Organ Allocation Research Articles

Multi-Armed Bandits with Endogenous Learning Curves: An Application to Split Liver Transplantation

Problem Definition: Proficiency in many sophisticated tasks is attained through experience-based learning, in other words, learning by doing. For example, transplant centers’ surgical teams need to practice difficult surgeries to master the skills required. Meanwhile, this experience-based learning may affect other stakeholders, such as patients eligible for transplant surgeries, and require resources, including scarce organs and continual efforts. To ensure that patients have excellent outcomes and equitable access to organs, the organ allocation authority needs to quickly identify and develop medical teams with high aptitudes. This entails striking a balance between exploring surgical combinations with initially unknown full potential and exploiting existing knowledge based on observed outcomes. Methodology/results: We formulate a multi-armed bandit (MAB) model in which parametric learning curves are embedded in the reward functions to capture endogenous experience-based learning. In addition, our model includes provisions ensuring that the choices of arms are subject to fairness constraints to guarantee equity. To solve our MAB problem, we propose the L-UCB and FL-UCB algorithms, variants of the upper confidence bound (UCB) algorithm that attain the optimal [Formula: see text] regret on problems enhanced with experience-based learning and fairness concerns. We demonstrate our model and algorithms on the split liver transplantation (SLT) allocation problem, showing that our algorithms have superior numerical performance compared with standard bandit algorithms in a setting where experience-based learning and fairness concerns exist. Managerial implications: From a methodological point of view, our proposed MAB model and algorithms are generic and have broad application prospects. From an application standpoint, our algorithms could be applied to help evaluate potential strategies to increase the proliferation of SLT and other technically difficult procedures. Funding: The authors would like to acknowledge the support of CMU Tepper’s Health Care Initiative Funding. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2022.0412 .

Long-Term Liver Machine Perfusion Preservation: A Review of Recent Advances, Benefits and Logistics.

The global shortage of suitable donor livers for transplantation has prompted efforts to expand the donor pool by using extended criteria donors. Machine preservation technology has shown promise in optimizing graft preservation and improving logistics. Additionally, it holds potential for organ repair, regeneration, therapeutic applications during extended preservation periods, and enhancing organ allocation. We conducted a comprehensive literature review using PubMed, Embase, and Web of Science databases. All studies published between January 1, 2022, and February 7, 2024, that described machine perfusion preservation of livers for more than 24 h were eligible for inclusion. The findings were synthesized in a narrative review format to highlight key benefits and advancements. We identified eleven studies from multiple research groups, employing various techniques, devices, and preservation durations. Perfusion durations ranged from 1 to 13 days, with notable variations in protocols for long-term preservation beyond 24 h. Viability was assessed during perfusion only. No livers were transplanted. Among the reviewed studies, the introduction of a dialysis system emerged as the most effective strategy for managing waste accumulation during long-term liver perfusion. Differences were also observed in hemodynamics, oxygenation, organ chambers, supplemental regimens, and glycemic control. Over the past two years, substantial progress has been made in refining protocols for long-term liver machine perfusion, with significant advancements in waste management, enabling successful multi-day perfusions. While these developments are promising, further research is necessary to standardize and optimize long-term perfusion protocols, establishing a reliable platform for both organ preservation and therapeutic applications.

HepatoPredict Accurately Selects Hepatocellular Carcinoma Patients for Liver Transplantation Regardless of Tumor Heterogeneity

Background/Objectives: Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths rising worldwide. This is leading to an increased demand for liver transplantation (LT), the most effective treatment for HCC in its initial stages. However, current patient selection criteria are limited in predicting recurrence and raise ethical concerns about equitable access to care. This study aims to enhance patient selection by refining the HepatoPredict (HP) tool, a machine learning-based model that combines molecular and clinical data to forecast LT outcomes. Methods: The updated HP algorithm was trained on a two-center dataset and assessed against standard clinical criteria. Its prognostic performance was evaluated through accuracy metrics, with additional analyses considering tumor heterogeneity and potential sampling bias. Results: HP outperformed all clinical criteria, particularly regarding negative predictive value, addressing critical limitations in existing selection strategies. It also demonstrated improved differentiation of recurrence-free and overall survival outcomes. Importantly, the prognostic accuracy of HP remained largely unaffected by intra-nodule and intra-patient heterogeneity, indicating its robustness even when biopsies were taken from smaller or non-dominant nodules. Conclusions: These findings support the usage of HP as a valuable tool for optimizing LT candidate selection, promoting fair organ allocation and enhancing patient outcomes through integrated analysis of molecular and clinical data.

