Faculty Bibliography

BACKGROUND AND AIMS/OBJECTIVE:While inflammation and (auto-)immunity are modifiers of atherosclerosis, immunological determinants of cardiovascular risk beyond the established inflammatory markers, high-sensitivity C-reactive protein (hsCRP) and interleukin-6 (IL-6) remain incompletely defined in humans. This exploratory proof-of-concept study aimed to detect immunological signals enriched in patients with increased cardiovascular risk. METHODS:A nested case-control study was performed within the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort, a single-centre prospective study of 3316 patients. Forty-four individuals with and without angiographically confirmed coronary artery disease (CAD) and defined survival status were selected for comprehensive immunophenotyping employing bulk RNA, single-cell RNA (scRNA), single-cell T-cell receptor sequencing, mass cytometry, spatial imaging, and cytokine secretion in blood immune cells. Multi-omics factor analysis was applied to identify shared transcriptional programmes. Readouts were tested for their association with CAD status and cardiovascular mortality and validated in two independent cohorts. RESULTS:The integrated analysis strategy revealed immunological signals not detectable with conventional biomarkers, including a prominent signature of cellular proliferation, linked to activation, memory formation, clonal expansion, interferon signalling, and cytokine secretion in T cells. Cell types and transcriptional programmes associated with CAD status and poor long-term survival were identified. These associations were independent of established inflammatory markers, hsCRP and IL-6 that originated from myeloid cells. Key findings were replicated across independent cohorts using 136 scRNA sequencing datasets. Cellular proliferation was confirmed by protein validation in atherosclerotic plaques, and signatures were evaluated as stratification tools to predict short-term outcomes in acute coronary syndromes. CONCLUSIONS:Multi-omic profiling of blood immune cells revealed novel immune signatures associated with CAD, short- and long-term cardiovascular disease outcomes that are independent of systemic inflammation and may be used as risk stratification tools in the future.

BACKGROUND/UNASSIGNED:The transition from hospital discharge to home is a critical period prone to gaps in care. Telemedicine has potential to smooth this transition; however, few educational interventions assess the unique skills required for high-quality telemedicine care at this critical juncture. OBJECTIVE/UNASSIGNED:standardized patient (SP) encounters in internal medicine residents' actual clinics to assess telemedicine skills in the post-discharge period. METHODS/UNASSIGNED:We developed 2 cases portraying recently discharged patients, designed behaviorally anchored assessment checklists, created mock electronic health record entries, and scheduled telemedicine visits in residents' clinics throughout the 2023-2024 academic year. SPs assessed skills as "not done," "partly done," or "well done" across 5 skill domains: Information Gathering, Relationship Development, Education and Counseling, Telemedicine-Specific Skills, and Care Transition Skills. We analyzed differences in the percentage of "well done" items, fit an ordinal mixed-effects model to assess for performance by case and postgraduate year (PGY) level, and surveyed residents. RESULTS/UNASSIGNED:All 42 (100%) PGY-1s and PGY-2s in our program participated in 79 total encounters. Residents performed well in core communication domains but struggled with Telemedicine-Specific Skills, Care Transition Skills, and Education and Counseling skills. PGY-2 performance was stronger than PGY-1 performance in these domains. Among residents who completed both cases, performance in case 2, which took place 6 months after case 1, was stronger; this effect was driven by PGY-1 performance. Twenty-nine of 31 residents (94%) reported this intervention improved their telemedicine skills. CONCLUSIONS/UNASSIGNED:We report a high-fidelity strategy that captures telemedicine skill development in the context of patient care.

