Transplantation最新文献

Background: Hepatic ischemia/reperfusion (I/R) injury (HIRI) is an intrinsic phenomenon observed in the process of various liver surgeries. Unfortunately, there are currently few options available to prevent HIRI. Accordingly, we aim to explore the role and key downstream effects of B-cell lymphoma 6 (BCL6) in hepatic I/R (HIR).

Methods: BCL6 expression levels were measured in I/R liver tissue and primary hepatocytes stimulated by hypoxia/reoxygenation (H/R). Moreover, we ascertained the BCL6 effect on HIR in vivo using liver-specific BCL6 knockout mice and adenovirus-BCL6-infected mice. RNA-sequencing, luciferase, chromatin immunoprecipitation, and interactome analysis were combined to identify the direct target and corresponding molecular events contributing to BCL6 function. DNA pull-down was applied to identify upstream of BCL6 in the H/R challenge.

Results: HIR represses BCL6 expression in vivo and in vitro. Hepatic BCL6 overexpression attenuates inflammation and apoptosis after I/R injury, whereas BCL6 deficiency aggravates I/R-induced liver injury. RNA-sequencing showed that BCL6 modulated nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 3 inflammasome signaling in HIRI. Mechanistically, BCL6 deacetylated nuclear factor kappa-B p65 lysine 310 by recruiting sirtuin 1 (SIRT1), thereby inhibiting the nuclear factor kappa-B/nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 3 pathway. Moreover, overexpression of SIRT1 blocked the detrimental effects of BCL6 depletion. Moreover, EX 527, a SIRT1 inhibitor, vanished protection from BCL6 overexpression. Furthermore, transcription factor 7 was found to mediate the transcription regulation of BCL6 on H/R challenge.

Conclusions: Our results provide the first evidence supporting BCL6 as an important protective agent of HIR. This suggests a potential therapeutic approach for HIR.

Background: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.

Methods: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio. Independent risk factors for PGD3 were determined by integrating the univariate logistic regression and least absolute shrinkage and selection operator regression analyses. Subsequently, 9 ML models were used to construct prediction models for PGD3 based on selected variables. Their prediction performances were further evaluated. Besides, model stratification performance was assessed with 3 posttransplant metrics. Finally, the SHapley Additive exPlanations algorithm was used to understand the predictive importance of selected variables.

Results: We identified 9 independent clinical risk factors as selected variables. Among 9 ML models, the random forest (RF) model displayed optimal performance (area under the curve [AUC] = 0.9415, sensitivity [Se] = 0.8972, specificity [Sp] = 0.8795 in the training cohort; AUC = 0.7975, Se = 0.7520, Sp = 0.7313 in the internal validation cohort; and AUC = 0.8214, Se = 0.8235, Sp = 0.6667 in the external validation cohort). Further assessments on calibration and clinical usefulness indicated the promising applicability of the RF model in PGD3 prediction. Meanwhile, the RF model also performed best in terms of risk stratification for postoperative support (extracorporeal membrane oxygenation time: P < 0.001, mechanical ventilation time: P = 0.006, intensive care unit time: P < 0.001).

Conclusions: The RF model had the optimal performance in PGD3 prediction and postoperative risk stratification for patients after Lung Tx.

Background: Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed.

Methods: This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained.

Results: Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score.

Conclusions: The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.

Background: Despite efforts to ensure equitable access to liver transplantation (LT), significant disparities remain. Although prior literature has considered the effects of patient sex, race, and income, the contemporary impact of community socioeconomic disadvantage on outcomes after waitlisting for LT remains to be elucidated. We sought to evaluate the association of community-level socioeconomic deprivation with survival after waitlisting for LT.

Methods: All waitlisted candidates for isolated LT were identified using the 2005-2023 Organ Procurement and Transplantation Network. The previously validated Distressed Communities Index, representing poverty rate, median household income, unemployment, business growth, education level, and housing vacancies, was used to characterize community socioeconomic distress. Zip codes in the highest quintile were classified as the "distressed" cohort (others: "nondistressed"). Kaplan-Meier and Cox proportional hazard models were applied to assess patient and graft survival. We performed a Fine and Gray competing risk regression to consider the impact of distress on waitlist mortality.

Results: Of 169 601 patients, 95 020 (56%) underwent LT and 74 581 (44%) remained on the waitlist. Among transplanted patients, 18 774 (20%) were distressed. After adjustment, distressed faced similar posttransplant survival at 1 y but greater mortality hazard at 5 y (hazard ratio [HR], 1.08; 95% confidence interval [CI], 1.04-1.12) and 10 y (HR, 1.09; 95% CI, 1.05-1.12). Considering all waitlisted patients, competing risk analysis demonstrated distressed candidates to face significantly greater cumulative incidence of death/deterioration on the waitlist (HR, 1.07; 95% CI, 1.04-1.11).

Conclusions: Community-level socioeconomic inequity is associated with greater waitlist mortality and inferior post-LT survival. Novel interventions are needed to address structural barriers to care and continued inequities in outcomes.