Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

Animal models of tendon calcification:Past,present,and future

Tendon calcification is a common clinical condition that frequently occurs as a complication after tendon injury and surgery,or as an expression of fibrodysplasia ossificans progressiva.This condition can be referred to by various names in clinical practice and literature,including tendon ossification,tendon mineralization,heterotopic ossification,and calcific tendonitis.The exact pathogenesis of tendon calcification remains uncertain,but current mainstream research suggests that calcification is mostly cell mediated.To further elucidate the pathogenesis of tendon calcification and to better simulate the overall process,selecting appropriate experimental animal models is important.Numerous animal models have been utilized in various clinical studies,each with its own set of advantages and limitations.In this review,we have discussed the advancements made in research on animal models of tendon calcification,with a focus on the selection of experimental animals,the sites of injury in these models,and the methods employed for modeling.

Animal models of eosinophilic esophagitis,review and perspectives

Eosinophilic oesophagitis(EoE)is an allergen/immune-mediated chronic esophageal disease characterized by esophageal mucosal eosinophilic infiltration and esophageal dysfunction.Although the disease was originally attributed to a delayed allergic reaction to allergens and a Th2-type immune response,the exact pathogenesis is complex,and the efficacy of existing treatments is unsatisfactory.Therefore,the study of the pathophysiological process of EOE has received increasing attention.Animal models have been used extensively to study the molecular mechanism of EOE pathogenesis and also provide a preclinical platform for human clinical intervention studies of novel therapeutic agents.To maximize the use of existing animal models of EOE,it is important to understand the advantages or limitations of each modeling approach.This paper systematically describes the selection of experimental animals,types of allergens,and methods of sensitization and excitation during the preparation of animal models of EoE.It also discusses the utility and shortcomings of each model with the aim of providing the latest perspectives on EoE models and leading to better choices of animal models.

Animal models of hepatitis E infection: Advances and challenges

Hepatitis E virus(HEV)is one of the leading causes of acute viral hepatitis worldwide.Although most of HEV infections are asymptomatic,some patients will develop the symptoms,especially pregnant women,the elderly,and patients with preexisting liver diseases,who often experience anorexia,nausea,vom-iting,malaise,abdominal pain,and jaundice.HEV infection may become chronic in immunosuppressed individuals.In addition,HEV infection can also cause several extrahepatic manifestations.HEV exists in a wide range of hosts in nature and can be transmitted across species.Hence,animals susceptible to HEV can be used as models.The establishment of animal models is of great significance for studying HEV transmission,clinical symptoms,extrahepatic manifestations,and therapeutic strategies,which will help us understand the pathogenesis,prevention,and treatment of hepatitis E.This review summarized the animal models of HEV,including pigs,monkeys,rabbits,mice,rats,and other animals.For each animal species,we provided a concise summary of the HEV genotypes that they can be infected with,the cross-species transmission pathways,as well as their role in studying extrahepatic manifestations,prevention,and treatment of HEV infection.The advantages and disadvantages of these animal models were also emphasized.This review offers new perspectives to enhance the current understanding of the research landscape surrounding HEV animal models.

Immunobiology of COVID-19: Mechanistic and therapeutic insights from animal models

The distribution of the immune system throughout the body complicates in vitro assessments of coronavirus disease 2019(COVID-19)immunobiology,often resulting in a lack of reproducibility when extrapolated to the whole organism.Consequently,developing animal models is imperative for a comprehensive understanding of the pathology and immunology of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.This review summarizes current progress related to COVID-19 animal models,including non-human primates(NHPs),mice,and hamsters,with a focus on their roles in exploring the mechanisms of immunopathology,immune protection,and long-term effects of SARS-CoV-2 infection,as well as their application in immunoprevention and immunotherapy of SARS-CoV-2 infection.Differences among these animal models and their specific applications are also highlighted,as no single model can fully encapsulate all aspects of COVID-19.To effectively address the challenges posed by COVID-19,it is essential to select appropriate animal models that can accurately replicate both fatal and non-fatal infections with varying courses and severities.Optimizing animal model libraries and associated research tools is key to resolving the global COVID-19 pandemic,serving as a robust resource for future emerging infectious diseases.

Knee osteoarthritis:A review of animal models and intervention of traditional Chinese medicine

Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poorly understood,as it involves complex mechanisms that result in the same outcome.Consequently,researchers are interested in studying KOA and require appropriate animal models for basic research.Chinese herbal compounds,which consist of multiple herbs with diverse pharmacological properties,possess characteristics such as multicomponent,multipathway,and multitarget effects.The potential benefits in the treatment of KOA continue to attract attention.Purpose:This study aims to provide a comprehensive overview of the advantages,limitations,and specific considerations in selecting different species and methods for KOA animal models.This will help researchers make informed decisions when choosing an animal model.Methods:Online academic databases(e.g.,PubMed,Google Scholar,Web of Science,and CNKI)were searched using the search terms“knee osteoarthritis,”“animal models,”“traditional Chinese medicine,”and their combinations,primarily including KOA studies published from 2010 to 2023.Results:Based on literature retrieval,this review provides a comprehensive overview of the methods of establishing KOA animal models;introduces the current status of advantages and disadvantages of various animal models,including mice,rats,rabbits,dogs,and sheep/goats;and presents the current status of methods used to establish KOA animal models.Conclusion:This study provides a review of the animal models used in recent KOA research,discusses the common modeling methods,and emphasizes the role of traditional Chinese medicine compounds in the treatment of KOA.

