Citations

Abstract Background Little research exists regarding geographic comparisons within the United States for Medicaid reimbursement. We compared Medicaid reimbursement for a broad range of orthopedic surgery codes from each state on a geographic and state level. Methods We obtained Medicaid reimbursement rates in each state for 211 orthopedic procedure codes along with corresponding work Relative Value Units (wRVUs). Rates were divided by wRVUs to control for differences between procedures and subspecialties. States were designated as either West, Midwest, South, or Northeast. Kruskal-Wallis tests with Dunn’s post-hoc analyses ascertained differences between regions and each orthopedic subspecialty by state. Coefficient of variation (CoV) values were calculated to measure the amount of variability in rates. A Kruskall-Wallis with Dunn’s post-hoc test assessed differences in quartiles for reimbursement compared to a square miles per orthopedic surgeon ratio. Results The West region reimbursed the highest at $72.92/wRVU and the Northeast reimbursed the lowest at $51.78. Hand surgery was the highest reimbursing subspecialty ($94.58/wRVU) and Oncology the lowest ($62.89/wRVU). The CoV ranged from 0.15–0.77 by state and 0.27–0.50 by subspecialty. A linear regression model between wRVU-adjusted Medicaid reimbursement and square miles per surgeon yielded an R2 value of 0.453. States with the most land area relative to orthopedic surgeons reimbursed significantly higher. Conclusion The West geographic region is associated with the highest Medicaid reimbursement for orthopedic procedures, and the Northeast the lowest. Wide variability was seen in rates, both between states and within states. Large, sparsely-populated states may increase Medicaid reimbursement as a tactic to attract physicians. Level of Evidence: IV; Economic Analysis

Abstract Inflammatory bowel diseases (IBD), principally Crohn’s disease (CD) and ulcerative colitis (UC), are common chronic disorders involving inflammation and often progressive tissue damage. Genome-wide association studies have mapped many risk signals, but the causal variants, effector genes and relevant cellular contexts remain difficult to resolve, limiting mechanistic interpretation and therapeutic translation. Here we performed a multi-ancestry GWAS meta-analysis of 125,992 individuals with IBD and more than 1.2 million controls, identifying 619 independent association signals (374 novel) at 420 IBD regions that account for 77–80% of SNP-based heritability. Fine-mapping resolved 81 high-confidence variants, 41 not previously reported. Although most signals were shared between CD and UC, 39% showed IBD subtype specificity, with UC signals showing stronger enrichment in functional annotations from intestinal epithelial, secretory and enteroendocrine cells, and CD showing stronger genetic correlations with circulating inflammatory biomarkers, including C-reactive protein and glycoprotein acetylation. Latent causal modelling supported a causal effect of decreased high-density lipoprotein on CD risk. By integrating bulk and single-cell eQTL and pQTL resources using colocalisation and Mendelian randomisation, together with coding-variant evidence from exome sequencing, we prioritised 664 candidate effector genes across 341 signals, including 390 newly implicated IBD genes, revealing new biological mechanisms and candidate therapeutic targets supported by human genetics.

Abstract A population of pathologic fibroblasts has been identified in several human fibrotic lung diseases, including IPF. These cells, marked by high expression of collagen triple-helix repeat containing 1 (CTHRC1), display an invasive phenotype and aggressively deposit extracellular matrix. The goal of this study was to identify transcriptional regulators of the CTHRC1+ pathologic fibroblast state. We analyzed published single-cell RNA sequencing data of IPF and control lungs to predict transcription factor activity in CTHRC1+ fibroblasts, which led us to hypothesize that transcription factors in the nuclear factor of activated T-cells (NFAT) family may contribute to the pathologic fibroblast state. To test whether NFAT family members promote and/or maintain pathologic fibroblasts, we silenced each cytosol-localized NFAT (NFATc1-4) in lung fibroblasts from IPF donors (passage 3-6, N = 5 donors) and measured expression of key NFAT target genes related to fibrosis using qPCR. NFATC1 silencing significantly reduced expression of CTHRC1, SERPINE1, FN1, HAS2, and TNC, whereas other computationally predicted regulators showed little effect. Our computational analysis also predicted high activator protein 1 (AP-1) activity in pathologic fibroblasts, which interestingly has been found to act in concert with NFATs to promote an effector response in T-cells. Thus, we hypothesized that NFAT:AP-1 signaling may be involved in lung fibroblast activation. To test this hypothesis, we employed a small molecule inhibitor of NFAT:AP-1 complex formation on DNA (“Compound 10”, 25-50 µM). NFAT:AP-1 inhibition significantly reduced expression of fibrillar collagens COL1A1 and COL3A1, as well as CTHRC1 in primary IPF lung fibroblasts. Additionally, NFAT:AP-1 inhibition prevented TGF-β-mediated increase of several fibrosis-related NFATc1 targets. Aside from CTHRC1, NFAT:AP-1 inhibition led to decrease in a different set of NFATc1 targets than NFATc1 silencing, suggesting a potentially complex relationship between NFATc1 and AP-1 in fibroblast gene regulation. Finally, in mouse precision cut lung slices (PCLS) stimulated with a fibrosis-inducing cocktail ex vivo, NFAT:AP-1 inhibition prevented increased expression of Col1a1 and Cthrc1. Taken together, these studies suggest that NFATc1, together with AP-1, is involved in fibrogenic activation of pathological fibroblasts. Ongoing and future experiments will determine the roles of these signaling mechanisms in matrix deposition and contraction, as well as fibroblast invasion using human fibroblasts and PCLS models. Furthermore, we aim to determine how mechanical forces and soluble factors encountered in IPF lungs promote NFAT signaling in fibroblasts. This abstract is funded by: NHLBI, Pulmonary Fibrosis Foundation

