Evaluation of G3BP1 in the prognosis of acute and acute-on-chronic liver failure after the treatment of artificial liver support system

BACKGROUND The increased expression of G3BP1 was positively correlated with the prognosis of liver failure.AIM To investigate the effect of G3BP1 on the prognosis of acute liver failure(ALF)and acute-on-chronic liver failure(ACLF)after the treatment of artificial liver support system(ALSS).METHODS A total of 244 patients with ALF and ACLF were enrolled in this study.The levels of G3BP1 on admission and at discharge were detected.The validation set of 514 patients was collected to verify the predicted effect of G3BP1 and the viability of prognosis.RESULTS This study was shown that lactate dehydrogenase(LDH),alpha-fetoprotein(AFP)and prothrombin time were closely related to the prognosis of patients.After the ALSS treatment,the patient’amount of decreased G3BP1 index in difference of G3BP1 between the value of discharge and admission(difG3BP1)<0 group had a nearly 10-fold increased risk of progression compared with the amount of increased G3BP1 index.The subgroup analysis showed that the difG3BP1<0 group had a higher risk of progression,regardless of model for end-stage liver disease high-risk or low-risk group.At the same time,compared with the inflam matory marks[tumor necrosis factor-α,interleukin(IL)-1βand IL-18],G3BP1 had higher discrimination and was more stable in the model analysis and validation set.When combined with AFP and LDH,concordance index was respectively 0.84 and 0.8 in training and validation cohorts.CONCLUSION This study indicated that G3BP1 could predict the prognosis of ALF or ACLF patients treated with ALSS.The combination of G3BP1,AFP and LDH could accurately evaluate the disease condition and predict the clinical endpoint of patients.

Liver transplantation and artificial liver support in fulminant hepatic failure

INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The advent of liver transplantation revolutionized the outcome of FHF[3,4].However,many patients were unwilling to accept liver transplantation until very late,hence most of them died because of donor shortage and urgency of the disease[5-7],To overcome he problems,we performed orthotopic liver transplantation(OLT)in combination with artificial liver support(ALS) in the treatment of FHF in the past 2 years with satisfactory results.Our experience was reported below.

TECA hybrid artificial liver support system in treatment of acute liver failure

AIM: To assess the efficacy and safety of TECA type hybrid artificial liver support system (TECA-HALSS) in providing liver function of detoxification, metabolism and physiology by treating the patients with acute liver failure (ALF). METHODS: The porcine liver cells (1-2) x 10(10) were separated from the Chinese small swine and cultured in the bioreactor of TECA-BALSS at 37.0 degrees C and circulated through the outer space of the hollow fiber tubes in BALSS. The six liver failure patients with various degree of hepatic coma were treated by TECA-HALSS and with conventional medicines. The venous plasma of the patients was separated by a plasma separator and treated by charcoal adsorbent or plasma exchange. The plasma circulated through the inner space of the hollow fiber tubes of BALSS and mixed with the patients' blood cells and flew back to their blood circulation. Some small molecular weight substances were exchanged between the plasma and porcine liver cells. Each treatment lasted 6.0-7.0 h. Physiological and biochemical parameters were measured before,during and after the treatment. RESULTS: The average of porcine liver cells was (1.0-3.0) x 10(10) obtained from each swine liver using our modified enzymatic digestion method. The survival rate of the cells was 85%-93% by trypan blue stain and AO/PI fluorescent stain. After cultured in TECA-BALSS bioreactor for 6 h, the survival rate of cells still remained 70%-85%. At the end of TECA-HALSS treatment, the levels of plasma NH(3), ALT, TB and DB were significantly decreased. The patients who were in the state of drowsiness or coma before the treatment improved their appetite significantly and regained consciousness, some patients resumed light physical work on a short period after the treatment.One to two days after the treatment, the ratio of PTA increased warkedly. During the treatment, the heart rates, blood pressure, respiration condition and serum electrolytes (K(+), Na(+) and Cl(-)) were stable without thrombosis and bleeding in all the six patients. CONCLUSION: TECA-HALSS treatment could be a rapid, safe and efficacious method to provide temporary liver support for patients with ALF.

