Publications by Year: 2026

Few evidence-based programs exist to help women with a history of gestational diabetes reduce their risk of developing type 2 diabetes. In secondary analyses from a randomized clinical trial of a web-based lifestyle intervention program for postpartum women with recent gestational diabetes, we studied changes in self-efficacy for diet and physical activity and readiness to change health behaviors. Women were randomized at ∼6 weeks postpartum and completed questionnaires at 6 weeks and 6, 12, 18, and 24 months. Our study included 181 women (mean age 32.4 ± 5.2 years; 48% White, 19% Asian, 14% Black or African American, 17% other/mixed race; 34% Hispanic). In a linear mixed effects model, women in the intervention had significantly greater improvement in overall self-efficacy scores for physical activity compared with the control group at 24 months (difference in change scores between groups .35, 95% CI: .03 to .67, P = .03). The intervention group also demonstrated significantly greater improvement in self-efficacy scores for both physical activity subdomains, specifically "sticking to it" at 24 months and "making time" at 12 months. Participants in the intervention did not experience a significant difference in change in self-efficacy for diet or readiness to change compared with those in the control arm.

BACKGROUND: Physical activity is an established protective factor for colorectal cancer (CRC), but it is unclear if genetic variants modify this effect. To investigate this possibility, we conducted a genome-wide gene-physical activity interaction analysis.

METHODS: Using logistic regression (1-d.f), two-step screening and testing method (EDGE), and joint tests (3-d.f), we analyzed interactions between common genetic variants across the genome and physical activity in relation to CRC risk. Self-reported physical activity levels were categorized as active (≥ 8.75 MET-h/wk) vs. inactive (< 8.75 MET-h/wk; 39,992 participants) and as study- and sex-specific quartiles of activity (42,602 participants).

RESULTS: Physical activity was inversely associated with CRC risk overall (OR [active vs. inactive] = 0.85; 95% CI = 0.81-0.90). The two-step EDGE method identified an interaction between rs4779584, an intergenic variant near the GREM1 and SCG5 genes, and physical activity for CRC risk (p-interaction = 2.6 × 10-8). Stratification by genotype at this locus showed a significant reduction in CRC risk by 20% in active vs. inactive participants with the CC genotype (OR = 0.80; 95% CI = 0.75-0.85), but no significant physical activity-CRC associations among CT or TT carriers. When physical activity was modeled as quartiles, the 1-d.f. test identified that rs56906466, an intergenic variant near the KCNG1 gene, modified the association between physical activity and CRC (p-interaction = 3.5 × 10-8). Stratification at this locus showed that an increase in physical activity (highest vs. lowest quartile) was associated with a lower CRC risk solely among TT carriers (OR = 0.77; 95% CI = 0.72-0.82).

CONCLUSIONS: In summary, we identified two genetic variants that modified the association between physical activity and CRC risk. One of them, related to GREM1 and SCG5, suggests that the bone morphogenetic protein (BMP)-related, inflammatory, and/or insulin signaling pathways may be involved in the protective association between physical activity and colorectal carcinogenesis.

Intervertebral disc (IVD) degeneration is a naturally occurring process that is a consequence of biological ageing and exposure to normal physiological loading over a lifetime and is characterized by loss of IVD tissue structural integrity. The nucleus pulposus changes with loss of pressurization, decreased collagen concentration and loss of distinction from annulus fibrosus. The annulus fibrosus and cartilaginous endplate suffer delamination, tears, fractures and clefts of their respective extracellular matrix at both microscopic and macroscopic scales. This loss of structural integrity generally follows a predictable pattern of degeneration, and it predisposes the IVD to pathological states. As the disc degenerates, the likelihood of functional failure to protect the neural elements and/or to provide stable spine motion and support increases. Functional failure takes the degenerated IVD to a state of disc pathology that has various phenotypes: the most common forms are disc herniation, mechanical instability, spinal stenosis, degenerative spondylolisthesis and degenerative scoliosis. IVD pathology is commonly self-limited and non-operative treatment remains the mainstay of treatment in most patients. For patients with refractory disease, surgical intervention focuses on neural decompression and, when indicated, motion segment stabilization. Future therapies for prevention of disc degeneration, targeted disc regeneration and biological modification of the degenerative cascade might prevent or reverse pathological changes across all spinal regions.

Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is costly, radiation-intensive, and impractical for large-scale screening. We propose a deep learning model to estimate regional metabolic activity in adipose tissue from standard non-contrast CT, enabling PET-like insights without radiotracers. Using paired PET/CT data from two independent cohorts, we trained a conditional Generative Adversarial Network (cGAN) to predict standardized uptake values (SUV) within adipose regions identified on CT. The network included a fat-focused loss function to enhance metabolic signal estimation. Predicted activations showed strong agreement with PET-derived values and were reproducible across anatomical regions and datasets. This method provides a radiation-sparing alternative for assessing adipose metabolic activity in clinical and research settings and it could support population-based studies of BAT, metabolic health, and disease progression using routine chest CT scans without additional imaging burden.

