Citations

The success of living donor liver transplantation (LDLT) has bridged the growing gap between organ demand and availability in patients with liver failure or liver cancer. Safety of the living liver donor (LLD) is paramount, and astute intraoperative management plays a critical role in ensuring optimal outcomes for them. Given that intraoperative surgical and anesthesia practices vary widely across centers, the International Liver Transplantation Society (ILTS) and the International Living Donor Liver Transplantation Group (iLDLT Group) convened a global consensus conference in Toronto in March 2025 to develop guidelines for intraoperative management of the LLD. This manuscript details the recommendations on intraoperative considerations for the safety of LLD developed by Working Group II of the Consensus Scientific Committee, using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus. Working Group II focused on intraoperative monitoring, fluid and ventilation strategies, surgical techniques for vascular and biliary division, surgeon and center-specific experience, and intraoperative crises planning. These guidelines aim to support both established and emerging LDLT teams to ensure high-quality and safe intraoperative care for the LLD.

Background/Objectives: Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. Methods: Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. Results: When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R2 = 0.56, p = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R2 = 0.56, p = 0.01). Conclusions: Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies.

Abstract Background More than 85% of Alzheimer's disease (AD) donor brains exhibit some degree of cerebral amyloid angiopathy (CAA), which is characterized predominantly by Aβ40 deposits in the brain vasculature and can cause blood brain barrier (BBB) leakage. CAA is associated with risk of infarcts, cerebral hemorrhages, and cognitive decline. Brain cell type‐specific mechanisms that influence CAA pathology, as well as related measures such as Aβ40 biochemical measures and tight junction proteins, an indicator of BBB integrity, are still elusive. Method We performed single‐nucleus RNA sequencing (snRNAseq) of temporal cortex tissue from 79 AD donors, with varying levels of CAA co‐pathology. Measurements of soluble, insoluble and membrane‐associated fractions of brain Aβ40 and tight junction protein (claudin‐5, occludin) levels were available for these donors. We correlated these measures with cell proportions. We also conducted differential gene expression and analyses for intercellular communications and regulatory networks. Result We identified 25 clusters and annotated them with major brain cell types. There were 3 oligodendrocyte, 7 excitatory and 7 inhibitory neuronal, 2 microglial, 2 astrocytic, and 1 each for endothelial, pericytic, fibroblast, and oligodendrocyte precursor cell clusters. We found that higher CAA and Aβ40 levels correlate with reduced neuronal and increased microglial, astrocytic and vascular cell proportions. Increased tight junction proteins correlate with increased inhibitory neuronal and reduced microglial, astrocytic and vascular cell proportions. Among all clusters, one microglial, both astrocytic and the pericytic clusters have greater number of genes significantly associated with CAA, Aβ40 and tight junction protein levels. Most significant genes have negative associations with CAA and membrane‐bound Aβ40 and positive with tight junction protein measures. Using these genes as input for intercellular communications analysis, we prioritized ligand‐target interactions from microglia or astrocytes to pericytes. Some of the prioritized targets are transcription factors in pericytes. Using regulon analysis, we identified genes downstream of these transcription factors that are also significantly associated with lower CAA and membrane‐bound Aβ40 and higher tight junction protein levels. Conclusion We revealed glia‐to‐pericytes intercellular interactions and pericytic gene regulatory networks that are potentially perturbed by CAA and BBB breakdown. Future directions include analyzing external snRNAseq datasets and performing functional validations.