INTRODUCTION/BACKGROUND:Adjuvant chemotherapy can improve recurrence-free survival (RFS) in gastrointestinal malignancies. Previous review of phase III randomized controlled trials (RCTs) for colorectal cancer observed that RFS improvements were driven by early divergences during active chemotherapy; late recurrences were not influenced by adjuvant therapy. The broader applicability of this finding is unknown. METHODS:PubMed, Cochrane Library (CENTRAL), Embase, Scopus, and Web of Science were queried from database inception to December 31, 2025, for phase III RCTs including pancreatic, gastroesophageal, hepatocellular, and biliary tract malignancies. Trials where significant differences in RFS were observed between experimental (adjuvant chemotherapy) and control (resection alone) arms were included. Summary data were extracted from Kaplan-Meier curves using DigitizeIT, and absolute differences in RFS were compared at matched intervals using Wilcoxon matched-pairs signed rank tests. RESULTS:A total of 14 RCTs were identified, investigating periampullary (n = 4), gastroesophageal (n = 5), hepatocellular (n = 4), and biliary tract (n = 1) carcinomas. Across pooled RCTs, the highest rates of recurrence were observed in the first year following resection. Median RFS event rate was significantly higher with resection alone (0-0.5 y: resection alone 44.9 [IQR 14.2-84.1] versus adjuvant chemotherapy 26.4 [IQR 7.7-41.9], P < 0.001; 0.5-1 y: resection alone 33.2 [22.7-42.1] versus adjuvant chemotherapy 25.0 [13.8-44.5]; P = 0.007). No difference was observed during later intervals from postoperative randomization (1-2 y: P = 0.952; 2-3 y: P = 0.191; 3-4 y: P = 0.999; 4-5 y: P = 0.110). For the subset of trials where ≤6 mo of adjuvant chemotherapy was used (n = 10), improvements in RFS event rates were observed only during and immediately following the treatment interval (0-6 mo: P = 0.001; 6-12 mo: P = 0.037). CONCLUSIONS:Across multiple gastrointestinal malignancies, improvements in RFS associated with active adjuvant chemotherapy regimens are driven by early changes in recurrence dynamics. These data may provide in vivo understanding of residual tumor cell populations after curative-intent resection and how chemotherapy can be best used to prevent recurrence.

IMPORTANCE/UNASSIGNED:Patients frequently forgo filling prescriptions due to high out-of-pocket costs. Real-time prescription benefit (RTPB) tools that recommend available lower-cost, clinically equivalent medications to prescribing clinicians may increase the likelihood that patients fill their prescriptions. OBJECTIVE/UNASSIGNED:To determine whether availability of an RTPB tool increases prescription fill rates. DESIGN AND SETTING/UNASSIGNED:This post hoc analysis of a cluster randomized clinical trial included medical practices within an urban ambulatory clinical network randomized to the RTPB tool between January and December 2021. Outpatient prescriptions that were eligible for an RTPB recommendation during the study period were analyzed. Data analyses were performed from October 18, 2022, to August 9, 2024. INTERVENTION/UNASSIGNED:An electronic health record-integrated RTPB tool that displays available lower-cost and clinically equivalent alternatives to the initiated prescription at the point of prescribing. MAIN OUTCOME AND MEASURE/UNASSIGNED:The primary outcome measured whether a prescription was filled. RESULTS/UNASSIGNED:Of 1 386 577 outpatient prescriptions at randomized practices during the trial period, 38 289 (2.8%) were included in the analytic sample. Across all orders, the availability of the RTPB tool did not impact the proportion of orders filled (54% and 55% in the control and RTPB groups, respectively; adjusted difference: 1.2 percentage points [pp]; 95% CI, -1.3 to 3.7 pp). However, in the quartile of drug classes with the highest out-of-pocket costs (average out-of-pocket cost for a 30-day fill of >$120.83), fill rates increased from 33% in the control group to 49% in the RTPB group (adjusted difference: 14.5 pp; 95% CI, 8.4-20.6 pp). Similar increases were not detected in lower-cost drug classes. Increases in fill rates within the highest out-of-pocket cost drug classes were largest for patients in the lowest-income communities served by the health system (30.3 pp; 95% CI, 19.5-41.1 pp) but not substantial in the highest-income communities (1.0 pp; 95% CI, -10.2 to 12.2 pp). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this post hoc analysis of a cluster randomized clinical trial, there was no change in overall prescription fill rates, but among high-cost drugs, the RTPB tool increased fill rates, particularly among patients from low-income communities. However, RTPB recommendations were made for a small proportion of orders, limiting the applicability of the findings to a narrow segment of the randomized population. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04940988.

The interaction between lymphoma cells and immune microenvironment cells and the impact of this functional interplay on therapeutic responses remain largely unexplored. Here, we utilized murine models with oncogenically active MYD88 and additional genetic lesions co-triggered at selected B cell stages to generate human-like lymphomas harboring the MYD88L265P mutation. Lymphomas exhibited behaviors ranging from clinically indolent small-cell tumors to aggressive diffuse large B-cell lymphoma (DLBCL). Genetically diverse lymphoma cells employ distinct immune evasion mechanisms that shape unique lymphoma microenvironment (LME) states. In this setting, clonally expanded T-cells function as a double-edged sword, either sustaining indolent lymphoma cell survival or promoting antitumor responses in DLBCL. Consequently, the efficacy of standard-of-care and novel immunotherapies was determined using individual T-cell features. Furthermore, the experimental targeting of newly identified immune mechanisms has improved therapeutic responses in vivo. Our results elucidate that genetically driven LME landscapes influence therapeutic outcomes across distinct lymphoma subtypes, providing proof-of-concept for personalized treatment based on immune LME information.

