Citations

Background. The adoption of technical variant grafts has expanded the donor pool for pediatric liver transplantation, but it also poses a risk of portal vein thrombosis (PVT). This study evaluated risk and prognostic factors of PVT in children undergoing liver transplantation with technical variant grafts. Methods. A total of 3137 pediatric patients from 19 centers were included: 470 received split/reduced deceased donor grafts and 2667 received living donor grafts. Multivariable logistic regression was used to identify risk factors, whereas time-dependent Cox models were used to assess the impact on survival. Results. PVT occurred in 102 patients (3.3%) and was associated with increased mortality (adjusted hazard ratio, 2.86; 95% confidence interval [CI], 1.13-7.25; P = 0.027) and graft loss (adjusted hazard ratio, 9.97; 95% CI, 4.90-20.3; P < 0.001). Risk factors included younger age, lower weight, higher Pediatric End-Stage Liver Disease score, longer cold ischemia and operation times, higher graft-to-recipient weight ratio, increased red blood cell transfusions, and biliary atresia. After adjusting for center, lower weight (odds ratio, 0.84 per kg; 95% CI, 0.71-1.00; P = 0.047) and biliary atresia (odds ratio, 2.34; 95% CI, 1.10-5.00; P = 0.027) remained predictors. Five-year patient (88.4% versus 93.1%) and graft survival (82.7% versus 96.3%) were significantly inferior in patients with PVT. Conclusions. Low-weight children with biliary atresia constituted a high-risk group, highlighting the necessity for early detection and management.

The role of the epigenome in age-related neurodegenerative disorders remains understudied. Here, we analyzed circulating cell-free DNA (cfDNA) from blood to detect methylation changes as a liquid biopsy for Amyotrophic Lateral Sclerosis (ALS). Our study included 20 patients with sporadic ALS, 10 patients with C9orf72-associated ALS, 10 asymptomatic carriers of the C9orf72 repeat expansion mutation, and 21 nondisease control individuals. Following targeted enzymatic methyl-sequencing (EM-seq) of approximately 4 million CpG sites, we detected numerous differentially methylated genes, including several implicated in ALS disease risk and pathogenesis. By integrating multiple epigenetic features, we delineated a distinct epigenetic signature, which achieved an average area under the curve (AUC) of 0.91 ± 0.10 upon receiver operator characteristic (ROC) analysis, which enabled detection of approximately 70% of patients with ALS with close to 100% specificity. Furthermore, we also identified a set of genes whose methylation status significantly correlated with clinical disease progression and cerebrospinal fluid (CSF) neurofilament levels. Our results reveal the potential of cfDNA-based biomarkers to accurately diagnose ALS and potentially predict disease progression.

TPS5632 Background: Concurrent chemoradiation therapy (CCRT) is the standard of care for locally advanced cervical cancer (LACC); however, outcomes remain suboptimal in patients with high-risk disease. Recent studies suggest that integration of immunotherapy (IO) with definitive chemoradiation, or induction chemotherapy prior to CCRT improves outcomes. Whether treatment intensification with induction systemic therapy and IO prior to CCRT and IO further improves disease control remains unknown. Induction chemotherapy may reduce tumor burden, eradicate micrometastatic disease, and improve tumor immunogenicity prior to definitive CCRT. Preclinical and clinical data support synergy between systemic therapy or radiotherapy and immune checkpoint blockade, providing the rationale for evaluating an induction strategy incorporating chemotherapy and immunotherapy before standard CCRT with immunotherapy, followed by maintenance immunotherapy. Optional tumor tissue and blood samples will be collected for exploratory correlative analyses, including immune and molecular biomarkers associated with response, resistance, and patterns of failure. Methods: NRG-GY037 is a prospective, multi-institutional phase III randomized trial conducted through NRG Oncology. A total of 336 patients will be enrolled (168 per arm). Patients will be stratified by FIGO stage (III vs. IVA) and presence or absence of para-aortic lymph nodes (N2). The primary endpoint is progression free survival (PFS). The study has 90% power to detect PFS hazard ratio of 0.65 in the experimental arm. Treatment: Patients with previously untreated, high-risk LACC are randomized to receive either induction chemotherapy with pembrolizumab for 6 weeks followed by CCRT with pembrolizumab and pembrolizumab maintenance, or standard CCRT with pembrolizumab followed by pembrolizumab maintenance. Major Eligibility Criteria: Eligible patients have histologically confirmed cervical carcinoma (squamous, adenocarcinoma, or adenosquamous), TNM T3 and T4 disease, N0-2, ECOG performance status 0–1, and no prior definitive therapy for cervical cancer. Current Enrollment Status: The trial is actively accruing patients. No efficacy or safety data are available at the time of submission. Clinical Trial Registration: ClinicalTrials.gov identifier: NCT07061977. Clinical trial information: NCT07061977 .