No Vaccine, No Organ? Ethics of Vaccine Mandates for Pediatric Transplant.

Many transplant programs worldwide are likely to impose vaccine mandates for pediatric solid organ transplant candidates; some already do. Three potential benefits that advocates invoke to justify mandates are improved patient outcomes, efficient organ allocation, and contributions to community protection. We show that none of these benefits can justify mandates. The medical benefits of mandates are unlikely to outweigh the risks of denying life-saving care, mandates threaten trust and equity in organ allocation, and the impact on community protection is likely negligible, while the burden on unvaccinated children would be disproportionate. We also reject the claim that clinician burdens in dealing with vaccine refusers are good reasons for mandates, and point out, to the contrary, that the potential political backlash to mandates is a good reason for restraint. Rather, we argue that vaccine mandates for pediatric transplant candidates should be a last resort; they should only be considered after all evidence-based noncoercive measures have been exhausted, and after mandates for transplant professionals and staff are in place. Since there is little evidence that all such measures have been attempted, it is premature to consider vaccine mandates.

Pediatric Age-Prioritized Waitlisting Policy Potentially Disadvantages Adolescents on Dialysis Not Listed for Transplant Until Adulthood.

Current kidney transplant (KT) policies offer advantages in waiting time and organ allocation priority to pediatric patients waitlisted before 18 years old. This study evaluates the effects of this policy for patients who are on dialysis before, but not waitlisted until after, age 18. Patients aged 11-25 years and waitlisted between 2001 and 2022 for KT were identified in the OPTN STAR data file for analysis. Cohorts were defined by age and dialysis status at time of listing: Peds if < 18 yo, young adult (YA) if ≥ 18 yo; NYOD-not yet on dialysis or OD-on dialysis at time of listing, with YA groups further segregated by age at dialysis initiation. Cumulative incidence of transplant was calculated for waitlisted patients. Graft survival was assessed using Kaplan-Meier analysis and multivariable Cox proportional hazards modeling. p values < 0.01 were significant. Amongst 35 764 KT registrations, candidates who initiated dialysis as pediatric patients but were not waitlisted until after turning 18 years old (YA + OD < 18) have the highest rate of nontransplantation (33.5%) and longest time on dialysis (median: 2103 days) before deceased donor (DD) KT. YA + OD < 18 patients were sixfold less likely than Peds + OD patients to undergo DDKT at 5 years after listing. YA + OD < 18 recipients had the worst post-KT graft survival of all groups at 5 years with adjusted hazard ratio of 1.477 (95% confidence interval: 1.218-1.792) compared to Peds-NYOD (p < 0.001). Current allocation policies significantly disadvantage candidates who initiate dialysis before, but are not listed until after age 18, and should be re-examined to address these inequities.

The Dynamics of Deceased Donor Kidney Transplant Decision-Making: Insights from Studying Individual Clinicians' Offer Decisions.

Despite the high demand, over 7,500 recovered kidneys annually go unused, with transplant centers showing significant variation in their offer acceptance practices. However, it remains unclear how much of this variation occurs between individual clinicians within the same center and its impact on allocation efficiency and equity. This study quantified the variability in kidney offer acceptance decisions attributable to clinicians versus enters and examined the role of donor quality in acceptance decisions. We analyzed national transplant registry data (Jan. 2016-Dec. 2020) linked to on-call records from 15 transplant centers, creating a clinician-level dataset with 344,678 deceased donor kidney offers. The primary outcome was the variability in offer acceptance attributable to clinicians versus centers, quantified via hierarchical, mixed effects logistic regression models. To complement KDPI as a measure of donor quality, we incorporated Expected Acceptance Probability (EAP), which adjusts for a broader set of donor characteristics and also recipient factors. Both center-level (0.35, 95% CI: 0.15-0.79) and clinician-level variance (0.10, 95% CI: 0.06-0.18) were significant, with heterogeneity in the KDPI-acceptance association among clinicians. These results underscore the need for further research into the mechanisms driving the clinician-level variation and its implications for organ allocation efficacy, equity, and patient outcomes.

Evaluating allograft risk models in organ transplantation: Understanding and balancing model discrimination and calibration.

