Routine utilization of machine perfusion in liver transplantation:Ready for prime time?

The last decade has been notable for increasing high-quality research and dramatic improvement in outcomes with dynamic liver preservation.Robust evidence from numerous randomized controlled trials has been pooled by meta-analyses,providing the highest available evidence on the protective effect of machine perfusion(MP)over static cold storage in liver transplantation(LT).Based on a protective effect with less complications and improved graft survival,the field has seen a paradigm shift in organ preservation.This editorial focuses on the role of MP in LT and how it could become the new“gold standard”.Strong collaborative efforts are needed to explore its effects on long-term outcomes.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Numerical parametric study on the influence of location and inclination of large-scale asperities on the shear strength of concreterock interfaces of small buttress dams

When assessing the sliding stability of a concrete dam,the influence of large-scale asperities in the sliding plane is often ignored due to limitations of the analytical rigid body assessment methods provided by current dam assessment guidelines.However,these asperities can potentially improve the load capacity of a concrete dam in terms of sliding stability.Although their influence in a sliding plane has been thoroughly studied for direct shear,their influence under eccentric loading,as in the case of dams,is unknown.This paper presents the results of a parametric study that used finite element analysis(FEA)to investigate the influence of large-scale asperities on the load capacity of small buttress dams.By varying the inclination and location of an asperity located in the concrete-rock interface along with the strength of the rock foundation material,transitions between different failure modes and correlations between the load capacity and the varied parameters were observed.The results indicated that the inclination of the asperity had a significant impact on the failure mode.When the inclinationwas 30and greater,interlocking occurred between the dam and foundation and the governing failure modes were either rupture of the dam body or asperity.When the asperity inclination was significant enough to provide interlocking,the load capacity of the dam was impacted by the strength of the rock in the foundation through influencing the load capacity of the asperity.The location of the asperity along the concrete-rock interface did not affect the failure mode,except for when the asperity was located at the toe of the dam,but had an influence on the load capacity when the failure occurred by rupture of the buttress or by sliding.By accounting for a single large-scale asperity in the concrete-rock interface of the analysed dam,a horizontal load capacity increase of 30%e160%was obtained,depending on the inclination and location of the asperity and the strength of the foundation material.

Hemoperfusion and continuous veno-venous hemodiafiltration for eliminating chlorfenapyr in poisoning patients

Chlorfenapyr is a liposoluble insecticide belonging to the pyrrole family.Chlorfenapyr is activated when the N-ethoxymethyl side chain breaks,forming a toxic metabolite,which uncouples oxidative phosphorylation in the mitochondria,inhibits the production of adenosine triphosphate (ATP),and leads to the death of cells and targe organisms.[1] Symptoms of chlorfenapyr poisoning in patients are mild and atypical in the early stage,especially in patients receiving low dose exposure;however,such cases are rare and may be ignored by physicians,often leading to delayed treatment.[2,3].

New strategy for distal limb perfusion in veno-arterial extracorporeal membrane oxygenation treatment: application of continuous renal replacement therapy tubing

Extracorporeal membrane oxygenation(ECMO)is a technology that can temporarily take over the functions of the heart and lungs.Acute kidney injury is a common issue in patients receiving ECMO treatment,with reported incidence rates ranging from 70%to 85%.[1]To maintain the balance of fluids and electrolytes in patients,continuous renal replacement therapy(CRRT)is frequently employed.

Early peripheral perfusion index predicts 28-day outcome in patients with septic shock

