Blastomyces species complex causes infection in persons with intact and impaired immune defenses. The diagnosis of blastomycosis is challenging because it mimics infectious and non-infectious diseases. Blastomyces adhesin-1 (BAD-1) protein is a major virulence factor in B. dermatitidis and B. gilchristii and induces a humoral immune response during infection. The goal of this retrospective, case-control study conducted at the University of Wisconsin- Madison is to investigate the test characteristics of the newly developed second generation BAD-1 IgG Enzyme Immunoassay (EIA) antibody test for the diagnosis of blastomycosis. The study was performed in an endemic area in a diverse patient population including persons with underlying immunocompromise. Thirty-six case patients with proven or probable blastomycosis were compared to 370 controls. Serum BAD-1 IgG was positive in 50% of the case patients and in 9.7% of the controls, which resulted in a sensitivity of 50% and specificity of 90.2%. The highest sensitivity (80%) occurred in non-immunocompromised persons with chronic blastomycosis and the lowest sensitivity occurred in those with acute blastomycosis (35.0%) or immunocompromise (37.5%). Sensitivity was not influenced by dissemination or severity of disease. In conclusion, this study demonstrates that the BAD-1 IgG EIA can serve as adjunctive test for the diagnosis of blastomycosis in select patient populations living in regions endemic for blastomycosis.

The University of Wisconsin (UW) solution is widely used for cardiac allograft preservation. In early 2021, our center transitioned to Del Nido (DN) cardioplegia for all donors. This study evaluated whether this shift affected post-transplant outcomes. Adult, single-organ, donation after brain death heart transplants from January 2020 to December 2023 were included; congenital cases and non-ice storage techniques were excluded. Recipients were grouped by preservation solution. Interrupted time series (ITS) regression accounted for temporal bias and baseline differences, while exponential decay analysis evaluated lactate clearance. Of 203 transplants, 71 used UW and 132 used DN. Baseline characteristics were similar aside from longer ischemic times in the DN group. Unadjusted outcomes showed no significant differences in severe primary graft dysfunction (PGD), early mortality, cardiac index, or 24 hour vasoactive inotrope score (VIS). Intensive care unit and hospital stays were longer in the DN group. Lactate clearance was faster with DN (half-life 11.3 vs. 18.6 hours; p = 0.07). Interrupted time series regression showed no significant impact of DN on PGD, mortality, or morbidity scores, though VIS modestly increased (p = 0.048), and peak lactate levels decreased (p = 0.004). Del Nido provides comparable preservation to UW, supporting its use based on logistics and availability.

Objective: Machine learning provides powerful tools for analyzing large datasets; however, it faces challenges such as high computational costs and overfitting. To overcome these issues techniques that reduce the dimensionality of data are frequently used. Dimensionality reduction aims to eliminate redundant or unnecessary information in the dataset thereby reducing computational load and improving the model's ability to generate more accurate results. The primary objective of this study is to evaluate the performance of the Autoencoder algorithm, one of the dimensionality reduction methods. This study will thoroughly examine the effectiveness of the Autoencoder algorithm in terms of data loss processing time and the model’s performance on new data. Materials and Methods: Breast masses can be effectively analyzed using quantitative features of cell nuclei obtained from fine-needle aspiration (FNA) samples. This study aimed to evaluate the performance of the Autoencoder algorithm for dimensionality reduction on these features. The analysis was conducted on 569 cases from the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, accessible via an online repository provided by the University of Wisconsin–Madison. The dataset included quantitative features for each cell nucleus, specifically radius, smoothness, compactness, and concavity. The Autoencoder algorithm was applied to the entire dataset to reduce dimensionality while preserving relevant information. To illustrate its operation concretely, four primary features from five randomly selected observations were used, demonstrating the algorithm’s performance on a small, non-linear subset of the data. For comparison, commonly used dimensionality reduction techniques, including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were also applied. Results indicate that the flexible architecture of Autoencoders effectively captures the most informative features, supporting their applicability to clinical datasets and potential integration into computer-aided diagnostic workflows. This approach provides a reliable foundation for analyzing complex biomedical data and assessing algorithm performance in real-world clinical contexts. Results: This study focused on a comparative analysis of four key variables radius mean, smoothness mean, compactness mean, and concavity mean using both the original dataset and the dataset reconstructed through an Autoencoder model. In the original dataset, the mean and standard deviation values of these variables were calculated as 14.13 ± 3.52, 0.10 ± 0.01, 0.10 ± 0.05, and 0.09 ± 0.08, respectively. At the output layer, the Autoencoder successfully reconstructed the input features, preserving their mean values and yielding corresponding mean ± standard deviation values of 14.13 ± 2.38, 0.10 ± 0.01, 0.10 ± 0.05, and 0.09 ± 0.07. The reduction in standard deviations in the reconstructed dataset, particularly for the radius mean and concavity mean variables, indicates decreased variability and suggests that the model produced a more compact representation while retaining the essential characteristics of the data. The primary objective of the Autoencoder is to ensure that the output closely resembles the original input by utilizing a hidden layer (h) that captures the essential structure of the data. Aligned with this purpose, the algorithm effectively compressed the four-dimensional input into a more compact latent representation while preserving key characteristics. The analyses showed that the hidden layer representations were highly consistent with the original data and were optimized successfully. Consequently, the dimensionality of the dataset was reduced from four variables to a lower-dimensional representation, enabling a more efficient and informative encoding of the data. Conclusion: This study evaluated the performance of the Autoencoder algorithm for dimensionality reduction using quantitative features of cell nuclei obtained from fine-needle aspiration (FNA) samples of breast masses. The analysis was conducted on a dataset of 569 cases, and to illustrate the algorithm’s operation, data from four key features (radius, smoothness, compactness, and concavity) of five randomly selected observations were used as examples. This approach allowed for the demonstration of the Autoencoder’s performance on small and non-linear subsets of the data. The findings indicate that dimensionality reduction plays a significant role in clinical data analysis and that the Autoencoder algorithm also reduces computational costs. These results confirm the potential of Autoencoders as a reliable and effective tool for dimensionality reduction. Consequently, the use of Autoencoders can enable faster, more accurate, and more efficient processing of healthcare data, thereby enhancing the effectiveness of clinical decision support systems.