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.

4562 Background: Approximately 50-80% of patients (pts) with la/mUC have tumors with HER2 protein expression. As HER2-directed therapies enter development, a robust understanding of HER2 prevalence and real-world outcomes with standard-of-care (SOC) therapies across HER2 expression subgroups is needed. SGNDV-003 (NCT05902494) is a global retrospective chart review study in which HER2 status was assessed via central lab testing and outcomes were analyzed by HER2 status in pts with la/mUC. Methods: SGNDV-003, conducted 6/2023-7/2025, included pts with la/mUC who received ≥1 prior treatment (tx) (incl. 1 line of platinum-based chemotherapy [chemo]). HER2 status was centrally tested using a UC-specific investigational immunohistochemistry [IHC] and in situ hybridization (ISH) assays. HER2 status was defined as: HER2-positive (IHC3+ or IHC2+/ISH+), HER2-low (IHC2+/ISH- or IHC1+), HER2-expressing (IHC3+, 2+, 1+), and HER2-zero (IHC0). Index line of therapy (LOT) was defined as the first LOT received after platinum-based chemo. Outcomes included real-world objective response rate (rwORR) (per RECIST 1.1 or physician-assessed response) to index LOT, real-world progression-free survival (rwPFS), and overall survival (OS) from start of index LOT. Data was analyzed via descriptive statistics. Results: A total of 353 pts with la/mUC and available HER2 status were analyzed (male, 76%; median age, 70 years). 70% of pts were HER2 expressing (19% HER2-positive, 51% HER2-low); 30% were HER2-zero. Disease characteristics were well balanced by HER2 status. Index LOT tx and real-world outcomes are shown in the Table. Immuno-oncology therapies (IO) and enfortumab vedotin (EV) were the most common index LOTs. For IO, rwORRs were 21% for HER2-expressing and 17% for HER2-zero. For EV mono, rwORRs were 36% (HER2-expressing) and 38% (HER2-zero), respectively. Conclusions: This global study provides a robust estimate of HER2 distribution in la/mUC based on standardized central lab testing, showing high prevalence of HER2 expression. Real-world outcomes with current SOC therapies were broadly consistent across HER2 subgroups, establishing an important benchmark for the clinical development of HER2-directed therapies in this setting. HER2-positive (n=67) HER2-low (n=181) HER2-expressing (n=248) HER2-zero (n=105) Total (n=353) Real-world Outcomes rwORR, n (%)[95% CI] 17 (25.4)[15.5, 37.5] 50 (27.6)[21.3, 34.7] 67 (27.0)[21.6, 33.0] 22 (21.0)[13.6, 30.0] 89 (25.2)[20.8, 30.1] Median rwPFS (95% CI), mo 2.8(2.0, 4.1) 3.7(2.7, 5.1) 3.0(2.7, 4.5) 2.6(2.1, 3.4) 2.9 (2.6, 3.5) Median OS(95% CI), mo 12.2(9.3, 16.5) 11.5(9.5, 13.5) 11.9(10.3, 13.4) 7.7(5.9, 9.9) 10.8(9.1, 12.3) Index LOT, n% IO EV monoChemoOther tx 37 (55)17 (25)10 (15)3 (5) 94 (52)51 (28)24 (13)14 (8) 131 (53)68 (27)34 (14)17 (7) 65 (62)21 (20)12 (11)7 (7) 196 (56)89 (25)46 (13)24 (7)