Nursing Care of 10 Patients with Vasovagal Reflex Caused by Artificial Liver Support System Treatment

This study outlines the essential nursing strategies employed in the care of 10 patients experiencing vascular vagal reflex, managed with artificial liver support systems. It highlights a holistic nursing approach tailored to the distinct clinical manifestations of these patients. Key interventions included early detection of psychological issues prior to initiating treatment, the implementation of comprehensive health education, meticulous monitoring of vital signs throughout the therapy, prompt emergency interventions when needed, adherence to prescribed medication protocols, and careful post-treatment observations including venous catheter management. Following rigorous treatment and dedicated nursing care, 7 patients demonstrated significant improvement and were subsequently discharged.

Revolutionizing disease diagnosis and management:Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

Plasma exchange-centered artificial liver support system in hepatitis B virus-related acute-onchronic liver failure:a nationwide prospective multicenter study in China

BACKGROUND： Plasma exchange （PE）-centered artificial liver support system reduced the high mortality rate of hepa titis B virus （HBV）-related acute-on-chronic liver failure （ACLF）. But the data were diverse in different medical centers. The present prospective nationwide study was to evaluate the effects of PE on patients with HBV-ACLF at different stages.

Hybrid artificial liver support system for treatment of severe liver failure

AIM: To construct a novel hybrid artificial liver support system (HALSS) and to evaluate its efficacy in patients with severe liver failure.METHODS: Hepatocytes were isolated from suckling pig by the modified Seglen's method. Isolated hepatocytes were cultured in a spinner flask for 24 h to form spheroids before use and the functions of spheroids were detected. HALSS consisted of a plasma separator, a hemo-adsorba and a bioreactor with hepatocytes spheroids in its extra-fiber space.HALSS was applied to 10 patients with severe liver failure.The general condition and the biochemical indexes of the patients were studied just before and after the treatment.RESULTS: The number of cells per liver was about 2-4× 1010 (mean, 3.1 ± 1.5 × 1010). The cell viabilities were more than 95%. After 24 h of spheroid culture, most hepatocytes formed spheroids. The levels of albumin and urea in the medium of spheroid culture were higher than those in supernatant of petri dish culture (P = 0.0015 and 0.0001, respectively). The capacity of albumin production and urea synthesis remained stable for more than one wk and declined rapidly after two weeks in vitro. In HALSS group, the duration of HALSS treatment was 6-10 h each time. All patients tolerated the treatment well without any fatal adverse reaction. After HALSS treatment, the general condition, psychic state, encephalopathy and hepatic function of the patients were improved. The survival rate of the HALSS group, Plasmapheresis group and control group was 30% (3/10), 20% (2/10) and 0% (0/10), respectively (P = 0.024). Two weeks after treatment, Tbil and ALT decreased and the PTA level elevated in HALSS group and pasmapheresis group (Pvalue: 0.015 vs 0.020, 0.009vs 0.012 and 0.032 vs 0.041, respectively). But there was no significant change of blood albumin concentration before and after treatment in HAlSS group and Plasmapheresis group.CONCLUSION: The HALSS established by us is effective in supporting liver function of patients with severe liver failure.

Comparison between bioartificial and artificial liver for the treatment of acute liver failure in pigs

AIM： To characterize and evaluate the therapeutic efficacy of bioartificial liver （BAL） as compared to that of continuous hemodiafiltration （CHDF） with plasma exchange （PE）, which is the current standard therapy for fulminant hepatic failure （FHF） in Japan. METHODS： Pigs with hepatic devascularization were divided into three groups： （1） a non-treatment group （NT; n = 4）; （2） a BAL treatment group （BAL; n = 4）, （3） a PE ＋ CHDF treatment group using 1.5 L of normal porcine plasma with CHDF （PE ＋ CHDF, n -- 4）. Our BAL system consisted of a hollow fiber module with 0.2 i^m pores and 1 × 10^10 of microcarrier-attached hepatocytes inoculated into the extra-fiber space. Each treatment was initiated 4 h after hepatic devascularization. RESULTS： The pigs in the BAL and the PE ＋ CHDF groups survived longer than those in the NT group. The elimination capacity of blood ammonia by both BAL and PE ＋ CHDF was significantly higher than that in NT. Aromatic amino acids （AAA） were selectively eliminated by BAL, whereas both AAA and branched chain amino acids, which are beneficial for life, were eliminated by PE ＋ CHDF. Electrolytes maintenance and acid-base balance were better in the CPE ＋ CHDF group than that in the BAL group. CONCLUSION： Our results suggest that PE ＋ CHDF eliminate all factors regardless of benefits, whereas BAL selectively metabolizes toxic factors such as AAA. However since PE ＋ CHDF maintain electrolytes and acid-base balance, a combination therapy of BAL plus CPE ＋ CHDF might be more effective for FHF.