Abstract Pancreatic cancer, projected to become the second-deadliest malignancy in Western societies by 2030, requires urgent innovations in early detection and risk stratification. Intraductal papillary mucinous neoplasms (IPMNs) represent critical precursor lesions, but current clinical guidelines demonstrate suboptimal accuracy in malignancy prediction, leading to either unnecessary surgeries or missed opportunities for early intervention. In this paper, we present Cyst-X, an artificial intelligence (AI) framework that accurately predicts IPMN malignant transformation using multicenter magnetic resonance imaging (MRI) data. Unlike most previous approaches that rely on computed tomography (CT), our method capitalizes on the superior soft tissue contrast of MRI, allowing for more precise identification of subtle imaging biomarkers. We developed and validated deep learning models on 723 T1-weighted and 738 T2-weighted MRI scans from 764 patients among seven international institutions, demonstrating significantly superior performance (AUC=0.82) compared to current clinical (Kyoto) guidelines (AUC=0.75) and expert radiologists. AI-based imaging features correlate strongly with clinically recognized malignancy markers, providing potential biologically relevant insights. This approach holds promise to significantly refine clinical decision-making, reduce unnecessary surgeries, and better identify high-risk IPMN patients for timely intervention. Our approach integrates a novel pancreas segmentation network with robust classification models that identify subtle imaging biomarkers associated with malignancy risk. Importantly, we demonstrate that these models retain high performance in a privacy-preserving federated learning setting, where institutions collaboratively train AI models without exchanging patient data to address key regulatory and ethical barriers. We publicly release the Cyst-X dataset--the first large-scale, multi-center pancreatic cyst MRI collection--to accelerate research in this field. This study addresses a critical clinical need while establishing technical foundations for privacy-preserving AI in radiology that could transform pancreatic cancer management through earlier intervention and reduced unnecessary procedures. The dataset can be accessed at https://osf.io/74vfs/, and the source code for our deep learning segmentation and classification models is available at https://github.com/NUBagciLab/Cyst-X.

Abstract Background: The spatial organization of the tumor microenvironment (TME) encodes the selective pressures that drive cancer evolution, but many tools focus on local cell contacts or single-modality readouts. We developed Tumor Landscape Analysis (TLA), a modular framework that borrows from landscape ecology and spatial statistics to quantify heterogeneity, fragmentation, and diversity across scales, from single cells to whole-slide ecosystems. Methods: TLA ingests whole-cell or nuclear centroids and region-level segmentations to compute (i) point statistics such as nearest-neighbor distance, Ripley’s H, and Morisita-Horn colocalization; (ii) kernel-smoothed local spatial profiles including Getis-Ord Gi* hot and cold spots; and (iii) fragmentation, shape, and diversity metrics such as patch density, contagion, shape index, and Shannon entropy. We also introduce Local Microenvironments (LMEs), unsupervised ecological niches defined by joint cell-type abundance and spatial mixing. Pilot analyses were performed in ductal carcinoma in situ (DCIS; n=353 samples, 175 patients) with cell-level maps of epithelial, lymphocyte, and fibroblast populations, and in pancreatic ductal adenocarcinoma (PDAC; n=203 resected cases after neoadjuvant therapy) using cancer and stroma segmentations. Results: In DCIS, spatial relationships rather than raw abundance associated with recurrence risk. Shorter lymphocyte-to-fibroblast minimum nearest-neighbor distance and lower Ripley’s H, indicating greater intermixing, correlated with earlier recurrence independent of cell counts. This highlights immune–stromal proximity as a candidate eco-evolutionary driver of persistence. In PDAC, tissue-level fragmentation captured prognostic architecture after therapy. Two shape-based signatures emerged: (1) stromal uniformity, where lower standard deviation of stromal patch shape index associated with improved disease-free survival, consistent with a more constrained, less reactive stroma; and (2) tumor geometric complexity, where higher mean cancer patch shape index, reflecting more irregular or dendritic nests, associated with lower recurrence risk, whereas compact nests predicted worse outcomes. Across datasets, LME maps localized niche phenotypes, such as mixed-immune or segmented tumor habitats, that align with hypothesized selection landscapes and provide inputs for fragmentation analysis. Conclusions: TLA operationalizes an eco-evolutionary view of cancer by unifying interpretable, multiscale spatial metrics in a single workflow that works with any rasterized data input. By quantifying how habitats, edges, and mixing patterns are organized, TLA reveals niche structures and architectural motifs linked to clinical outcomes and complements foundation models and modality-specific pipelines with theory-grounded, clinically translatable descriptors. Ongoing work integrates TLA features with molecular data and outcomes to derive robust biomarkers and to inform adaptive, niche-targeted strategies in precision oncology. Citation Format: Ryan M. Carr, Luis Cisneros, Merih Deniz. Toruner, Martin E. Fernandez-Zapico, Carlo Maley. Tumor landscape analysis: An ecologically informed framework to understand tumor microenvironments [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr A015.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .

AbstractIntroductionThe immune system's inflammatory response, driven by pro-inflammatory proteins, protects against external threats. Fear and disgust-inducing stimuli have been linked to immune responses, yet their specific physiological and inflammatory mechanisms in trypophobia remain unclear. This study aimed to elucidate the inflammatory and physiological responses in relation to natural (non-skin) and skin-related trypophobic images.Material and methodsFifty participants (n = 50) were recruited for the study, and their sensitivity to trypophobia was measured using the trypophobia questionnaire. Then, participants were randomly assigned to either the skin related or non-skin related visual exposure group and viewed trypophobic images from a computer screen. Blood samples were collected pre- and post-exposure to trypophobic images and analyzed for IL-6 and TNF-α using ELISA and RT-qPCR methods.ResultsIL-6 and TNF-α protein levels significantly increased post-exposure, with IL-6 changes varying by stimulus type. mRNA expression showed significant interaction with participants' trypophobia sensitivity scores, suggesting post-transcriptional mechanisms. Heart rate variability (HRV) and heart rate were measured before, during, and after exposure using photoplethysmography. Significant changes in HRV metrics, influenced by stimulus type and trypophobia sensitivity, indicated increased sympathetic and decreased parasympathetic nervous system activity during and after exposure.ConclusionsThese findings highlight the role of physiological and inflammatory responses in trypophobia, suggesting immune activation and autonomic nervous system involvement based on stimulus type and individual sensitivity. These findings not only contribute to phobia literature but also shed light on the physiological and immunological changes that take place in the bodies of individuals with high sensitivity to trypophobia.

Abstract Introduction Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers. While total neoadjuvant therapy (TNT) has improved outcomes, recurrence rates remain high. Major pathologic response after TNT is prognostic of better survival, but is rarely achieved. Current pathologic systems primarily consider residual tumor percentage, nodal status, and perineural invasion but overlook spatial organization within the tumor microenvironment (TME). We aimed to evaluate spatial features of the TME to identify biologically and clinically relevant patterns of TME conformation and configuration after TNT and surgical resection of PDAC. Methods We retrospectively analyzed patients with PDAC treated with TNT followed by surgery at Mayo Clinic (2010-2022) with partial or no response per College of American Pathologists (CAP) criteria. Hematoxylin and eosin-stained whole slide images (WSIs) were processed using PathExplore™ PDAC (v1.0) to quantify spatial TME features. These were correlated with disease-free survival (DFS). Results A total of 203 patients were included: 47.3% female, median age 66.5 years. All received TNT; 73.4% had neoadjuvant chemotherapy followed by chemoradiation. Surgical procedures included pancreaticoduodenectomy (62.1%), distal pancreatectomy (22.2%), and total pancreatectomy (15.8%). CAP-defined response was partial in 82.1% and absent in 17.9%. As expected, pathologic T stage (p=0.001), N stage (p=0.002), lymphovascular invasion (p=0.048), and R2 resection (p=0.007) were significantly associated with DFS. However, CAP response category (p=0.053), residual cancer area (p=0.47), and cancer-to-stroma ratio (p=0.25) were not. We analyzed 297 WSIs from 203 patients. Of 31 spatial features evaluated, 5 were associated with DFS (p<0.20). Recursive partitioning using ln(mean cancer shape index [CSI]) <1.775 and ln(SD of stroma shape index [SSI]) >0.651 identified a high-risk group (n=63) with median DFS of 7.23 months (95% CI: 5.19–9.63), compared to 11.57 months in the low-risk group (95% CI: 9.24–17.92) (HR=1.82; 95% CI: 1.31–2.52; p=0.0003). This remained significant after adjusting for T and N stage (HR=1.75; 95% CI: 1.17–2.63; p=0.007). An alternate prognostic index derived via backwards elimination, calculated using 0.2701(mean stromal area) + 0.3905(edge density), also stratified patients. High-risk patients (n=51) had a significantly shorter (p<0.001) DFS (6.2 months) than those in the low-risk category (20.0 months). This significant difference holds in a multivariable analysis controlling for pathologic stage, N stage, and resection margins (HR=1.94, 95% CI: 1.21-3.10, p=0.006). Discussion Current assessment of residual PDAC after TNT is limited by subjective grading and lack of spatial context. Using Tumor Landscape Analysis, we identified high-risk spatial features of the TME that independently predict recurrence. These findings support a novel spatially-informed framework for risk stratification in resected PDAC with residual disease. Citation Format: Miranda Lin, Luis Cisneros, Merih D. Toruner, Andrea Maraone, Paul Dizona, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Spatial features of the tumor microenvironment predict recurrence risk in resected pancreatic ductal adenocarcinoma after total neoadjuvant therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr A051.

