Citations

ABSTRACT Introduction Changes in peripheral eosinophils follow endoscopic sinus surgery (ESS) in chronic rhinosinusitis with nasal polyps (CRSwNP), with and without aspirin sensitivity. Cytometry by time‐of‐flight (CyTOF) identifies more immune cell subsets than conventional assays or clinical testing. We hypothesized that ESS‐associated changes in peripheral immune cell subsets differ between aspirin‐tolerant (AT)‐CRSwNP and aspirin‐exacerbated respiratory disease (AERD) as measured by CyTOF. Methods Pre‐ and post‐ESS peripheral blood from patients with CRSwNP ( n = 13) or with AERD ( n = 15) was analyzed for immune cell populations using a 37‐marker CyTOF panel. CITRUS analysis identified cell clusters most predictive of patient group or sample timepoint. Results In the combined cohort of 28 patients, circulating CD4 + naïve T cells and CD194 + neutrophils increased after ESS, while eosinophils decreased. When stratified by disease, CD4 + naïve T cells increased in both AERD and CRSwNP post‐ESS; however, eosinophils decreased after ESS only in CRSwNP. Neutrophil subsets showed a mixed response in both patient groups after ESS. Dendritic cells increased in CRSwNP but not in AERD after ESS. Individual timepoint analyses showed more CD141 + monocyte‐derived DCs (moDCs) in CRSwNP than in AERD pre‐ and post‐ESS. Pre‐ESS, both CD185 + and CD194 + neutrophils were higher in AERD compared to CRSwNP. Conclusion CD141 + moDCs were more prevalent in CRSwNP versus AERD, both before and after ESS. Naïve CD4 + T cells increased, and neutrophil subsets changed in all groups after ESS, but eosinophils decreased only in CRSwNP. Our study points to possible systemic immune effects of ESS and candidate immune signatures in CRSwNP and AERD.

Abstract Background and Objectives Cryoprecipitated antihemophilic factor (cryoprecipitate), fibrinogen concentrate (FC), and pathogen‐reduced cryoprecipitated fibrinogen complex (PR cryoprecipitate) are sources of fibrinogen replacement, and each has advantages and disadvantages in terms of cost, efficacy, safety, and logistics of administration. This study examines the respective utilization frequencies of these different products at institutions in North America and the factors influencing the decision on which one(s) to adopt. Materials and Methods A survey was distributed via email to contacts for Advancement of Blood and Biotherapies (AABB)‐accredited blood banks/transfusion services. Survey questions covered topics including the availability and usage of FC, cryoprecipitate, and PR cryoprecipitate, as well as the perceived advantages of each product. Results A total of 53 responses were received from surveyed institutions (estimated 25% response rate). Over half (31/53, 58.5%) of respondents exclusively used cryoprecipitate, 30.2% (16/53) used two sources of fibrinogen replacement, and 7.5% (4/53) used all three (cryoprecipitate, FC, and PR cryoprecipitate). More than half of the institutions restricted FC to specific indications, primarily for cost containment. The most commonly cited advantages of FC over cryoprecipitate were storage convenience and rapid reconstitution. Conclusion Surveyed institutions in the United States and Canada primarily use cryoprecipitate for fibrinogen replacement, with variable usage of FC and limited usage of PR cryoprecipitate. The perception of FC being more expensive than cryoprecipitate predominated. Further studies comparing available fibrinogen replacement options would be valuable, particularly with consideration of product acquisition cost, wastage, impacts on utilization of other blood products, and patient outcomes.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