The University of Wisconsin (UW) is part of a team supporting General Atomics Electromagnetic Systems (GA-EMS) in its development of a 100-MWth gas-cooled fast modular reactor. In particular, the FMR uses the reactor vessel cooling system (RVCS) to passively remove the decay heat from the reactor pressure vessel. UW has developed an RVCS test facility for the GA-EMS modular high-temperature gas-cooled reactor and has performed past experiments to demonstrate its performance. This RVCS facility has been updated, and we have performed a series of repeatability tests under ISO (International Organization for Standardization) 17025 standards to validate the existing data set and our previous work. In addition, the UW team has developed a MELCOR model of the UW RVCS facility to analyze these tests and to simulate the two-phase natural circulation flow. This paper presents the recent tests and associated analyses that demonstrate good agreement with the data, except for the larger flow oscillation. The effects of the heat loss in the loop and the water tank pressurization were investigated, but no significant impact was shown. The detailed investigation revealed that the MELCOR simulation results in a higher void fraction (10% to 70%) compared to the experiments (5% to 40%) and an earlier start of flashing. We hypothesize that the water needs to be superheated to flash in the UW RVCS facility due to the slightly colder pipe structures with heat loss.

Background:A number of reviews have been published concerning clinical decision support systems in long-term care facilities (DSSLTCF). These reviews frequently utilize literature analysis to evaluate the characteristics of DSSLTCF. However, none of the existing research has employed social network analysis (SNA) to classify their features concerning digital innovations aimed at mitigating staff shortages and improving quality. To better understand the current landscape of DSSLTCF, it is crucial to examine the tools currently in use. The objectives of this review are 2-fold: to classify DSSLTCF using cluster analysis, and to identify the authors who have significantly contributed to DSSLTCF research in recent years.Methods:Literature published since 2010 was reviewed using key search terms in the Web of Science Core Collection. The review focused solely on articles and review articles that evaluated DSSLTCF. Cluster analysis was performed using SNA, with evidence provided by the similarity in proportional counts of major keywords between groups. A performance sheet was used to illustrate the top 10 contributing entities (including countries, institutes, departments, and authors) to DSSLTCF based on the h-index.Results:A total of 69 papers were included in the final review, divided into 2 groups: target papers (n = 16) and contrast papers (n = 43). There was no significant difference in proportional counts for major keywords between the 2 groups. Nine themes of DSSLTCF were identified, including digital technology. The 4 entities contributing the most to DSSLTCF with the highest publication counts were: the United States (25), the University of Wisconsin (4), Medicine (6), and Christine R, Kovach from the US (3) in the categories of countries, institutes, departments, and authors, respectively.Conclusions:The use of SNA and h-indexes is a viable and effective method for classifying and identifying DSSLTCF. This study demonstrates the visualization of DSSLTCF characteristics, including their classifications and authors’ contributions, and recommends these methods for future research beyond the scope of DSSLTCF.