BACKGROUND:To investigate the prognostic value of the peripheral perfusion index(PPI)in patients with septic shock.METHODS:This prospective cohort study,conducted at the emergency intensive care unit of Peking University People's Hospital,recruited 200 patients with septic shock between January 2023 and August 2023.These patients were divided into survival(n=84)and death(n=116)groups based on 28-day outcomes.Clinical evaluations included laboratory tests and clinical scores,with lactate and PPI values assessed upon admission to the emergency room and at 6 h and 12 h after admission.Risk factors associated with mortality were analyzed using univariate and multivariate Cox regression analyses.Receiver operator characteristic(ROC)curve was used to assess predictive performance.Mortality rates were compared,and Kaplan-Meier survival plots were created.RESULTS:Compared to the survival group,patients in the death group were older and had more severe liver damage and coagulation dysfunction,necessitating higher norepinephrine doses and increased fl uid replacement.Higher lactate levels and lower PPI levels at 0 h,6 h,and 12 h were observed in the death group.Multivariate Cox regression identifi ed prolonged prothrombin time(PT),decreased 6-h PPI and 12-h PPI as independent risk factors for death.The area under the curves for 6-h PPI and 12-h PPI were 0.802(95%CI 0.742-0.863,P<0.001)and 0.945(95%CI 0.915-0.974,P<0.001),respectively,which were superior to Glasgow Coma Scale(GCS),Sequential Organ Failure Assessment(SOFA)scores(0.864 and 0.928).Cumulative mortality in the low PPI groups at 6 h and 12 h was signifi cantly higher than in the high PPI groups(6-h PPI:77.52%vs.22.54%;12-h PPI:92.04%vs.13.79%,P<0.001).CONCLUSION:PPI may have value in predicting 28-day mortality in patients with septic shock.

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Prognostic impact and reasons for variability by tumor location in gastric cancer

BACKGROUND Gastric cancer(GC)is a highly prevalent gastrointestinal tract tumor.Several trials have demonstrated that the location of GC can affect patient prognosis.However,the factors determining tumor location remain unclear.AIM To investigate the tumor location of patients,we went on to study the influencing factors that lead to changes in the location of GC.METHODS A retrospective evaluation was carried out on 3287 patients who underwent gastrectomy for GC in Zhejiang Cancer Hospital.The patients were followed up post-diagnosis and post-gastrectomy.The clinicopathological variables and overall survival of the patients were recorded.By analyzing the location of GC,the tumor location was divided into four categories:“Upper”,“middle”,“lower”,and“total”.Statistical software was utilized to analyze the relationship of each variable with the location of GC.RESULTS A total of 3287 patients were included in this study.The clinicopathological indices of gender,age,serum levels of carcinoembryonic antigen(CEA),carbohydrate antigen(CA19-9)and CA72-4 levels,were significantly associated with tumor location in patients with GC.In addition,there was a strong correlation between GC location and the prognosis of postoperative patients.Specifically,patients with“lower”and“middle”GC demonstrated a better prognosis than those with tumors in other categories.CONCLUSION The five clinicopathological indices of gender,age,CEA,CA19-9 and CA72-4 levels exhibit varying degrees of influence on the tumor location.The tumor location correlates with patient prognosis following surgery.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.

Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

Artificial intelligence software for assessing brain ischemic penumbra/core infarction on computed tomography perfusion:A real-world accuracy study

BACKGROUND With the increasingly extensive application of artificial intelligence(AI)in medical systems,the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.AIM To investigate the accuracy of AI diagnostic software(Shukun)in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion.METHODS From November 2021 to March 2022,consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy(MT)post-Shukun AI penumbra assessment were included.Computed tomography angiography(CTA)and perfusion exams were analyzed by AI,reviewed by senior neurointerventional experts.In the case of divergences among the three experts,discussions were held to reach a final conclusion.When the results of AI were inconsistent with the neurointerventional experts’diagnosis,the diagnosis by AI was considered inaccurate.RESULTS A total of 22 patients were included in the study.The vascular recanalization rate was 90.9%,and 63.6%of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up.The computed tomography(CT)perfusion diagnosis by Shukun(AI)was confirmed to be invalid in 3 patients(inaccuracy rate:13.6%).CONCLUSION AI(Shukun)has limits in assessing ischemic penumbra.Integrating clinical and imaging data(CT,CTA,and even magnetic resonance imaging)is crucial for MT decision-making.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

How Does a University’s Computer Science Strength and Location Impact Its Total ChatGPT News?

As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.