Background Traumatic injury is the fourth leading cause of death nationally; rural residents are twice as likely to die from injury as urban counterparts. On-scene mortality coverage gaps across regional trauma systems remain poorly characterized. Objective To identify geographic blind spots and time-critical, spatially advantageous on-scene deaths (2019-2023) that fall outside modeled 10-minute drive-time service areas across seven North Central state trauma regions. Methods This retrospective analysis geocoded death certificate records to ZIP Code Tabulation Areas and modeled 10-minute drive-time service areas in ArcGIS Online. EMS agency headquarters and staffing classification (volunteer, mixed, paid) were obtained from the DHS EMS Service Map. Candidate sites were identified by maximizing coverage of weighted, uncovered death centroids per region. Results Of 11,868 decedents (60.7% male; mean age 64.4 years [SD 25.6]; 49.9% fall, 26.4% firearm), 7815 had geocodable addresses. Coverage ranged from 53.5% (Western) to 87.0% (North Central); 5492 (70.3%) fell within a 10-minute service area. One candidate site per region could extend modeled coverage to 4-321 additional death locations (480 combined). Volunteer-dominant regions, where 63% of agencies reported scheduling gaps, had the lowest coverage, suggesting headquarters presence overstates availability. Conclusion Geospatial analysis identified actionable blind spots where one additional EMS resource per region could extend modeled coverage to hundreds of death locations. Because these are modeled coverage gains, not demonstrated mortality reductions, and depend in volunteer-dominant regions on staffing reliability that placement alone cannot resolve, the identified locations warrant further evaluation, not presumed survival benefit.

Abstract Background Upper-airway and respiratory conditions impose a large and growing global burden, yet there is no standardized way to assess whether a nasal microbiome is “healthy”. Currently, monitoring remains reactive, beginning only after symptoms manifest. The nasal cavity is well suited to proactive monitoring: it shapes respiratory health and pathogen colonization resistance, and can be sampled non-invasively and repeatedly. To address this gap, we introduce the Nasal Microbiome Wellness Index (NMWI), a disease-agnostic, continuous score of nasal microbiome health derived from LASSO-penalized logistic regression. Rather than counting taxa, it learns which taxa (and in what balance) characterize a healthy nose and returns the predicted log-odds that a profile resembles a healthy state. The index was trained on 1654 nasal 16S rRNA gene amplicon sequencing samples (589 healthy, 1065 non-healthy) pooled from 27 publicly available studies, uniformly reprocessed through a single computational pipeline. Results The NMWI comprises an interpretable signature of 24 taxa whose combined relative abundances determine the health-associated log-odds. Health-associated genera such as Corynebacterium and Cutibacterium raised the score, while dysbiosis-associated genera such as Pseudomonas and Escherichia – Shigella lowered it. The NMWI substantially outperformed the Shannon, Simpson, and Chao1 diversity indices, which showed negligible, directionally inconsistent separation between healthy and non-healthy samples (|Cliff’s δ | = 0.01–0.20), whereas the NMWI produced large, consistent separation ( δ = 0.65). It achieved a balanced accuracy of 74.37% on the training data (resubstitution estimate), and 73.43% under repeated 10-times 10-fold cross-validation. Performance remained stable at mean balanced accuracy of 73.82% under a leave-one-study-out framework, reflecting cross-study generalizability. In independent external cohorts, the balanced accuracy was 71.49%, and leave-one-disease-out analysis (in which each disease condition was withheld from training) showed a mean balanced accuracy of 63.92% across unseen conditions, consistent with a disease-agnostic design. The index also generalized across heterogeneous datasets spanning multiple 16S rRNA gene hypervariable regions—to our knowledge the first demonstration of such cross-study, cross-region transferability for the nasal cavity. Conclusions The NMWI distills a complex nasal microbial profile into a single interpretable score computed directly from the 16S rRNA gene data that dominate existing nasal research, making it immediately applicable to published and future datasets without re-sequencing. By replacing descriptive, diversity-based comparison with a quantitative standard, it offers a reproducible, open-source foundation for cross-study benchmarking, individual-level phenotyping, and longitudinal respiratory wellness monitoring.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