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p < 0.001) and HR = 2.51 (p < 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p < 0.001) and HR = 1.87 (p < 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p < 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4003.

Background: Diabetic gastroparesis (DG) is associated with a loss in anti-inflammatory muscularis macrophages (MMs), contributing to delayed gastric emptying (DGE). Lyve1 macrophages, known for their anti-inflammatory phenotype, are involved in lipid metabolism across organs. While diabetes is typically associated with increased lipid levels, the dependence of lipid handling on altered function of Lyve1+ MMs in the stomach remains unknown. Aim: To investigate lipid accumulation in MMs and test the effect of palmitate and CD36 on Lyve1+ macrophages. Methods: FACS sorted CD45+ cells were isolated from the gastric muscularis of non-diabetic mice and analyzed using 10X Genomics Cellranger and Seurat package. Gene Set Enrichment Analysis was performed to identify enriched pathways. Separately, the cells underwent flow cytometry to identify Lyve1+/- MMs then determining lipid accumulation by LipidTox intensity. RAW 264.7 macrophage cells were cultured with macrophage colony stimulating factor (M-CSF), IL-4, and dexamethasone for 7 days to induce Lyve1 expression. RNA was collected from cells treated with palmitate and a CD36 inhibitor, Sulfosuccinimidyl oleate (SSO), for RT-qPCR. Lyve1+ and Lyve1- macrophages treated with 300µM palmitate overnight were collected for lipidomic analysis. Untargeted lipidomic analysis was done with Thermo Orbitrap Exploris 120, and data were analyzed using principal component analysis for group clustering and unpaired t-tests for differential analysis. Results: Unbiased clustering analysis revealed 6 transcriptionally distinct gastric MM populations, one of which was notably enriched in Lyve1 expression. Pathway analysis identified that Lyve1 enriched cluster was associated with lysosomal pathways and receptors involved in lipid uptake, such as CD36, as well as anti-inflammatory markers such as HO1 and CD206. Flow cytometry quantification showed that Lyve1+ MMs had higher accumulation of lipids compared to Lyve1- MMs (10.1±0.7 vs 2.9±0.2 mean fluorescence intensity/cell count, p=0.0002). RT-qPCR validated that Lyve1+ RAW 264.7 macrophages exhibited significantly higher expression of Lyve1 (6.95 times fold change, p=0.023) and CD36 (3.27 times fold change, p=0.011) compared to Lyve1- macrophages. Post palmitate treatment, Lyve1+ macrophages had increased expression of the pro-apoptotic gene caspase-3 (12.15-times fold change, p=0.033), as well as the fatty acid oxidation marker ACADM (4.98 times fold change, p=0.045). Notably, these differences were not found in the presence of SSO. Lipidomic analysis identified 171 differentially expressed lipids, particularly increased levels of ceramides and diacylglycerols in Lyve1+ macrophages compared to Lyve1- macrophages when both are treated with palmitate. Conclusions: Lyve1+ macrophages preferentially uptake lipids compared to Lyve1- macrophages consistent with increased expression of CD36. Lipid treatment of Lyve1+ macrophages increases expression of apoptosis and oxidation markers in a CD36-dependent manner that may lead to cell loss. These mechanisms may contribute to Lyve1+ MMs loss in DG. Funding: DK127992; DK129297; DK115255; ANMS Young Investigator grant; AGA grant#36; P30DK084567; Robert and Arlene Kogod Center on Aging Fundamental Mechanisms of Aging Award. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Abstract Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation 1,2 . Here we analysed whole-exome and whole-genome sequencing data from 86,213 IBD cases and 478,363 controls of European ancestry. We identified 68 IBD genes directly implicated by conditionally independent protein-coding associations across the allele frequency spectrum. Many newly implicated IBD genes are supported by orthogonal genomic or pleiotropic evidence, pointing to disease-related pathways and nominating targets with therapeutic relevance. We further identified allelic series and non-additive effects at key loci such as NOD2 and TYK2. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.