Clinical decision support (CDS) systems have increasingly optimized care pathways. Integrating imaging findings into CDS remains a challenge due to unstructured radiology outputs. To evaluate the role of imaging-based decision support within an integrated clinicoradiological pathway for determining emergency department (ED) disposition using modality-based assessment. A computer-assisted reporting/decision support (CAR/DS) tool was developed to standardize chest radiograph and CT interpretations and integrated with a CDS system. 9,036 adult patients with suspected viral pneumonia presenting to the ED (07/2020 -12/2021) were analyzed as a use case. Associations between CAR/DS outputs and ED disposition were assessed using correlation and logistic regression. Agreement between radiograph- and CT-derived outputs was also examined. Most exams were negative [70.9% (6,408/9,036)] and 3.1% typical (276/9,036). Higher CAR/DS likelihood was independently associated with increased odds of admission [all p < 0.001; Groups: radiograph only (OR = 1.86), CT only (OR = 1.83), or both (CT OR = 1.97, radiograph OR = 1.71)]. Radiograph and CT outputs agreement was moderate (Spearman's ρ = 0.43, p < 0.001). Most negative radiographs were followed by a negative CT (60.7%, 389/642), only 23.8% (24/101) of typical radiographs had a subsequent typical CT; most typical radiographs were followed by an indeterminate (37.6%, 38/101) or atypical (28.7%, 29/101) CT. CAR/DS outputs integrated into CDS systems provide actionable information that independently predicts ED disposition. CT added value by excluding suspected pneumonia on radiographs. This exemplifies how imaging data can be standardized and seamlessly incorporated into broader decision pathways, with potential applicability well beyond pandemic-related use cases.

Gastrointestinal adenocarcinomas, including colorectal cancer (CRC) and gastroesophageal junction (GEJ) carcinoma, represent a significant global health burden. Recent advances in large-scale multi-omics profiling, particularly through The Cancer Genome Atlas (TCGA), have revealed the genetic heterogeneity and underlying biology of these tumors. Integrating molecular biomarkers with histopathology into routine practice guides classification, prognosis, and targeted interventions. In CRC, hypermutated subtypes-defined by microsatellite instability (MSI) or polymerase epsilon (POLE) mutations-demonstrate high tumor mutational burden (TMB) and robust response to immune checkpoint blockade. Alternatively, non-hypermutated tumors, driven by chromosomal instability, harbor recurrent alterations in RAS, BRAF, HER2, and NTRK, enabling biomarker-based stratification for targeted therapies. Exploratory markers such as PIK3CA mutations and TMB are being investigated, although their predictive value in microsatellite-stable CRC remains limited. Similarly, GEJ carcinomas can be classified into four molecular subgroups: Epstein-Barr virus (EBV)-associated, MSI, chromosomal instability, and genomically stable. Each subtype is defined by characteristic biology and carries distinct therapeutic implications, with actionable targets including HER2 amplification, PD-L1 expression, and claudin 18.2 (CLDN18.2). Established clinical biomarkers such as MSI, PD-L1, and HER2 are standard in precision oncology, while emerging markers like CLDN18.2, TMB, and KRAS G12C, extend the therapeutic landscape. Combining biomarker-driven immunotherapy and targeted approaches such as PD-1 blockade in MSI-H or EBV-positive tumors, HER2-directed therapy, and CLDN18.2 inhibition, has demonstrated a paradigm shift in the clinical management. This review highlights a pathologist-centered perspective on molecularly defined subgroups, actionable biomarkers, and evolving therapeutic paradigms in CRC and GEJ carcinoma, advancing precision oncology.

OBJECTIVE: CHWs are a key workforce to address health disparities and offer expertise in community engagement, health promotion, and system navigation. Academic Medical Institutions (AMIs) play a critical role in supporting CHW workforce development and training, yet a systematic review of how AMIs engage with CHWs has not been conducted.

METHODS: Literature was systematically searched in November 2022 and February 2024 from the following databases: PubMed, Web of Science, CINAHL, SocINDEX, and PsychInfo. Forward and backward citation searches in February 2025 identified an additional 64 articles. We reviewed 347 full-text articles, and 136 were included in the final sample.

RESULTS: CHW/AMI engagement was delineated by three, non-mutually exclusive categories: 1) intervention implementation/evaluation (n = 104); 2) workforce development (n = 32), and 3) community-based participatory research (CBPR) (n = 23). Intervention implementation and evaluation studies measured the effectiveness of CHWs in a variety of healthcare settings. Among intervention studies that assessed efficacy, 52 (79%) found that CHWs significantly improved at least one health outcome. In workforce development, AMIs developed specialized training for CHWs or incorporated CHWs into training for medical students and residents. In CBPR studies, CHWs contributed to recruitment, community engagement, needs assessment, data collection, and community expertise. However, CHWs were rarely included in the interpretation or dissemination of findings, or as authors.

CONCLUSIONS: CHWs contribute to AMI’s tripartite mission and preventive medicine efforts including addressing health disparities, improving patient outcomes and educating future doctors. Developing sustainable CHW career paths with equitable payment structures is essential to move from engagement to partnership.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-026-02770-w.