Abstract Background Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL‐based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy. Purpose To design a physics‐aware and generalizable AI‐based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases. Methods This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo‐based dose engine as the reference standard, we compared three methods: the region of interest (ROI)‐based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low‐statistics dose via uniformly weighted spots on an expanded spot‐placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cases with uncommon beam angles or prescription doses were used to assess their performance, which was evaluated using dose‐volume histogram (DVH) indices, 3D Gamma passing rates (3%/2 mm/10%), and Dice coefficients for dose agreement, while prediction times were measured to gauge model efficiency. Results The proposed noisy probing dose method consistently outperformed the ROI‐based and beam mask baselines across all evaluated metrics, with more accurate dose agreement and superior generalizability. For DVH indices, the noisy probing dose method achieved the smallest deviation in clinical target volume (CTV) dose coverage: in prostate cancer, CTV D98 deviation was reduced by 45% (from 0.53 ± 0.22 Gy [RBE] for ROI‐based) and 29% (from 0.41 ± 0.28 Gy [RBE] for beam mask) to 0.29 ± 0.06 Gy [RBE]; in lung cancer, similar improvements were observed, with CTV D98 deviations reduced to 0.34 ± 0.12 Gy [RBE]. The 3D Gamma passing rates improved to 99.65% ± 1.15% for prostate targets and 97.04% ± 1.17% for lung targets. The dice coefficients of the 90% iso‐dose lines were also the highest with the noisy probing dose method (prostate: 0.983 ± 0.005; lung: 0.967 ± 0.01). For the 12 outlier cases, the noisy probing dose method maintained superior generalizability, yielding higher 3D Gamma passing rates (prostate targets: 96.79% ± 0.83%, OARs: 94.29% ± 1.01%; lung targets: 93.38% ± 1.34%, OARs: 93.95% ± 1.32%), demonstrating robust generalizability to rare clinical scenarios. The dose predictions for all testing cases were completed within 0.3 seconds. Conclusions A novel noisy probing dose method was proposed for PBSPT dose prediction in prostate and lung cancer patients. By embedding more proton‐specific physics, this method demonstrated an improvement in the generalizability of dose prediction.

Emerging evidence suggests that systemic physiological factors may influence plasma biomarker concentrations of Alzheimer’s disease (AD) and related neurodegenerative processes, potentially affecting their specificity for central nervous system pathology. This study examined the relationship of demographic factors and medical comorbidities with plasma biomarkers of AD and neurodegeneration in a community-dwelling cohort of Black/African American (B/AA) older adults (N = 141). Participants underwent plasma assessment of phosphorylated tau at threonine 217 (p-Tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Results showed associations between plasma p-Tau217 and amyloid PET positivity, and significant intercorrelations among p-Tau217, GFAP, and NfL. Stepwise regression models incorporated demographics, amyloid PET status, and laboratory measures of renal, metabolic, and lipid function as predictors for each biomarker. p-Tau217 was primarily predicted by amyloid PET and renal function; GFAP by age and sex; and NfL by renal function, age, and sex. Findings indicate plasma biomarker concentrations in B/AA older adults reflect both central AD-related pathology and systemic physiological factors, particularly renal function, and demographic influences. Results underscore the importance of accounting for comorbid medical conditions and demographic characteristics when interpreting blood-based biomarkers and highlight the need for comprehensive medical phenotyping to improve diagnostic specificity and clinical utility.

As an emerging tire wear-derived environmental contaminant, 6PPD-quinone (6PPDQ) has raised significant concerns regarding its neurotoxic potential, particularly for children exposed to recycled tire crumb rubber in playgrounds. However, the molecular mechanisms by which 6PPDQ influences neurological disorders such as epilepsy remain poorly understood. In this study, we employed an integrative framework combining network toxicology, bulk analysis of human epileptic brain tissues, Mendelian randomization, and molecular dynamics simulations to elucidate these mechanisms. Our findings, validated through CETSA-WB and SPR, identify 6PPDQ as a direct ligand that binds to and stabilizes neuronal TP53. Through a synergistic double-hit mechanism, 6PPDQ directly engages the TP53 pathway while simultaneously triggering microglial interleukin-6 secretion. These converging pathways lead to the suppression of the master antioxidant regulator Nrf2, resulting in glutathione depletion, excessive reactive oxygen species accumulation, and exacerbated neuronal damage under excitotoxic stress. Experimental validation using glutamate-induced HT22 cell models and microglia–neuron crosstalk systems confirmed that targeting the TP53/Nrf2 axis or scavenging ROS significantly attenuates 6PPDQ-induced neurotoxicity. Our findings highlight critical risks to pediatric neurological health posed by tire-derived contaminants and identify the TP53/Nrf2 axis as a promising therapeutic target. Furthermore, this work provides a robust scientific basis for refining risk assessment frameworks and developing regulatory strategies to mitigate environmental exposure to 6PPDQ.