For over a century, scientists reported the disruption of normal nuclear shape and size in cancer. These changes have long been used as tools for diagnosis and staging of malignancies. However, to date, the mechanisms underlying these aberrant nuclear phenotypes and their biological significance remain poorly understood. Using a model of pancreatic ductal adenocarcinoma (PDAC), the major histological subtypes of pancreatic cancer, we found that oncogenic mutant KRAS reduces nuclear size. Transcriptomic and protein expression analysis of mutant KRAS–expressing PDAC cells revealed differential levels of several nuclear envelope–associated genes. Further analysis demonstrated the nuclear lamina protein, Emerin (EMD), acted downstream of KRAS to mediate nuclear size reduction in PDAC. Analysis of human PDAC samples showed that increased EMD expression associates with reduced nuclear size. Finally, in vivo genetic depletion of EMD in a mutant KRAS–driven PDAC model resulted in increased nuclear size and a reduced incidence of poorly differentiated PDAC. Thus, our data provide evidence of a potentially novel mechanism underlying nuclear size regulation and its effect in PDAC carcinogenesis.

e14626 Background: Despite advancements in neoadjuvant therapy and surgical techniques, patients with pancreatic ductal adenocarcinoma (PDAC) often experience high recurrence rates after total neoadjuvant therapy (TNT) and resection. Existing tumor response scoring systems, such as the College of American Pathologists (CAP) guidelines, fail to stratify recurrence risk effectively, emphasizing the need for novel, objective approaches. Methods: This study evaluated spatial characteristics of residual cancer and stroma in a multi-site cohort of 203 patients with resected PDAC following TNT, not having achieved a major pathologic response (CAP 2 or 3). Whole slide images (WSI) of hematoxylin and eosin-stained sections were digitized and analyzed using artificial intelligence to perform tissue segmentation. Metrics quantifying spatial morphology and configuration, inspired by principles of landscape ecology, were computed to explore spatial features of the tumor microenvironment (TME). Statistical models (recursive partitioning and regression tree [RPRT] model and Cox model with backwards eliminations) incorporating these features were developed to stratify recurrence risk. Results: Five metrics were used for risk classification model build. Three models successfully classified patients into high- and low-risk groups based on spatial metrics. Models demonstrated moderate discriminatory power (C-statistics: 0.560–0.566) and showed that increased tumor fragmentation, like stromal patch density and cancer patch shape index, were strongly associated with improved outcomes. Conclusions: Integrating spatial metrics of cancer-stroma configuration enhances risk stratification beyond conventional scoring systems. Given increased tumor fragmentation is associated with improved outcomes, these data indirectly suggest ecological mechanisms of treatment resistance. This approach offers a pathway toward personalized post-operative management strategies to improve outcomes in PDAC. Risk classification models. Model Description Event/n Median DFS (95% CI) HR(95% CI) p-value C-statistics 1(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 87 / 101 8.22(5.72 – 10.1) 1.64(1.20 – 2.25) 0.002 0.562 Low risk: log standard deviation of the stroma shape index ≤ 0.651 71 / 102 11.90(9.24 – 25.1) Ref 2(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 & mean cancer shape index ≤ 1.775 58 / 63 7.23(5.19 – 9.63) 1.82(1.31 – 2.52) 0.0003 0.560 Low risk: (log standard deviation of the stroma shape index > 0.651 & mean cancer shape index > 1.775) or log standard deviation of the stroma shape index ≤ 0.651 100 / 140 11.57(9.24 – 17.92) Ref 3(Backwards elimination) High risk: PI * > median 86 / 101 8.02(6.08 – 9.86) 1.73(1.26 – 2.37) 0.0007 0.566 Low risk:PI * ≤ median 72 / 102 15.72(9.97 – 25.05) Ref *PI: (0.2701 * log mean stroma area) + (0.3905 * log edge density).