In the field of organ transplantation, the accurate assessment of donor organ quality is necessary to efficient organ allocation and to informed consent for recipients. A common approach to organ quality assessment is the development of statistical models that accurately predict post-transplant survival by integrating multiple characteristics of the donor and allograft. Despite the proliferation of predictive models across many domains of medicine, many physicians may have limited familiarity with how these models are built, the assessment of how well models function in their population and the risks of a poorly performing model. Our goal in this perspective is to offer advice to transplant professionals about how to evaluate a prediction model, focusing on the key aspects of discrimination and calibration. We use liver allograft assessment as a paradigm example, but the lessons pertain to other scenarios too.

Risk factors for survival after lung transplantation in cystic fibrosis: impact of colonization with multidrug-resistant strains of Pseudomonas aeruginosa.

Lung transplantation is the ultimate treatment option for patients with advanced cystic fibrosis. Chronic colonization of these recipients with multidrug-resistant (MDR) pathogens may constitute a risk factor for an adverse outcome. We sought to analyze whether colonization with MDR pathogens, as outlined in the German classification of multiresistant Gram-negative bacteria (MRGN), was associated with the success of lung transplantation. We performed a monocentric retrospective analysis of 361 lung transplantations performed in Homburg, Germany, between 1995 and 2020. All recipients with a main diagnosis of cystic fibrosis (n = 69) were stratified into two groups based on colonization with Pseudomonas aeruginosa in view of MRGN before transplantation: no colonization and colonization without (n = 23) or with (n = 46) resistance to three or four antibiotic groups (3MRGN/4MRGN). Multivariable analyses were performed including various clinical parameters (preoperative data, postoperative data). CF patients colonized with multidrug-resistant pathogens (Pseudomonas aeruginosa) classified as 3MRGN/4MRGN had poorer survival (median survival 16 years (without MRGN) versus 8 years (with MRGN), P = 0.048). Extracorporeal support (P = 0.014, HR = 2.929), re-transplantation (P = 0.023, HR = 2.303), female sex (P = 0.019, HR = 2.244) and 3MRGN/4MRGN (P = 0.036, HR = 2.376) were predictors of poor outcomes in the multivariate analysis. Co-colonization with the mold Aspergillus fumigatus was further associated with mortality risk in the 3MRGN/4MRGN group (P = 0.037, HR = 2.150). Patients with cystic fibrosis and MDR colonization (Pseudomonas aeruginosa) are risk candidates for lung transplantation, targeted diagnostics and tailored anti-infective strategies are essential for survival after surgery. MDR colonization as expressed by MRGN may help to identify patients at increased risk to improve the organ allocation process.

Challenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions.

This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological interpretation and allocation decisions driven by biopsy findings. With the increasing use of artificial intelligence (AI) in digital pathology, it is timely to evaluate whether these advancements can overcome current challenges and improve organ allocation amidst a growing organ shortage. Significant inconsistencies exist in biopsy methodologies, including core versus wedge sampling, frozen versus paraffin-embedded processing, and variability in pathologist expertise. These differences complicate study comparisons and limit the reproducibility of histological assessments. Emerging AI-driven tools and digital pathology show potential for standardizing assessments, enhancing reproducibility, and reducing dependence on expert pathologists. However, few studies have validated their clinical utility or demonstrated their predictive performance for long-term outcomes. Novel AI-driven tools hold promise for improving the standardization and accuracy of preimplantation kidney biopsy assessments. However, their clinical application remains limited due to a lack of proven associations with posttransplant outcomes and insufficient evaluation of predictive performance metrics. Future research should prioritize longitudinal studies using large-scale datasets, rigorous validation, and comprehensive assessments of predictive performance for both short- and long-term outcomes to fully establish their clinical utility.

Prognostic Value of Ambulatory Status at Transplant in Older Heart Transplant Recipients: Implications for Organ Allocation Policy.