Preoperative risk factor analysis in orthotopic liver transplantation with pretransplant artificial liver support therapy

AIM： To assess the value of pre-transplant artificial liver support in reducing the pre-operative risk factors relating to early mortality after orthotopic liver transplantation （OLT）. METHODS： Fifty adult patients with various stages and various etiologies undergoing OLT procedures were treated with molecular adsorbent recycling system （MARS） as preoperative liver support therapy. The study included two parts, the first one is to evaluate the medical effectiveness of single MARS treatment with some clinical and laboratory parameters, which were supposed to be the therapeutical pre-transplant risk factors, the second part is to study the patients undergoing OLT using the regression analysis on preoperative risk factors relating to early mortality （30 d） after OLT. RESULTS： In the 50 patients, the statistically significant improvement in the biochemical parameters was observed （pre-treatment and post-treatment）. Eight patients avoided the scheduled Ltx due to significant relief of clinical condition or recovery of failing liver function, 8 patients died, 34 patients were successfully bridged to Ltx, the immediate outcome of this 34 patients within 30d observation was： 28 kept alive and 6 patients died. CONCLUSION： Pre-operative SOFA, level of creatinine, INR, TNF-α, IL-10 are the main preoperative risk factors that cause early death after operation, MARS treatment before transplantion can relieve these factors significantly.

Establishment of a risk assessment score for deep vein thrombosis after artificial liver support system treatment

BACKGROUND The artificial liver support system(ALSS)is an effective treatment method for liver failure,but it requires deep venous intubation and long-term indwelling catheterization.However,the coagulation mechanism disorder of basic liver failure diseases,and deep venous thrombosis(DVT)often occur.AIM To evaluate the risk factors for DVT following use of an ALSS and establish a risk assessment score.METHODS This study was divided into three stages.In the first stage,the risk factors for DVT were screened and the patient data were collected,including ALSS treatment information;biochemical indices;coagulation and hematology indices;complications;procoagulant use therapy status;and a total of 24 indicators.In the second stage,a risk assessment score for DVT after ALSS treatment was developed.In the third stage,the DVT risk assessment score was validated.RESULTS A total of 232 patients with liver failure treated with ALSS were enrolled in the first stage,including 12 with lower limb DVT.Logistic regression analysis showed that age[odds ratio(OR),1.734;P=0.01],successful catheterization time(OR,1.667;P=0.005),activity status(strict bed rest)(OR,3.049;P=0.005),and D-dimer level(≥500 ng/mL)(OR,5.532;P<0.001)were independent risk factors for DVT.We then established a scoring system for risk factors.In the validation group,a total of 213 patients with liver failure were treated with ALSS,including 14 with lower limb DVT.When the cutoff value of risk assessment was 3,the specificity and sensitivity of the risk assessment score were 88.9%and 85.7%,respectively.CONCLUSION A simple risk assessment scoring system was established for DVT patients with liver failure treated with ALSS and was verified to have good sensitivity and specificity.

Large-for-size syndrome prophylaxis in infant liver recipients with low body mass

Transplantation of the left lateral section(LLS)of the liver is now an established practice for treating advanced diffuse and unresectable focal liver diseases in children,with variants of the LLS primarily used in infants.However,the surgical challenge of matching the size of an adult donor's graft to the volume of a child's abdomen remains significant.This review explores historical developments,various approaches to measuring the required functional liver mass,and techniques to prevent complications associated with large-for-size grafts in infants.

Past,present,and future perspectives of ultrasound-guided ablation of liver tumors:Where could artificial intelligence lead interventional oncology?

The first ablation procedures for small hepatocellular carcinomas were percutaneous ethanol injection under ultrasound(US)guidance.Later,radiofrequency ablation was shown to achieve larger coagulation areas than percutaneous ethanol injection and became the most used ablation technique worldwide.In the past decade,microwave ablation systems have achieved larger ablation areas than radiofrequency ablation,suggesting that the 3-cm barrier could be broken in the treatment of liver tumors.Likewise,US techniques to guide percutaneous ablation have seen important progress.Contrast-enhanced US(CEUS)can define and target the tumor better than US and can assess the size of the ablation area after the procedure,which allows immediate retreatment of the residual tumor foci.Furthermore,fusion imaging fuses real-time US images with computed tomography or magnetic resonance imaging with significant improvements in detecting and targeting lesions with low conspicuity on CEUS.Recently,software powered by artificial intelligence has been developed to allow three-dimensional segmentation and reconstruction of the anatomical structures,aiding in procedure planning,assessing ablation completeness,and targeting the residual viable foci with greater precision than CEUS.Hopefully,this could lead to the ablation of tumors up to 5-7 cm in size.

Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis

Non-alcoholic fatty liver disease(NAFLD)and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide.It is critical to develop reliable methods of predicting progression to fibrosis,cirrhosis,and decompensated liver disease.Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data.Artificial intelligence(AI)provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis,and to identify patients at high risk for disease progression.We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis.Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis.We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction(MELD-Na score,liver biopsy,FIB-4 score,and biomarkers).They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy,for which there are currently no established prediction tools.These studies show that AI could be a helpful adjunct to existing techniques for diagnosing,monitoring,and treating both NAFLD and viral hepatitis.

Drug-induced liver injury and COVID-19:Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice

BACKGROUND Liver injury is a relevant condition in coronavirus disease 2019(COVID-19)inpatients.Pathophysiology varies from direct infection by virus,systemic inflammation or drug-induced adverse reaction(DILI).DILI detection and monitoring is clinically relevant,as it may contribute to poor prognosis,prolonged hospitalization and increase indirect healthcare costs.Artificial Intelligence(AI)applied in data mining of electronic medical records combining abnormal liver tests,keyword searching tools,and risk factors analysis is a relevant opportunity for early DILI detection by automated algorithms.AIM To describe DILI cases in COVID-19 inpatients detected from data mining in electronic medical records(EMR)using AI and the updated Roussel Uclaf Causality Assessment Method(RUCAM).METHODS The study was conducted in March 2021 in a hospital in southern Brazil.The NoHarm©system uses AI to support decision making in clinical pharmacy.Hospital admissions were 100523 during this period,of which 478 met the inclusion criteria.From these,290 inpatients were excluded due to alternative causes of liver injury and/or due to not having COVID-19.We manually reviewed the EMR of 188 patients for DILI investigation.Absence of clinical information excluded most eligible patients.The DILI assessment causality was possible via the updated RUCAM in 17 patients.RESULTS Mean patient age was 53 years(SD±18.37;range 22-83),most were male(70%),and admitted to the non-intensive care unit sector(65%).Liver injury pattern was mainly mixed,mean time to normalization of liver markers was 10 d,and mean length of hospitalization was 20.5 d(SD±16;range 7-70).Almost all patients recovered from DILI and one patient died of multiple organ failure.There were 31 suspected drugs with the following RUCAM score:Possible(n=24),probable(n=5),and unlikely(n=2).DILI agents in our study were ivermectin,bicalutamide,linezolid,azithromycin,ceftriaxone,amoxicillin-clavulanate,tocilizumab,piperacillin-tazobactam,and albendazole.Lack of essential clinical information excluded most patients.Although rare,DILI is a relevant clinical condition in COVID-19 patients and may contribute to poor prognostics.CONCLUSION The incidence of DILI in COVID-19 inpatients is rare and the absence of relevant clinical information on EMR may underestimate DILI rates.Prospects involve creation and validation of alerts for risk factors in all DILI patients based on RUCAM assessment causality,alterations of liver biomarkers and AI and machine learning.

Clinical use of augmented reality,mixed reality,three-dimensionalnavigation and artificial intelligence in liver surgery

A precise knowledge of intra-parenchymal vascular and biliary architecture and the location of lesions in relation to the complex anatomy is indispensable to perform liver surgery.Therefore,virtual three-dimensional(3D)-reconstruction models from computed tomography/magnetic resonance imaging scans of the liver might be helpful for visualization.Augmented reality,mixed reality and 3Dnavigation could transfer such 3D-image data directly into the operation theater to support the surgeon.This review examines the literature about the clinical and intraoperative use of these image guidance techniques in liver surgery and provides the reader with the opportunity to learn about these techniques.Augmented reality and mixed reality have been shown to be feasible for the use in open and minimally invasive liver surgery.3D-navigation facilitated targeting of intraparenchymal lesions.The existing data is limited to small cohorts and description about technical details e.g.,accordance between the virtual 3D-model and the real liver anatomy.Randomized controlled trials regarding clinical data or oncological outcome are not available.Up to now there is no intraoperative application of artificial intelligence in liver surgery.The usability of all these sophisticated image guidance tools has still not reached the grade of immersion which would be necessary for a widespread use in the daily surgical routine.Although there are many challenges,augmented reality,mixed reality,3Dnavigation and artificial intelligence are emerging fields in hepato-biliary surgery.