PURPOSE/OBJECTIVE:The objective of this study was to evaluate cross-sectional and longitudinal associations between contact with the criminal legal system (CCLS) and substance use among transgender women of color in New York City. METHODS:= 311). CCLS included arrest and incarceration and was measured at baseline, 6 months, and 12 months. Substance use included recreational substance use (any), alcohol use (any), and specific substance use (cannabis, cocaine, and methamphetamine). Generalized estimating equations were used to assess longitudinal associations between baseline CCLS and substance use over time. RESULTS:At baseline, 50.5% of participants reported having been arrested, and 28.0% reported experiencing incarceration. In multivariable analyses, CCLS was associated with substance use after controlling for confounders. In particular, at baseline, incarceration was associated with a 1.73 times (95% confidence interval: 1.06-2.81) likelihood of recreational substance use. We found no significant associations between CCLS and alcohol or specific substance use. CONCLUSION/CONCLUSIONS:The findings suggest that CCLS was associated with recreational substance use among transgender women of color at baseline. Substance use prevention and treatment programs should consider screening for substance use during reentry after incarceration and providing additional support for individuals with criminal legal system involvement.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality. Despite various therapeutic options, treatment resistance is common, underscoring the need for effective combination therapies and reliable pre-clinical models for patient-specific evaluation. Here, we describe strategies for reconstituting tumor immune microenvironment (TIME) components within patient-derived tumor organoid (PDTO) cultures. We established a tumor processing pipeline that enables concurrent expansion of tumor-infiltrating lymphocytes (TILs) and PDTO generation from the same resection. We optimized scalable assays to assess IFN-γ secretion and T cell cytotoxicity with immune checkpoint inhibitors (alone or in combination) and targeted inhibitors, capturing inter-patient heterogeneity and intra-patient variations between TILs and peripheral blood mononuclear cells (PBMCs). Additionally, we developed methods for differentiating PDTO-specific tumor-associated macrophages (TAMs) and established PDTO-TAM co-culture systems to evaluate TAM effects on PDTO growth and chemotherapy sensitivity. All approaches are scalable to high-throughput levels, highlighting the value of TIME-PDTO co-cultures for therapeutic modeling and precision medicine.

INTRODUCTION/BACKGROUND:with dysglycemia, it remains unknown if reducing air pollution exposure through air filtration can affect improvements in glucose. This study aims to test the hypothesis that short-term, in-home air pollution reduction using high efficiency particulate air (HEPA) filtration will improve blood sugar in adults with prediabetes. METHODS AND ANALYSIS/METHODS:as the independent variable. ETHICS AND DISSEMINATION/BACKGROUND:This study has undergone peer review; and the work was supported by Grant 2023-0214 from the Doris Duke Foundation, who had no other role in study design or implementation. The study was registered in ClinicalTrials.gov (NCT05994937) prior to recruitment.

BACKGROUND/UNASSIGNED:Efforts to reduce waitlist mortality in lung transplantation have been hindered by a high donor lung non-use rate, with approximately one-fifth of deceased organ donors ultimately providing lungs for transplantation. Rationale for organ decline varies across individual providers and transplant centers. Greater donor lung utilization could significantly reduce waitlist mortality and improve clinical outcomes for adult lung transplant candidates. OBJECTIVE/UNASSIGNED:This study aimed to evaluate whether machine learning models can successfully predict donor lung non-use and characterize donor factors associated with current utilization patterns, with the long-term goal of informing more consistent and data-driven donor lung evaluation. METHODS/UNASSIGNED:Using U.S. national registry data collected between 2012 and 2022, we generated five machine learning models - logistic regression, decision tree, random forest, XGBoost, and Naive Bayes - all of which were trained with 19 donor variables to predict donor lung non-use. Models were tested with both full donor feature sets and restricted donor feature sets comprised of the top 15, 10, and 5 predictors. Performance metrics, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, were assessed using cross-validation. RESULTS/UNASSIGNED:ratio, donor age, body mass index, and serum aspartate aminotransferase levels. More restricted random forest models (i.e., those that only included 5 or 10 predictors) and any of the restricted logistic regression and XGBoost models had inferior but still acceptable performance (AUROC 0.867-0.958). CONCLUSION/UNASSIGNED:Machine learning models, particularly random forest, XGBoost, and logistic regression, accurately predicted donor lung non-use. Restricted models using fewer predictors also maintained strong performance, demonstrating technical feasibility for future evaluation in time-sensitive workflows. Because the models were trained on historical utilization decisions rather than recipient outcomes or independent measures of donor suitability, prospective validation is needed to determine whether they can improve appropriate donor lung utilization or support acceptance decisions without reinforcing practice-pattern bias.