Shortage of organ donors in the Republic of Korea has become a major problem. To address this, it has been questioned whether heart transplant (HTx) allocation should be modified to reduce priority of older patients. We aimed to evaluate post-HTx outcomes according to recipient age and specific pre-HTx conditions using a nationwide prospective cohort. We analyzed clinical characteristics of 628 patients from the Korean Organ Transplant Registry who received HTx from January 2015 to December 2020. Enrolled recipients were divided into three groups according to age. We also included comorbidities including ambulatory status. Non-ambulatory status was defined as pre-HTx support with either extracorporeal membrane oxygenation, continuous renal replacement therapy, or mechanical ventilation. Of the 628 patients, 195 were < 50 years, 322 were 50-64 years and 111 were ≥ 65 years at transplant. Four hundred nine (65.1%) were ambulatory and 219 (34.9%) were non-ambulatory. Older recipients tended to have more comorbidities, ischemic cardiomyopathy, and received older donors. Post-HTx survival was significantly lower in older recipients (P = 0.025) and recipients with non-ambulatory status (P < 0.001). However, in contrast to non-ambulatory recipients who showed significant survival differences according to the recipient's age (P = 0.004), ambulatory recipients showed comparable outcomes (P = 0.465). Our results do not support use of age alone as an allocation criterion. Transplant candidate age in combination with some comorbidities such as non-ambulatory status may identify patients at a sufficiently elevated risk at which suitability of HTx should be reconsidered.

Risk Estimation of Severe Primary Graft Dysfunction in Heart Transplant Recipients Using a Smartphone.

Currently, there are no standardized guidelines for graft allocation in heart transplants (HTxs), particularly when considering organs from marginal donors and donors after cardiocirculatory arrest. This complexity highlights the need for an effective risk analysis tool for primary graft dysfunction (PGD), a severe complication in HTx. Existing score systems for predicting PGD lack superior predictive capability and are often too complex for routine clinical use. This study sought to develop a user-friendly score integrating variables from these systems to enhance the efficacy of the organ allocation process. Severe PGD was defined as the need for mechanical circulatory support and/or death from an unknown etiology within the first 24 hours following HTx. We used a meta-analytical approach to create a derivation cohort to identify risk factors. We then applied a logistic regression analysis to generate an equation predicting severe PGD risk. We used our previous experience in HTx to create a validation cohort. Subsequently, we implemented the formula in a smartphone application. The meta-analysis comprising six studies revealed a 10.5% ( 95% confidence interval (CI): 5.3-12.4) incidence rate of severe PGD and related 30-day mortality of 38.6%. Eleven risk factors were identified: female donors, female donor to male recipient, undersized donor, donor age, recipient on ventricular assist device support, recipient on amiodarone treatment, recipient with diabetes and renal dysfunction, re-sternotomy, graft ischemic time, and bypass time. An equation to predict the risk, including the 11 parameters (GREF-11), was created using logistic regression models and validated based on our experience involving 116 patients. In our series, 29 recipients (25%) required extracorporeal membrane oxygenation support within 24 hours post-HTx. The overall 30-day mortality was 4.3%, 3.4%, and 6.8% in the non-PGD and severe PGD groups, respectively. The area under the receiver operating characteristic (AU-ROC) curve of the model in the validation cohort was 0.804. The GREF-11 application should offer HTx teams several benefits, including standardized risk assessment and bedside clinical decision support, thereby helping minimize the risk of severe PGD post-HTx.

Out of Sequence Liver Transplants: A Nationwide Analysis of Organ Utilization and Allocation Equity

Out of Sequence Liver Transplants: A Nationwide Analysis of Organ Utilization and Allocation Equity

Survival benefit associated with liver transplantation for hepatocellular carcinoma based on tumor burden scores at listing.

Liver transplantation (LT) provides significant survival benefits to patients with unresectable HCC. In the United States, organ allocation policies for HCCs within the United Network for Organ Sharing criteria do not prioritize patients based on their differences in oncological characteristics. This study assessed whether transplant-associated survival benefits (TASBs) vary among patients with different tumor burden scores (TBS) measured at the time of listing. We analyzed data from adults applying for HCC MELD exception points between 2002 and 2019, with follow-up until December 2023, using the Scientific Registry of Transplant Recipients. TBS was determined based on the largest tumor diameter and number of HCCs. Patients were categorized into low (≤3), intermediate (3.1-5), and high (>5) TBS groups. TASB was measured as the difference in 5-year survival with and without LT. This study included 36,634 LT candidates. High-TBS patients had higher waitlist dropout rates and marginally lower post-transplant survival, resulting in a significantly greater TASB. The 5-year TASB for the low, intermediate, and high TBS groups were 15.7, 22.1, and 25.0 months, respectively. The adjusted survival benefit expressed in 5-year survival differences was 21.9%, 34.5%, and 39.4% in the low, intermediate, and high TBS groups, respectively (p<0.001). Higher TBS during listing correlates with greater LT benefits for patients with unresectable HCC within UNOS criteria. We conclude that organ allocation policies in the United States should prioritize patients with high TBS due to their increased risk of dropout and comparable post-transplant survival when compared to patients with less advanced tumors.