Static cold storage (SCS) remains the most widely used method of liver graft preservation due to its simplicity, accessibility, and reduced cost in transplantation practice. Since the invention of the University of Wisconsin (UW) solution, several alternative preservation solutions-including histidine-tryptophan-ketoglutarate (HTK), Celsior, and more recently IGL-1 and IGL-2-have been formulated to optimize cellular and vascular protection during cold ischemia. More recently, the introduction of dynamic perfusion techniques, such as hypothermic oxygenated perfusion (HOPE) and normothermic machine perfusion (NMP), approximately fifteen years ago, has further enhanced transplantation protocols, being applied either alone or in combination with traditional SCS to ensure optimal graft preservation prior to implantation. Despite these technological advances, achieving fully effective dynamic perfusion remains a key challenge for improving outcomes in vulnerable grafts, particularly steatotic or marginal livers. This review details how Polyethylene Glycol 35 (PEG35)-based solutions activate multiple cytoprotective pathways during SCS, including AMP-activated protein kinase (AMPK), nitric oxide (NO) production, and the antioxidant transcription factor Nrf2. We propose that these molecular mechanisms serve as a form of preconditioning that is synergistically leveraged by HOPE to preserve mitochondrial function, endothelial glycocalyx integrity, and microvascular homeostasis. Furthermore, the oncotic and rheological properties of PEG35 reduce perfusate viscosity, mitigating shear stress and microcirculatory damage during dynamic perfusion-effects that are further enhanced by NO- and AMPK-mediated protection initiated during the SCS phase. This integrated approach provides a strong rationale for combining PEG35-mediated SCS with HOPE, particularly for grafts with high susceptibility to ischemia-reperfusion injury, such as fatty livers. Finally, we highlight emerging avenues in graft preservation, including the design of unified perfusion solutions that optimize endothelial, mitochondrial, and redox protection, with the potential to improve post-transplant outcomes and extend applicability to other solid organ grafts.

ABSTRACT In this study, we examine the role of the Roberto Hernández Center (RHC) in serving Latine students at the University of Wisconsin–Milwaukee, an emerging Hispanic Serving Institution. We analyze quantitative data (center utilization, student appointments) and qualitative data (listening sessions, key informant interviews) using the RE-AIM framework. We describe the Center’s Reach, Effectiveness, Adoption, Implementation, and Maintenance. Findings illuminate the Center’s contributions to Latine students’ personal and educational experiences and provide insights into best practices for multicultural centers supporting this demographic, especially in regions with educational inequities and underrepresentation, amid sociopolitical changes in higher education.

BackgroundRecent studies demonstrate tau burden is heterogeneous after A+ onset and is temporally proximal to clinical impairment in sporadic AD. This study uses temporal modeling and neuroimaging data from three cohorts to investigate common factors that may hasten amyloid‐related tau accumulation.MethodsParticipants with available amyloid and tau PET imaging were included from ADNI (n = 880), OASIS (n = 445), and University of Wisconsin (WISC: WRAP and Wisconsin ADRC; n = 739) cohorts. The following steps were completed separately for each cohort. Amyloid and tau were quantified, respectively, using Centiloids (CL) and medial temporal and temporal neocortex standard uptake value ratios (SUVR). A+ and T+ thresholds were defined as the mean plus two standard deviations (SDs) of lower Gaussian mixture model distributions. Sampled iterative linear approximation (SILA) was used to estimate A+ onset age (EAOA) and A+ time (age at observation minus EAOA). To understand moderators of the relationship between A+ time and tau SUVR's, we excluded those deemed confidently A‐ (<1 SD below the lower GMM group mean) and used LMEs to characterize associations between A+ time and tau SUVR, and investigated whether age at tau baseline, APOE‐e4 carriage, sex, or education category explained additional variation in tau, both as main effects and interactions with A+ time.ResultsCohort characteristics are shown in Table 1. Results (Figure 1) were mostly consistent between OASIS and WISC cohorts with A+ time having a significant positive association with tau SUVR in both medial temporal and temporal neocortex, and APOE‐e4 carriage having a significant interaction with A+ time for the medial temporal SUVR (APOE‐e4 carriers had faster tau trajectories). These effects were also significant in ADNI, and additionally interactions of A+ time by baseline tau age and by sex (females had faster tau trajectories) were significant for medial temporal tau, and A+ time by baseline tau age for temporal neocortex (younger age had faster tau trajectories). The education by A+ time interaction did not reach significance in any cohort/region.ConclusionIn three longitudinal cohorts, APOE‐e4 carriage consistently accelerated tau trajectories relative to A+ onset. Future work will further investigate these relationships and cohort differences that may contribute to mixed findings.

