Citations

ABSTRACT Background With increasing use of donation after circulatory death (DCD) donor grafts, the need for accurate risk‐stratification tools in liver transplantation has become critical. Machine perfusion has been shown to improve both graft and patient survival. However, existing prediction models were developed in the static cold storage (SCS) era and their accuracy when using machine perfusion remains unknown. Our study aims to study if current scoring systems can predict graft and patient survival for DCD livers preserved using normothermic machine perfusion (NMP). Methods This was a retrospective study of patient and graft survival in a two‐center U.S. DCD NMP cohort ( n = 532) between January 2022 and December 2024. Predicted graft and patient survival rates were obtained from established risk scoring systems, including the United Kingdom DCD risk score (UK‐DCD), Early Allograft Dysfunction (EAD) Olthoff score, Liver Graft Assessment Following Transplantation (L‐GrAFT 7 ) score, Donor Risk Index (DRI), Balance of Risk (BAR) score, and Survival Outcomes Following Liver Transplant (SOFT) score. Model discrimination was assessed using receiver operating characteristic (ROC) curves (C‐statistics). Survival outcomes across risk strata were estimated and compared using Kaplan‐Meier (KM) analysis. Results Across scoring systems, predicted survival consistently underestimated actual graft and patient survival in the NMP DCD cohort. The DRI, SOFT, EAD and L‐GrAFT 7 did not demonstrate predictive value. UK‐DCD predicted ≤85% and <40% 1‐year graft survival for its high‐risk and “futile” groups, whereas observed rates were 93.7% and 86.8%. BAR low‐risk predicted >60% 5‐year survival but 94.2% was observed; the high‐risk group predicted <60% and observed was 53.3%. Despite underestimations and generally poor C‐statistics, in KM stratification, high‐risk groups still showed statistical differences in the UK‐DCD categories ( p = 0.007), and in SOFT score groups ( p < 0.001). No statistical difference was seen in BAR ( p = 0.36), DRI ( p = 0.36), EAD ( p = 0.12), or L‐GrAFT 7 ( p = 0.75). Conclusion Existing liver transplant risk scores show limited predictive value for donor‐related risk factors in the NMP era, whereas recipient‐related factors remain somewhat predictive, despite overestimating risk. Early allograft dysfunction metrics were not reliable predictors of short or long‐term outcomes. Future risk models should integrate machine‐perfusion parameters and refined donor variables to reflect outcomes more accurately in contemporary DCD liver transplantation.

Donation after circulatory death (DCD) liver transplants (LTs) are associated with increased risk of post-LT acute kidney injury (AKI). This retrospective study aimed to examine the effect of continuous normothermic machine perfusion (NMP) on the incidence and stage of AKI in DCD LT. The incidence of AKI within 48 hours was compared between 507 NMP-DCD and 472 static cold storage (SCS)-DCD LT transplanted between January 1, 2016, and December 31, 2024. AKI developed in 337 (34%) patients. On univariate analysis, AKI rate was lower in NMP-DCD compared with SCS-DCD (28% vs. 41%, p <0.001). Stages 2 and 3 AKI at 48 hours and renal replacement therapy (RRT) within the first 3 weeks were also lower in NMP-DCD compared with SCS-DCD ( p <0.001). After adjusting for multiple recipient, donor, and intraoperative factors using multivariate logistic regression, NMP-DCD was independently associated with a 37% lower odds of AKI (aOR: 0.63, CI: 0.46–0.85, p =0.003) and 55% lower odds of stages 2 and 3 AKI (aOR: 0.45, CI: 0.29–0.69, p <0.001) compared with SCS-DCD. To mitigate for the era effect and other unmeasured confounders, an inverse probability of treatment weighting (IPTW) analysis was performed using a propensity score model with average treatment effect (ATE). After weighting for multiple factors, NMP-DCD was associated with lower odds of AKI at 48 hours (OR: 0.65, CI: 0.53–0.81, p =0.001), stages 2 and 3 AKI at 48 hours (OR: 0.60, CI: 0.42–0.85, p =0.02), and RRT at day 7 (OR: 0.31, CI: 0.13–0.65, p =0.003) compared with SCS-DCD. In conclusion, compared with SCS-DCD, NMP-DCD was independently associated with lower odds of AKI at 48 hours, of stages 2 and 3 AKI at 48 hours from LT, and of RRT requirement at day 7 from LT, an effect that extended for the first 3 post-LT weeks.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract Background High‐resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super‐resolution (SR) techniques offer a post‐acquisition solution to enhance resolution, existing deep learning approaches face trade‐offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. Purpose This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. Materials and Methods We propose a novel SR framework based on multi‐head selective state‐space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long‐range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN‐based (CycleGAN, Pix2pix, SPSR), transformer‐based (SwinIR), Mamba‐based (MambaIR), and diffusion‐based (I 2 SB, Res‐SRDiff) methods. Results The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: ) and peak signal‐to‐noise ratio (PSNR: dB), along with the best perceptual quality scores (LPIPS: ; GMSD: ). These results represented statistically significant improvements over all baselines (), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res‐SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of , PSNR of dB, LPIPS of , and GMSD of . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res‐SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. Conclusion The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well‐defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state‐of‐the‐art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows.