The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.

COVID-19 mortality among solid organ transplant recipients and candidates before specific vaccine availability in Colombia.

Coronavirus disease 2019 (COVID-19) is a life-threatening disease that was declared a pandemic in March 2020. Organ transplant recipients are vulnerable to infection and complications from COVID-19. The objective of this study was to investigate the rates of infection, mortality, and case-fatality ratios (CFR) in solid organ transplant recipients and patients on the waiting list for organ allocation in the period prior to the availability of specific vaccines. This was an observational study of official sources that are used to report information on COVID-19. Quantitative variables were described with arithmetic means and categorical variables with proportions. Percentages of positivity to infection, number of deaths, CFR, and all-cause mortality were calculated for each group and subgroup. There were 2,551 eligible subjects; 602 (26.2%) were positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There were 265 (10.4%) deaths from all causes during the follow-up period; 119 (44.9%) of them were associated with COVID-19, which indicated a COVID-19-related mortality rate of 4.7 %. CFRs were 21.4% and 17.1% in transplant recipients and waitlisted patients, respectively. CFR was significantly higher in transplant recipients (23.8%) than in patients waitlisted for kidney (16.5%; p = 0.044) Among SARS-CoV-2-positive patients, the probability of dying from COVID-19 was higher in the first group (87.3% and 73%, respectively; p = 0.034). COVID-19 had a significant impact on the deaths of transplant patients and patients on the solid organ waiting list during the first year of the pandemic in Colombia, before the availability of vaccines.

The regulatory module BnamiR827-BnaA09.NLA1-BnaPHT1s modulates phosphate homeostasis, pollen viability and seed yield in Brassica napus.

Phosphorus (P) is an essential macronutrient for the growth and yield of crops. However, there is limited understanding of the regulatory mechanisms of phosphate (Pi) homeostasis, and its impact on growth, development, and yield-related traits in Brassica napus. Here, we identified four NITROGEN LIMITATION ADAPATATION1 (BnaNLA1) genes in B. napus, their expression was predominant in roots and suppressed by Pi starvation-induced MicroRNA827s (BnamiR827s). All the BnaNLA1 proteins have similar sequences, subcellular localizations, and abilities to rescue the growth defects of atnla1 mutant. One of the genes, BnaA09.NLA1 expressed abundantly in roots, and also in old leaves, anthers and pollens. Knocking out of BnaNLA1(s) or overexpressing BnamiR827 resulted in increased concentrations of Pi in leaves as well as in stamen and had reduced pollen viability thereby negatively impacting seed yield. BiFC and split-ubiquitin Y2H analyses demonstrated that BnaA09.NLA1 interacted with seven Pi transporters highly expressed in roots and/or anthers (i.e., BnaPT8/10/11/27/35/37/42) to regulate Pi uptake and Pi allocation in anthers. Taken together, this study demonstrates that the BnamiR827-BnaA09.NLA1-BnaPHT1s module is involved in regulating Pi uptake and Pi allocation in floral organs, which is vital for the growth, pollen viability and seed yield of B. napus.

Ethical frontiers in liver transplantation.

Liver transplantation represents a pivotal intervention in the management of end-stage liver disease, offering a lifeline to countless patients. Despite significant strides in surgical techniques and organ procurement, ethical dilemmas and debates continue to underscore this life-saving procedure. Navigating the ethical terrain surrounding this complex procedure is hence paramount. Dissecting the nuances of ethical principles of justice, autonomy and beneficence that underpin transplant protocols worldwide, we explore the modern challenges that plaques the world of liver transplantation. We investigate the ethical dimensions of organ transplantation, focusing on allocation, emerging technologies, and decision-making processes. PubMed, Scopus, Web of Science, Embase and Central were searched from database inception to February 29, 2024 using the following keywords: "liver transplant", "transplantation", "liver donation", "liver recipient", "organ donation" and "ethics". Information from relevant articles surrounding ethical discussions in the realm of liver transplantation, especially with regards to organ recipients and allocation, organ donation, transplant tourism, new age technologies and developments, were extracted. From the definition of death to the long term follow up of organ recipients, liver transplantation has many ethical quandaries. With new transplant techniques, societal acceptance and perceptions also play a pivotal role. Cultural, religious and regional factors including but not limited to beliefs, wealth and accessibility are extremely influential in public attitudes towards donation, xenotransplantation, stem cell research, and adopting artificial intelligence. Understanding and addressing these perspectives whilst upholding bioethical principles is essential to ensure just distribution and fair allocation of resources. Robust regulatory oversight for ethical sourcing of organs, ensuring good patient selection and transplant techniques, and high-quality long-term surveillance to mitigate risks is essential. Efforts to promote equitable access to transplantation as well as prioritizing patients with true needs are essential to address disparities. In conclusion, liver transplantation is often the beacon of hope for individuals suffering from end-stage liver disease and improves quality of life. The ethics related to transplantation are complex and multifaceted, considering not just the donor and the recipient, but also the society as a whole.