Radiofrequency ablation (RFA) is a minimally invasive technique increasingly utilized in the treatment of musculoskeletal pain, particularly when pharmacological or rehabilitative approaches prove insufficient. However, the thematic evolution and research development of RFA over the past 2 decades have not been explored adequately. To conduct a comprehensive bibliometric analysis of the global literature on RFA for musculoskeletal pain, identifying publication trends, research focuses, leading contributors, and thematic transitions between 2000 and 2024. Bibliometric analysis. Data were retrieved from the Web of Science Core Collection (WoSCC). A total of 736 English-language articles published between January 1, 2000, and December 31, 2024, were included. Bibliometric tools such as VOSviewer, CiteSpace, and Biblioshiny were used to analyze annual publication trends, co-authorship networks, country/institutional productivity, keyword co-occurrences, co-citation patterns, and citation bursts. A marked increase in publication volume was observed after 2010, with the peak occurring in 2023. The United States led in both publication output and international collaboration. The Johns Hopkins School of Medicine, University of Wisconsin, and University of Utah were among the most productive institutions. Steven P. Cohen and Laxmaiah Manchikanti emerged as the most influential authors, centrally positioned within international co-authorship networks. Meanwhile, Pain Physician, Pain Medicine, and Regional Anesthesia and Pain Medicine were identified as the journals that exerted the greatest impact. Co-citation analysis revealed a thematic shift from early spinal facet interventions and diagnostic blocks toward peripheral applications, especially genicular nerve ablation, and consensus-based clinical practices. Keyword co-occurrence and citation burst analyses identified 3 chronological research themes: firstly, early spinal interventions (2000-2010), secondly, the diagnostic standardization era (2010-2017), and thirdly, expansion into peripheral, imaging-guided, and multidisciplinary applications (2017-2024). The analysis was limited to English-language articles indexed in the WoSCC. Conference proceedings, book chapters, and articles from other databases were excluded. Therefore, some relevant studies might not have been captured. This bibliometric analysis demonstrates a steady growth in RFA-related publications globally. While spinal interventions remain the primary focus, an interest in peripheral applications has seen a notable increase. The expansion of RFA reflects both technological advancements and evolving clinical demands. Future studies should focus on long-term outcomes, clinical adoption, and the evidence-based optimization of treatment algorithms across spinal and peripheral indications.

High-fidelity forced convection simulations of the University of Wisconsin–Madison air-cooled reactor cavity cooling system

BackgroundIndividuals with the APOE4 allele have a lifetime enhanced risk for Alzheimer’s disease (AD) including earlier average onset of amyloid and tau. [F‐18]MK6240 is a PET radioligand that binds to tau aggregates in AD, however, variable MK6240 off‐target signal in the meninges adjacent to target regions can influence PET quantification. Previous studies (Smith, 2021) have identified higher MK6240 meninges signal in females and a PET signal dependence on scanner model. The goal of this study is to compare the magnitude and distribution of meninges MK6240 signal observed in APOE4 carrier and non‐carrier populations.MethodAll participants (n = 1051) were scanned at the University of Wisconsin–Madison from 90‐110 minutes on a Biograph Horizon mCT or ECAT HR+ (Table 1). MK6240 PET images were processed using a standardized pipeline to generate SUVR images using the inferior cerebellar grey matter reference region and smoothed to a common 6mm resolution. The meninges ROI used was created by diluting the MNI‐152 cortical brain mask by 5mm and then subtracting the original mask. This mask was then warped into native space for analysis. APOE4 carriers and non‐carriers were compared using a multiple regression model inlcuding meninges SUVR with APOE4 carriage, sex, scanner model and the interaction terms between variables. In a subgroup of the study a sensitivity analysis including female participants imaged on the HR+ (n = 349) was performed with a student’s t‐test comparing carriers and non‐carriers.ResultHigher average meninges signal was observed for APOE4 carriers (p = 0.03), females (p < 0.001) and participants imaged on the mCT (p < 0.001). No significant interaction terms were observed. In the sensitivity analysis female APOE4 carriers on the HR+ had significantly higher meninges signal (p = 0.009). Similar results were observed with female participants on the mCT (p = 0.05), although the differences for males were not significant on either scanner (p = 0.24, p = 0.15).ConclusionsSpill‐in effects from MK6240 meninges signal can potentially bias tracer outcomes in target analysis regions. Meninges signal can be highly variable, but the relationships between APOE4 carriage and sex on this measure should be accounted for in MK6240 quantification and population‐based comparisons.