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 Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, pathologically defined by the presence of Lewy bodies. Peripheral and central inflammation are increasingly recognized in DLB in clinical, post-mortem and animal studies. Finding clinically relevant biomarkers of inflammation in DLB will support the identification of novel pathways for disease-modifying therapies or use in clinical trials of immunomodulatory agents. Whilst there are cross-sectional studies of inflammation markers in DLB, there is limited evidence on the association between these markers and cognitive decline over time. Twenty participants with DLB underwent blood sampling for serum inflammatory markers, paired with PET imaging of the translocator protein (TSPO) and up to 4 years of longitudinal cognitive testing. Thirty participants with Alzheimer’s disease—comprising both Alzheimer’s dementia and/or mild cognitive impairment with biomarker evidence of amyloid pathology (AD/MCI+)—and 28 controls were also recruited for group comparisons. Data from 42 baseline cytokine immunoassays and TSPO PET were used as predictors of longitudinal cognitive scores in linear mixed-effects models. Partial least squares regression was used to test the association between peripheral and central inflammation. Using peripheral inflammatory markers as single predictors, we identified 14 associated with either a slower or faster rate of cognitive decline in DLB, whilst no single marker was predictive of decline in AD/MCI+. As many inflammatory markers were highly correlated, we used principal component analysis to identify a cytokine component associated with reduced cognitive decline in both DLB and AD/MCI+, that overlapped with the single markers identified in the previous analysis. A separate component was associated with cognitive decline in AD/MCI+ or DLB with Alzheimer’s dementia co-pathology (ascertained by amyloid PET). Widespread TSPO binding was associated with reduced cognitive decline in DLB, whilst a fronto-temporal pattern was associated with more rapid cognitive decline in both DLB and AD/MCI+. There were associations between peripheral cytokines and TSPO PET in AD/MCI+, but these were not significant in DLB. Overall, peripheral and central inflammation predicted cognitive decline in DLB. Specific patterns associated with both faster and slower rates of decline were identified. These profiles had both overlapping and contrasting associations when compared to AD/MCI+. Collectively, these data add to a body of evidence suggesting clinically relevant levels of inflammation in DLB. Future studies in larger, multi-site cohorts with multiple biomarker sampling points are required to understand the impact and dynamics of inflammation across all stages of the disease.

Abstract Introduction: Lymphomas are among the most common malignancies in dogs, and their morphologic and biological similarities to human counterparts have established them as widely accepted models of human disease. However, emerging molecular data suggest that conservation varies by subtype, and that many common lymphoma subtypes are principally convergent rather than homologous conditions, requiring a critical reassessment of when and how canine lymphomas provide relevant models of human disease. Methods: We employed conventional pathology, immunohistochemistry, and next generation sequencing platforms, including single-cell sequencing and spatial genomics, to characterize the transcriptional landscape and cellular organization of canine lymphomas, complemented by population metadata. Results: Our analysis revealed that canine DLBCL lacks the traditional GCB/ABC bifurcation seen in humans. While activation of NFκB pathways is seen commonly in these tumors, a germinal center-like DLBCL subtype is not apparent in dogs, and the transcriptional signatures are distinct from those of activated B-cell like DLBCL. Instead, we identified a novel canine-specific DLBCL subset driven by an AP-1-associated transcriptional signature, mirroring advanced human follicular lymphoma rather than human DLBCL. In contrast, emerging data suggest that downregulation of quiescence programs might be a shared feature of both human and canine MZLs, making this subtype a candidate for conserved disease mechanisms. While canine T-cell lymphomas exhibited extensive transcriptional heterogeneity, the samples in our dataset did not align with established human T-cell subgroups, suggesting that high incidence in dogs might not equate to molecular similarity. Conclusions: Our results suggest canine and human lymphomas are principally convergent rather than homologous diseases whose evolution and topological organization are constrained by developmental programs. The distinct transcriptional programs of canine and human DLBCLs reveal informative biological divergence, while potential shared features of MZL suggest contexts where comparative oncology may illuminate conserved disease mechanisms. Canine T-cell lymphomas exhibit extensive transcriptional heterogeneity that, at present, defies simple classification, mirroring challenges in their human counterparts and underscoring the need for larger studies to establish molecular patterns that inform causation, prognosis, and treatment. These findings highlight contexts where distinct and conserved molecular features between canine and human lymphoma can reveal shared and divergent disease mechanisms, ultimately underscoring the need for subtype-specific validation as canine lymphomas are adopted as models of human disease. Use of AI: Generative AI was used to improve clarity and readability of the abstract. AI was not used to conduct the study or to produce new text or material. Citation Format: Jaime F. Modiano, Amy E . Treeful, Grace E. Walker, Davis M. Seelig, Caitlyn M. Callaghan, Timothy D. O'Brien, Shruthi Naik, Aaron L. Sarver. Different paths to the same destination: Molecular divergence and functional convergence in human and canine lymphomas [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr A023.