Advancements in MELD Score and Its Impact on Hepatology.

There continues to be an ongoing need for fair and equitable organ allocation. The Model for End-Stage Liver Disease (MELD) score has evolved as a calculated framework to evaluate and allocate patients for liver transplantation objectively. The original MELD score has undergone multiple modifications as it is continuously scrutinized for its accuracy in objectively representing the clinical context of patients with liver disease. Several refinements and iterations of the score have been developed, including the widely accepted MELD-Na score. In addition, the most recent updated iteration, MELD 3.0, has been created. The MELD 3.0 calculator incorporates new variables such as patient sex and serum albumin levels and assigns new weights for serum sodium, bilirubin, international normalized ratio, and creatinine levels. It is anticipated that the use of MELD 3.0 scores will reduce overall waitlist mortality and enhance access for female liver transplant candidates. However, despite the emergence of the MELD score as one of the most objective measures for fair organ allocation, various countries and healthcare systems employ alternative methods for stratification and organ allocation. This review article will highlight the origins of the MELD score, its iterations, the current MELD 3.0, and future directions for managing liver transplantation organ allocation. LAY SUMMARY: Organ donation is crucial for the management of patients unwell with liver disease, but organs must be allocated fairly and equitably. One method used for this is the Model for End-Stage Liver Disease (MELD) score, which helps objectively decide which patient is a candidate for liver transplant. Over time, the MELD score has been refined to better reflect patients' needs. For example, the latest version, MELD 3.0, now considers factors like nutrition and gender. This should ensure that more patients, especially females, are candidates and receive appropriate access to liver transplantation. However, not every country uses the MELD score. Some countries have created their own scoring systems based on local research. This review will explain where the MELD score came from, how it has changed, the current characteristics of the MELD 3.0 score, and what the future might hold for organ allocation in liver transplants.

Effect of innovative peat-free organic growing media and fertilizer on nutrient allocation in pedunculate oak (Quercus robur L.) and European beech (Fagus sylvatica L.) seedlings

This study evaluates the effects of novel peat-free organic growing media and a novel liquid fertilizer on the biometric features and macronutrient allocation of Quercus robur and Fagus sylvatica seedlings with the view to compare biomass and nutrient allocation of plant organs in seedlings cultivated on peat growing medium against those grown on novel peat-free growing medium and fertilizer. The experimental setup involved four growing medium variants, including peat as the control (R20, R21, R22 and C). The novel growing medium and fertilizer were designed and formulated by the University of Agriculture in Kraków, Poland (UAK). Fertilization used in the state forest nurseries was represented as SR20, SR21, and SR22, while the novel fertilizer of UAK was represented as UR20, UR21, and UR22; meanwhile, SC and UC represented the control growing medium (peat) in both cases, respectively. The experiment was laid in a 2 × 2 × 4 experimental design using five seedlings per treatment. Seedlings were assessed for roots, shoots, and leaves biomass. The allocation patterns highlighted the variability of nutrient allocation within the plants, with more nutrients allocated to the root system. Interestingly, treatment UR22 yielded the highest mean root values, root biomass, and virtually all macroelement allocation. The SC solid fertilizer treatment and the UR22 liquid fertilizer treatment consistently showed superior performance across both species and different plant organs. These findings suggest that these treatments are particularly effective in enhancing the nutrient content of oak and beech seedlings, making them suitable choices for optimizing the growth and health of these species. Seedlings were assessed for roots, shoots, and leaves biomass after the nursery production cycle.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.