BackgroundIndividuals with the APOE4 allele have a lifetime enhanced risk for Alzheimer's disease (AD) including earlier average onset of amyloid and tau. [F‐18]MK6240 is a PET radioligand that binds to tau aggregates in AD, however, variable MK6240 off‐target signal in the meninges adjacent to target regions can influence PET quantification. Previous studies (Smith, 2021) have identified higher MK6240 meninges signal in females and a PET signal dependence on scanner model. The goal of this study is to compare the magnitude and distribution of meninges MK6240 signal observed in APOE4 carrier and non‐carrier populations.MethodAll participants (n = 1051) were scanned at the University of Wisconsin–Madison from 90‐110 minutes on a Biograph Horizon mCT or ECAT HR+ (Table 1). MK6240 PET images were processed using a standardized pipeline to generate SUVR images using the inferior cerebellar grey matter reference region and smoothed to a common 6mm resolution. The meninges ROI used was created by diluting the MNI‐152 cortical brain mask by 5mm and then subtracting the original mask. This mask was then warped into native space for analysis. APOE4 carriers and non‐carriers were compared using a multiple regression model inlcuding meninges SUVR with APOE4 carriage, sex, scanner model and the interaction terms between variables. In a subgroup of the study a sensitivity analysis including female participants imaged on the HR+ (n = 349) was performed with a student's t‐test comparing carriers and non‐carriers.ResultHigher average meninges signal was observed for APOE4 carriers (p = 0.03), females (p < 0.001) and participants imaged on the mCT (p < 0.001). No significant interaction terms were observed. In the sensitivity analysis female APOE4 carriers on the HR+ had significantly higher meninges signal (p = 0.009). Similar results were observed with female participants on the mCT (p = 0.05), although the differences for males were not significant on either scanner (p = 0.24, p = 0.15).ConclusionsSpill‐in effects from MK6240 meninges signal can potentially bias tracer outcomes in target analysis regions. Meninges signal can be highly variable, but the relationships between APOE4 carriage and sex on this measure should be accounted for in MK6240 quantification and population‐based comparisons.

Graft vs host disease outcomes in early tacrolimus withdrawal following allogeneic hematopoietic stem cell transplant

Area deprivation index as a predictor of survival and treatment disparities in older adults with Acute Myeloid Leukemia: A population-based Study

Impact of recipient sociodemographic factors on allogeneic transplant conditioning intensity and survival outcomes in myeloid malignancies: A secondary analysis of CIBMTR data

Impact-AL: A phase 2 clinical trial of teclistamab and daratumumab in previously untreated AL amyloidosis

Understanding access and treatment challenges in multiple myeloma care

Abstract This article explores the interconnectedness of two significant events at the University of Wisconsin–Madison in spring 2024: the university's adoption and promotion of Microsoft's generative AI program, Copilot, and the aggressive police response to a peaceful pro-Palestinian protest. Taking student writing as a locus of power, the article begins with an examination of the ways surveillant technologies such as Turnitin and Honorlock foster an institutional climate that normalizes security interventions in campus spaces, then pivots to an exploration of how universities’ recent investments in generative AI technologies impose subtle yet significant constraints on the diversity and agency of student critical inquiry and creative expression. Viewed collectively, AI plagiarism detection software, remote proctoring systems, and chatbots construct an institutional climate that normalizes both technological and physical interventions against student expression. Drawing on theoretical frameworks of Paolo Freire and political philosopher Annette Zimmermann, it becomes clear that generative AI, implemented in haste and with little or no student input, often functions to reinforce surveillant power structures, enacting a form of linguistic violence by alienating students from authentic and ethical decision-making processes in their intellectual work. This rhetorical regulation, or constraint of student communicative agency, challenges fundamental principles of universities as spaces for critical inquiry and creative expression.