Matched pair analysis of the effect of longer hypothermic machine perfusion time on kidney transplant outcomes

BACKGROUND Hypothermic machine perfusion(HMP)has demonstrated benefits in terms of early kidney transplant function compared to static cold storage.While longer preservation times have shown detrimental effects,a previous paired study indicated that longer pump times(the second kidney in a pair)might lead to improved outcomes.AIM To revisit the prior paired study's somewhat unexpected results by reviewing our program's experience.METHODS A total of 61 pairs of transplant recipients who received kidneys from the same donor(2012-2021)were analyzed.Patients were divided into two groups depending on whether they were transplanted first(K1)or second(K2).Therefore,the patients in each pair had identical donor characteristics,except for time on the pump.Statistical analyses included Kaplan-Meyer analysis and paired tests,including McNemar's test,student's paired t-test,or Wilcoxon's test,as appropriate.RESULTS The two groups of recipients had similar demographics(age,body mass index,diabetes,time on dialysis,sensit-ization and retransplants).Cold ischemic times for K1 and K2 were 8.9(95%CI:7.9,9.8)and 14.7 hours(13.7,15.8)(P<0.0001),respectively.Overall,K2 had a higher rate of freedom from biopsy-proven acute rejection at 1 year(P=0.015).Delayed graft function was less common in K2,12/61(20%)than in K1,20/61(33%)(P=0.046).Finally,K2 showed a higher graft survival than K1(P=0.023).CONCLUSION Our results agree with a previous study that suggested possible advantages to longer pump times.Both studies should encourage further research into HMP's potential anti-inflammatory effect.

Normothermic regional perfusion mobile teams in controlled donation after circulatory death pathway: Evidence and peculiarities

To facilitate the implementation of controlled donation after circulatory death(cDCD)programs even in hospitals not equipped with a local Extracorporeal Membrane Oxygenation(ECMO)team(Spokes),some countries and Italian Regions have launched a local cDCD network with a ECMO mobile team who move from Hub hospitals to Spokes for normothermic regional perfusion(NRP)implantation in the setting of a cDCD pathway.While ECMO teams have been clearly defined by the Extracorporeal Life Support Organization,regarding composition,responsibilities and training programs,no clear,widely accepted indications are to date available for NRP teams.Although existing NRP mobile networks were developed due to the urgent need to increase the number of cDCDs,there is now the necessity for transplantation medicine to identify the peculiarities and responsibility of a NRP team for all those centers launching a cDCD pathway.Thus,in the present manuscript we summarized the character-istics of an ECMO mobile team,highlighting similarities and differences with the NRP mobile team.We also assessed existing evidence on NRP teams with the goal of identifying the characteristic and essential features of an NRP mobile team for a cDCD program,especially for those centers who are starting the program.Differences were identified between the mobile ECMO team and NRP mobile team.The common essential feature for both mobile teams is high skills and experience to reduce complications and,in the case of cDCD,to reduce the total warm ischemic time.Dedicated training programs should be developed for the launch of de novo NRP teams.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Machine perfusion and the prevention of ischemic type biliary lesions following liver transplant:What is the evidence?

The widespread uptake of different machine perfusion(MP)strategies for liver transplant has been driven by an effort to minimize graft injury.Damage to the cholangiocytes during the liver donation,preservation,or early posttransplant period may result in stricturing of the biliary tree and inadequate biliary drainage.This problem continues to trouble clinicians,and may have catastrophic consequences for the graft and patient.Ischemic injury,as a result of compromised hepatic artery flow,is a well-known cause of biliary strictures,sepsis,and graft failure.However,very similar lesions can appear with a patent hepatic artery and these are known as ischemic type biliary lesions(ITBL)that are attributed to microcirculatory dysfunction rather than main hepatic arterial compromise.Both the warm and cold ischemic period duration appear to influence the onset of ITBL.All of the commonly used MP techniques deliver oxygen to the graft cells,and therefore may minimize the cholangiocyte injury and subsequently reduce the incidence of ITBL.As clinical experience and published evidence grows for these modalities,the impact they have on ITBL rates is important to consider.In this review,the evidence for the three commonly used MP strategies(abdominal normothermic regional perfusion[A-NRP],hypothermic oxygenated perfusion[HOPE],and normothermic machine perfusion[NMP])for ITBL prevention has been critically reviewed.Inconsistencies with ITBL definitions used in trials,coupled with variations in techniques of MP,make interpretation challenging.Overall,the evidence suggests that both HOPE and A-NRP prevent ITBL in donated after circulatory death grafts compared to cold storage.The evidence for ITBL prevention in donor after brain death grafts with any MP technique is weak.