Background The optimal management of biochemical recurrence (BCR) after radical prostatectomy (RP) remains controversial, particularly in the absence of clinical or radiographic evidence of disease. Salvage radiotherapy (SRT) remains standard of care, but it is unclear whether SRT benefits patients when modern imaging with a multiparametric magnetic resonance imaging (mpMRI) and prostate‐specific membrane antigen‐positron emission tomography/computed tomography (PSMA‐PET/CT) are negative for local recurrence. Retrospective data comparing SRT to observation for such patients is fraught with selection bias, underscoring the need for a prospective randomised controlled trial. Study Design and Methods The Biochemical Recurrence Irradiation or Observation (BRIO) is a randomised study under the DIVINE trial framework (NCT06378866) enrolling men with BCR (prostate‐specific antigen [PSA] level ≥0.2, up to 1.5 ng/mL) after RP with no radiographic evidence of recurrence on mpMRI and PSMA‐PET/CT. Major inclusion criteria include age ≥18 years, biochemically recurrent prostate cancer after RP with a PSA level between 0.2 and 1.5 ng/mL, and no local or metastatic recurrence on imaging. A total of 312 patients will be enrolled. Participants are randomised 1:1 to standard‐of‐care SRT followed by surveillance (Group A), or initial observation with subsequent image‐guided therapy at radiographic progression (Group B). Based on historical analysis, a total of 30 metastasis events provides 80% power to detect a significant improvement (hazard ratio 0.5 corresponding to a 5‐year metastasis‐free proportion of 80.7% in the control group and 89.8% in the experimental group with one‐sided type I error rate of 0.15). All participants undergo baseline blood collection to measure prostate cancer‐extracellular vesicles and cell‐free DNA for biomarker development. Participants are followed in a 16‐week cycle with serial PSA measurements, repeat imaging if PSA doubles, safety assessment, and patient‐reported outcomes. Endpoints The primary endpoint is distant recurrence‐free survival at 5‐years using a 12‐month landmark to mitigate bias arising from differing assessment schedules for patients receiving optional 6 months of androgen deprivation therapy. Key secondary outcomes include time to hormone therapy, quality of life measures, adverse effects, and novel blood and urine biomarkers. Conclusion The BRIO study will clarify the clinical benefit of immediate SRT vs observation with image‐guided intervention in mpMRI and PET‐negative BCR after RP.

Background/Objectives: Primary spinal sarcomas, encompassing both bone and soft tissue histotypes, demand individualized reconstruction due to heterogeneous tumor biology, anatomic complexity, and host environments compromised by radiation, systemic therapy, or prior surgery. This narrative review reframes post-resection spinal reconstruction through a precision-medicine lens. Methods: A structured literature review was performed using PubMed and Scopus, targeting articles published between 2000 and 2026. Searches encompassed spinal sarcoma reconstruction, radiation and fusion, biologic reconstruction, and emerging technologies. Results: Tumor grade, radiation exposure, and systemic therapy timing emerge as multiplicative determinants of reconstructive environment quality, with drug-class-specific perioperative effects warranting stratified management. Vascularized bone grafts achieve reliable fusion in compromised hosts where avascular constructs fail. A precision-oriented reconstructive ladder is proposed as a conceptual, hypothesis-generating framework to guide strategy selection. Hybrid PSI-VBG constructs may further expand reconstructive possibilities. The evidence base remains largely composed of small, retrospective series. Conclusions: Individualized strategies anchored in tumor biology and host environment are the cornerstone of durable spinal sarcoma reconstructions. The proposed framework requires prospective, multi-institutional validation. Standardized outcome definitions, prospective registries, and histotype-stratified analyses are needed to advance the field.