144 Background: Despite high patient interest in clinical trials, fewer than 10% of adult oncology patients enroll, often due to missed matching opportunities and labor-intensive screening processes. At the Medical College of Wisconsin (MCW) Cancer Center, we deployed an AI platform (Triomics PRISM) powered by a domain-specific large language model (OncoLLM) to prospectively screen every upcoming oncology visit against our active trial portfolio, with a focus on improving equitable access through scalable pre-screening. Methods: We conducted a prospective quality improvement initiative guided by the RE-AIM framework. In July 2024, PRISM was deployed across five disease-oriented teams (GI, GU, Breast, Thoracic, GYN Onc) to screen 100% of upcoming visits. The platform used structured and unstructured EHR data to evaluate eligibility against &gt; 100 recruiting trials and generated match summaries for coordinators and physicians. Coordinators completed a 2-hour onboarding session and integrated the tool into routine workflows. We assessed RE-AIM domains: Reach (proportion of visits screened), Effectiveness (accrual changes, match accuracy), Adoption (daily use, coordinator engagement), Implementation (integration fidelity, time spent per review), and Maintenance (sustained coverage over time). Concordance between PRISM matches and final trial enrollment was used to assess accuracy. Results: Between July 1, 2024 and December 31, 2024, the platform automatically screened &gt; 19,000 patient visits (~6,000/month), and reduced coordinator review time from 20–25 minutes to 3–12 minutes per patient. Among those enrolled during the study period, 72% were identified by PRISM prior to their clinic visit. Clinical trial accruals increased by 39% in Q3 and 27% in Q4 compared to the same periods in 2023. Importantly, trial matches showed &gt; 95% concordance with final enrollment choices, affirming accuracy. Coordinators demonstrated enthusiastic adoption, with several completing hundreds of deep trial matches, suggesting sustained and meaningful engagement. Monthly platform coverage has remained consistently high, with &gt; 97% of all upcoming patients reviewed every month since deployment. The AI-supported workflow also surfaced trial access opportunities in community patients and newly referred individuals, enabling more equitable screening. Conclusions: A scalable OncoLLM-based solution enabled comprehensive trial screening across multiple oncology indications, improving patient-trial matching speed, increasing accrual, and ensuring access for patients previously missed by manual approaches. Our deployment offers a reproducible model for increasing equity in clinical trial access by reducing reliance on variable human screening capacity and transforming the trial-matching process into an inclusive, data-driven system.

142 Background: Black patients with pancreatic cancer remain significantly underrepresented in clinical trials, contributing to persistent disparities in outcomes. While digital health tools hold promise for improving clinical trial communication and engagement, limited data exist on technology access, preferences, and readiness among Black patients with pancreatic cancer. As part of the PROmoting CLinicAl TrIal EngageMent for Pancreatic Cancer App (PROCLAIM) Study, a multi-phase initiative to develop a mobile health (mHealth) intervention, this study explored participants’ digital engagement patterns and information preferences to inform culturally responsive trial communication strategies. Methods: Semi-structured interviews were conducted with 15 Black adults diagnosed with pancreatic cancer at the Medical College of Wisconsin (n = 6) and the University of North Carolina (n = 9) between June and August 2024. They ranged in age from 38 to 81years and included 9 females and 6 males. Topics included device ownership, digital health behaviors, patient portal use, and preferences for receiving information about clinical trials. Interviews were audio-recorded, professionally transcribed, and analyzed using thematic analysis in Dedoose. Multiple researchers independently coded transcripts using consensus procedures to ensure thematic reliability. Results: Fourteen of 15 participants owned smartphones, though levels of digital literacy varied. Three key themes emerged: (1) Patient portal reliance and barriers – While MyChart (Epic Systems' patient portal) was widely utilized, 7 of 15 participants reported requiring assistance (e.g., “My daughter checks it for me I don’t go in there much.”); (2) Supplement, not replace – Participants expressed preference for digital tools that enhanced provider interaction rather than served as substitutes (e.g., “I want to hear it from my doctor, not just read it.”); (3) Platform integration over app novelty – Participants preferred digital tools embedded within trusted and familiar platforms like MyChart. Barriers included complex interfaces and technology anxiety. Facilitators encompassed family support and provider endorsement. Conclusions: Initiatives to enhance clinical trial engagement through digital tools should: (1) build upon existing patient portals, (2) accommodate varying levels of digital literacy, and (3) support, rather than replace, provider relationships. These findings inform design principles for mHealth interventions that reflect the lived experiences and digital preferences of Black people with pancreatic cancer. Clinical trial information: NCT06252545 .