Importance Ischemia-reperfusion injury contributes to graft dysfunction after liver transplant. Hypothermic oxygenated perfusion (HOPE) has been shown to improve liver function, but its benefit in extended criteria donors in a US population remains unclear. Objective To assess whether back-to-base portal-venous HOPE impacts outcomes after liver transplant in the US. Design, Setting, and Participants The Bridge to HOPE Trial is a multicenter, randomized, open-label trial with 1-year follow-up. The study took place at 15 US liver transplant centers and patients were enrolled between January 2021 and May 2023. Central assessment of complications and cholangiograms for nonanastomotic biliary strictures (NAS) was blinded to treatment arm. Participants included 219 recipients of extended-criteria donor livers after brain death (DBD) and after circulatory death (DCD) who were randomized 1:1 to HOPE after static cold storage (SCS) (n = 109) or SCS only (n = 110). Randomization was performed after liver inspection and acceptance for transplant. Data were analyzed from December 2024 to July 2025. Intervention After SCS and transport, livers underwent portal-venous HOPE until implant, using the VitaSmart HOPE System. Main Outcomes and Measures The primary efficacy end point was early allograft dysfunction (EAD). Other main outcomes included model for early allograft function (MEAF) score, hospital length of stay (LOS), patient and graft survival, and biliary complications. Additional post hoc analyses were performed. Results Overall, 219 patients (82 female [37.4%] and 137 male[62.6%]) were included (DCD, 57 of 219 [26%]) with 97% (212 of 219) completing 12-month follow-up. EAD incidence was 20.2% (22 of 109) with HOPE vs 37.3% (41 of 110) with SCS (rate difference, −17.1%; 95% CI, −27.2 to −7.0; noninferiority P &amp;lt; .001; superiority P = .005). HOPE treatment led to significantly lower MEAF mean (SD) scores (HOPE group, 4.28 [1.75]; control group, 4.82 [1.85]; mean difference, −0.54; 95% CI, −1.02 to −0.06; P = .03) and shorter median LOS (hazard ratio, 1.32; 95% CI, 1.01-1.73; P = .04). NAS on imaging and 1-year survivals were comparable between groups (patients: HOPE group, 106 of 109 [97.2%] vs SCS, 106 of 110 [96.4%]; graft: HOPE group, 104 of 109 [95.4%] vs SCS, 102 of 110 [92.7%]). Post hoc analyses showed numerically less graft loss due to NAS after HOPE (4 vs 1) and less major complications (Clavien-Dindo classification ≥IIIa) per patient, respectively. Conclusions and Relevance Back-to-base portal-venous HOPE was compared with SCS alone for extended risk DBD and DCD livers. The HOPE approach was safe and associated with reduced EAD, MEAF score, and LOS. These findings support the use of HOPE as a pragmatic strategy to improve early posttransplant outcomes. Trial Registration ClinicalTrials.gov Identifier: NCT05045794 .

Abstract Background Ultra‐high‐field 7 Tesla (7T) magnetic resonance imaging (MRI) provides improved resolution and signal‐to‐noise ratio (SNR) over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce additional challenges such as susceptibility artifacts. Purpose We propose an efficient transformer‐based model (7T‐Restormer) to synthesize 7T‐like MRI (quantitative T1 MP2RAGE maps) from routine 1.5T or 3T T1‐weighted (T1W) images. Methods The proposed method leverages an efficient restoration transformer backbone and spatial attention layers to capture long‐range dependencies and generate high‐quality 7T‐like images. Our model was validated on an institutional dataset, comprised of 35 1.5T and 108 3T T1w MRI paired with corresponding 7T T1 maps of patients with confirmed multiple sclerosis (MS). A total of 141 patient cases (32 128 slices) were randomly divided into 105 (25 1.5T and 80 3T) training cases (19 204 slices), 19 (5 1.5T and 14 3T) validation cases (3476 slices), and 17 (5 1.5T and 12 3T) test cases (3145 slices). The synthetic 7T T1 maps were evaluated by comparing their similarity to the ground truth volumes using peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE), and were compared against ResViT and ResShift. In addition, a blinded reader study was performed in which three clinicians scored the diagnostic image quality of each method on a 5‐point Likert scale (1 = non‐acceptable, 5 = excellent). Statistical significance ( α = 0.05) was assessed using two‐sided paired t ‐tests with Holm correction for multiple comparisons and Wilcoxon signed‐rank tests with Holm correction for reader scores. Effect sizes (Cohen's for paired data) were also computed to quantify practical significance. We interpreted and as small, medium, and large effects. Results For 1.5T inputs, 7T‐Restormer achieved an NMSE of 0.018 ± 0.003, PSNR of 25.0 ± 0.6 dB, and SSIM of 0.870 ± 0.019; for 3T inputs, the corresponding values were 0.019 ± 0.006, 24.5 ± 1.2 dB, and 0.874 ± 0.027. Across both field strengths, 7T‐Restormer provided the lowest NMSE and highest PSNR and SSIM among the three methods, reducing NMSE by approximately 5% and 25% relative to ResShift and ResViT at 1.5T and by 14% and 24% at 3T, respectively, and increasing PSNR by 0.5–1.5 dB (Holm‐adjusted p < 0.05, paired Cohen's = 1.37–6.84). Training on mixed 1.5T+3T data improved performance for 1.5T inputs compared with 1.5T‐only training (e.g., NMSE 0.018 vs. 0.019; p = 0.001, ), without degrading performance for 3T inputs ( p = 0.630, for NMSE). In the blinded reader study, 7T‐Restormer received higher diagnostic quality scores (3.50 ± 0.28) than ResShift (3.00 ± 0.27) and ResViT (2.33 ± 0.27), corresponding to mean improvements of 0.5–1.2 points on the 5‐point scale (Holm‐adjusted p = 0.014 and 0.004; paired Cohen's and 3.24, respectively). Conclusion We propose a novel method for predicting quantitative 7T MP2RAGE maps from 1.5T and 3T T1W scans with higher quality than existing state‐of‐the‐art methods. Future development and application of our approach may enhance diagnostic accuracy, treatment planning, and facilitate downstream tasks by making the benefits of 7T MRI more accessible to standard clinical workflows.