Prognosis of acute-on-chronic liver failure patients treated with artificial liver support system

AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort(n = 113) and a validation cohort(n = 68). Laboratory parameters at baseline were analyzed and correlatedwith clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange(PE) or plasma bilirubin adsorption(PBA) combined with plasma exchange. For the derivation cohort, KaplanMeier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve.RESULTS: The mean overall survival for the derivation cohort was 441 d(95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d(95%CI: 455-605 d) and 343 d(95%CI: 254-432 d), respectively, which were significantly different(P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease(MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort(P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD.CONCLUSION: A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system.

Lesson learnt from 60 years of liver transplantation:Advancements,challenges,and future directions

Over the past six decades,liver transplantation(LT)has evolved from an experimental procedure into a standardized and life-saving intervention,reshaping the landscape of organ transplantation.Driven by pioneering breakthroughs,technological advancements,and a deepened understanding of immunology,LT has seen remarkable progress.Some of the most notable breakthroughs in the field include advances in immunosuppression,a revised model for end-stage liver disease,and artificial intelligence(AI)-integrated imaging modalities serving diagnostic and therapeutic roles in LT,paired with ever-evolving technological advances.Additionally,the refinement of transplantation procedures,resulting in the introduction of alternative transplantation methods,such as living donor LT,split LT,and the use of marginal grafts,has addressed the challenge of organ shortage.Moreover,precision medicine,guiding personalized immunosuppressive strategies,has significantly improved patient and graft survival rates while addressing emergent issues,such as short-term complications and early allograft dysfunction,leading to a more refined strategy and enhanced postoperative recovery.Looking ahead,ongoing research explores regenerative medicine,diagnostic tools,and AI to optimize organ allocation and posttransplantation car.In summary,the past six decades have marked a transformative journey in LT with a commitment to advancing science,medicine,and patient-centered care,offering hope and extending life to individuals worldwide.

Risk factors and prediction of acute kidney injury after liver transplantation:Logistic regression and artificial neural network approaches

BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation.Recently,artificial neural network(ANN)was reported to have better predictive ability than the classical logistic regression(LR)for this postoperative outcome.AIM To identify the risk factors of AKI after deceased-donor liver transplantation(DDLT)and compare the prediction performance of ANN with that of LR for this complication.METHODS Adult patients with no evidence of end-stage kidney dysfunction(KD)who underwent the first DDLT according to model for end-stage liver disease(MELD)score allocation system was evaluated.AKI was defined according to the International Club of Ascites criteria,and potential predictors of postoperative AKI were identified by LR.The prediction performance of both ANN and LR was tested.RESULTS The incidence of AKI was 60.6%(n=88/145)and the following predictors were identified by LR:MELD score>25(odds ratio[OR]=1.999),preoperative kidney dysfunction(OR=1.279),extended criteria donors(OR=1.191),intraoperative arterial hypotension(OR=1.935),intraoperative massive blood transfusion(MBT)(OR=1.830),and postoperative serum lactate(SL)(OR=2.001).The area under the receiver-operating characteristic curve was best for ANN(0.81,95%confidence interval[CI]:0.75-0.83)than for LR(0.71,95%CI:0.67-0.76).The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38,respectively.CONCLUSION The severity of liver disease,pre-existing kidney dysfunction,marginal grafts,hemodynamic instability,MBT,and SL are predictors of postoperative AKI,and ANN has better prediction performance than LR in this scenario.

Emerging artificial intelligence applications in liver magnetic resonance imaging

Chronic liver diseases(CLDs)are becoming increasingly more prevalent in modern society.The use of imaging techniques for early detection,such as magnetic resonance imaging(MRI),is crucial in reducing the impact of these diseases on healthcare systems.Artificial intelligence(AI)algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation.When applied to liver MRI,they have the potential to improve clinical decision making,and increase throughput by automating analyses.With Liver diseases becoming more prevalent in society,the need to implement these techniques to utilize liver MRI to its full potential,is paramount.In this review,we report on the current methods and applications of AI methods in liver MRI,with a focus on machine learning and deep learning methods.We assess four main themes of segmentation,classification,image synthesis and artefact detection,and their respective potential in liver MRI and the wider clinic.We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field.Though there are many hurdles to overcome in implementing AI methods in the clinic,we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.