Stochastic Maximum Principle for Optimal Advertising Models with Delay and Non-Convex Control Spaces

In this paper we study optimal advertising problems that model the introduction of a new product into the market in the presence of carryover effects of the advertisement and with memory effects in the level of goodwill. In particular, we let the dynamics of the product goodwill to depend on the past, and also on past advertising efforts. We treat the problem by means of the stochastic Pontryagin maximum principle, that here is considered for a class of problems where in the state equation either the state or the control depend on the past. Moreover the control acts on the martingale term and the space of controls U can be chosen to be non-convex but now the space of controls U can be chosen to be non-convex. The maximum principle is thus formulated using a first-order adjoint Backward Stochastic Differential Equations (BSDEs), which can be explicitly computed due to the specific characteristics of the model, and a second-order adjoint relation.

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

How can we establish animal models of HIV-associated lymphoma?

Human immunodeficiency virus(HIV)infection is strongly associated with a height-ened incidence of lymphomas.To mirror the natural course of human HIV infection,animal models have been developed.These models serve as valuable tools to inves-tigate disease pathobiology,assess antiretroviral and immunomodulatory drugs,ex-plore viral reservoirs,and develop eradication strategies.However,there are currently no validated in vivo models of HIV-associated lymphoma(HAL),hampering progress in this crucial domain,and scant attention has been given to developing animal models dedicated to studying HAL,despite their pivotal role in advancing knowledge.This re-view provides a comprehensive overview of the existing animal models of HAL,which may enhance our understanding of the underlying pathogenesis and approaches for malignancies linked to HIV infection.

Preliminary exploration of animal models of congenital choledochal cysts

BACKGROUND Various animal models have been used to explore the pathogenesis of choledochal cysts(CCs),but with little convincing results.Current surgical techniques can achieve satisfactory outcomes for treatment of CCs.Consequently,recent studies have focused more on clinical issues rather than basic research.Therefore,we need appropriate animal models to further basic research.AIM To establish an appropriate animal model that may contribute to the investigation of the pathogenesis of CCs.METHODS Eighty-four specific pathogen-free female Sprague-Dawley rats were randomly allocated to a surgical group,sham surgical group,or control group.A rat model of CC was established by partial ligation of the bile duct.The reliability of the model was confirmed by measurements of serum biochemical indices,morpho-logy of common bile ducts of the rats as well as molecular biology experiments in rat and human tissues.RESULTS Dilation classified as mild(diameter,≥1 mm to<3 mm),moderate(≥3 mm to<10 mm),and severe(≥10 mm)was observed in 17,17,and 2 rats in the surgical group,respectively,while no dilation was observed in the control and sham surgical groups.Serum levels of alanine aminotransferase,aspartate aminotrans-ferase,total bilirubin,direct bilirubin,and total bile acids were significantly elevated in the surgical group as compared to the control group 7 d after surgery,while direct bilirubin,total bilirubin,and gamma-glutamyltransferase were further increased 14 d after surgery.Most of the biochemical indices gradually decreased to normal ranges 28 d after surgery.The protein expression trend of signal transducer and activator of transcription 3 in rat model was consistent with the human CC tissues.CONCLUSION The model of partial ligation of the bile duct of juvenile rats could morphologically simulate the cystic or fusiform CC,which may contribute to investigating the pathogenesis of CC.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Progress in experimental models to investigate the in vivo and in vitro antidiabetic activity of drugs

Diabetes mellitus is one of the world's most prevalent and complex metabolic disorders,and it is a rapidly growing global public health issue.It is characterized by hyperglycemia,a condition involving a high blood glucose level brought on by deficiencies in insulin secretion,decreased activity of insulin,or both.Prolonged effects of diabetes include cardiovascular problems,retinopathy,neuropathy,nephropathy,and vascular alterations in both macro-and micro-blood vessels.In vivo and in vitro models have always been important for investigating and characterizing disease pathogenesis,identifying targets,and reviewing novel treatment options and medications.Fully understanding these models is crucial for the researchers so this review summarizes the different experimental in vivo and in vitro model options used to study diabetes and its consequences.The most popular in vivo studies involves the small animal models,such as rodent models,chemically induced diabetogens like streptozotocin and alloxan,and the possibility of deleting or overexpressing a specific gene by knockout and transgenic technologies on these animals.Other models include virally induced models,diet/nutrition induced diabetic animals,surgically induced models or pancreatectomy models,and non-obese models.Large animals or non-rodent models like porcine(pig),canine(dog),nonhuman primate,and Zebrafish models are also outlined.The in vitro models discussed are murine and human beta-cell lines and pancreatic islets,human stem cells,and organoid cultures.The other enzymatic in vitro tests to assess diabetes include assay of amylase inhibition and inhibition ofα-glucosidase activity.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.