equation of the concrete face slab under dead weight and water load was established.The proposed model was verified through a case study on the failure of the Gouhou CFSGD.The results showed that the proposed model could well simulate the erosion mode of the special vortex flow of the CFSGD scouring the support body of the concrete face slab inward and reflect the mutual coupling relationship between the dam-break flow,sand gravels,and concrete face slabs.Compared with the measured values,the relative errors of the peak discharge,final breach average width,dam breaching duration,and maximum failure length of the face slab calculated using the proposed model were all less than 12%,thus verifying the rationality of the model.The proposed model was demonstrated to perform better and provide more detailed results than three selected parametric models and three simplified mathematical models.The study results can aid in establishing the risk level and devising early warning strategies for CFSGDs.

Modeling the Failure Rate of a Standby Multi-Component System and Improving Reliability

Evaluating the reliability of a system requires knowledge of the failure modes to which it is subjected. Complex topology systems generally require a high level of availability, which is a function of the arrangement of elements (components) in the system. To avoid serious failures for such complex systems, recourse can be had to the redundancy techniques available in the literature. These techniques help to improve system reliability, without affecting the reliability of system components. This paper is interested in the proposal of a model for evaluating the failure rate of a standby multi-components system and in improving the reliability of mechanical systems, arranged in a topology (series, parallel, or mixed).

Effects of health concept model-based detailed behavioral care on mood and quality of life in elderly patients with chronic heart failure

BACKGROUND With the intensification of social aging,the susceptibility of the elderly population to diseases has attracted increasing attention,especially chronic heart failure(CHF)that accounts for a large proportion of the elderly.AIM To evaluate the application value of health concept model-based detailed behavioral care in elderly patients with CHF.METHODS This study recruited 116 elderly CHF patients admitted from October 2018 to October 2020 and grouped them according to the nursing care that they received.The elderly patients who underwent health concept model-based detailed behavioral care were included in a study group(SG;n=62),and those who underwent routine detailed behavioral nursing intervention were included as a control group(CG;n=54).Patients’negative emotions(NEs),quality of life(QoL),and nutritional status were assessed using the self-rating anxiety/depression scale(SAS/SDS),the Minnesota Living with Heart Failure Questionnaire(MLHFQ),and the Modified Quantitative Subjective Global Assessment(MQSGA)of nutrition,respectively.Differences in rehabilitation efficiency,NEs,cardiac function(CF)indexes,nutritional status,QoL,and nursing satisfaction were comparatively analyzed.RESULTS A higher response rate was recorded in the SG vs the CG after intervention(P<0.05).After care,the left ventricular ejection fraction was higher while the left ventricular end-diastolic dimension and left ventricular end systolic diameter were lower in the SG compared with the CG(P<0.05).The post-intervention SAS and SDS scores,as well as MQSGA and MLHFQ scores,were also lower in the SG(P<0.05).The SG was also superior to the CG in the overall nursing satisfaction rate(P<0.05).CONCLUSION Health concept model-based detailed behavioral care has high application value in the nursing care of elderly CHF patients,and it can not only effectively enhance rehabilitation efficiency,but also mitigate patients’NEs and improve their CF and QoL.

Compression failure conditions of concrete-granite combined body with different roughness interface

Bedrock and concrete lining are typical composite structures in the engineering field and the stability of the geological body and engineering body is directly connected to the mechanical properties of the composite body.Under this background,the study provides the transverse isotropic equivalent model of concrete-granite double-layer composite based on the notion of strain energy equivalence.Assuming that the strength failure of concrete and granite meets the Mohr-Coulomb criterion,then the strength failure model of the combined body considering the joint roughness coefficient(JRC)is derived,and the influences of JRC,the height ratio of concrete to granite,and confining pressure on the strength failure characteristics of the combined body are emphatically analyzed.Finally,the model applicability is illustrated by the uniaxial and triaxial compression tests on concrete monomer,granite monomer and concretegranite composite samples(CGCSs)with different JRCs.The results revealed that the compressive strength of the composite is closer to the concrete with lower strength in the combined body under different confining pressures.Adding interface roughness causes to raise the compressive strength of the composite due to interfacial adhesion between concrete and granite,and a slowing growth trend is observed in compressive strength as roughness.The model can provide a certain reference for the stability design and evaluation of engineering rock mass.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

Fecal cytolysin does not predict disease severity in acutely decompensated cirrhosis and acute-on-chronic liver failure

Background:Cirrhosis with acute decompensation(AD)and acute-on-chronic liver failure(ACLF)are characterized by high morbidity and mortality.Cytolysin,a toxin from Enterococcus faecalis(E.faecalis),is associated with mortality in alcohol-associated hepatitis(AH).It is unclear whether cytolysin also contributes to disease severity in AD and ACLF.Methods:We studied the role of fecal cytolysin in 78 cirrhotic patients with AD/ACLF.Bacterial DNA from fecal samples was extracted and real-time quantitative polymerase chain reaction(PCR)was performed.The association between fecal cytolysin and liver disease severity in cirrhosis with AD or ACLF was analyzed.Results:Fecal cytolysin and E.faecalis abundance did not predict chronic liver failure(CLIF-C)AD and ACLF scores.Presence of fecal cytolysin was not associated with other liver disease markers,including Fibrosis-4(FIB-4)index,‘Age,serum Bilirubin,INR,and serum Creatinine(ABIC)’score,Child-Pugh score,model for end-stage liver disease(MELD)nor MELD-Na scores in AD or ACLF patients.Conclusions:Fecal cytolysin does not predict disease severity in AD and ACLF patients.The predictive value of fecal cytolysin positivity for mortality appears to be restricted to AH.