Despite varying colorectal cancer (CRC) outcomes across distinct Asian American subgroups, Asian individuals are often assessed in aggregate. We performed disaggregated analyses to assess for heterogeneity in CRC outcomes across the six largest Asian origin groups in the United States. We identified adults with a primary diagnosis of CRC using surveillance, epidemiology, and end results (SEER) registry data from 2006 to 2020. Individuals from the six largest Asian origin groups and a referent non-Hispanic White (NHW) group were included. We identified predictors for regional or distant stage CRC at the time of diagnosis using multivariable logistic regression and predictors for CRC related death using multivariable Cox proportional hazards models. Our cohort included 25,379 Asian Americans, who identified as Chinese (6585 [25.9%]), Filipino (6399 [25.2%]), Japanese (4309 [17.0%]), Vietnamese (3047 [12.0%]), Korean (2964 [11.7%]), and Asian Indian/Pakistani (2075 [8.2%]), in addition to 234,938 NHW individuals. In our multivariable logistic regression, Vietnamese (aOR 1.23; 95% CI 1.14-1.32), Korean (aOR 1.20; 95% CI 1.11-1.30), and Asian Indian/Pakistani (aOR 1.10; 95% CI 1.01-1.21) individuals had increased odds of regional or distant stage at time of diagnosis compared to NHW individuals. In our multivariable Cox model, Chinese (aHR 0.92; 95% CI 0.88-0.97) and Asian Indian/Pakistani (aHR 0.88; 95% CI 0.80-0.97) individuals had decreased likelihood of CRC related death compared to NHW individuals. Lower neighborhood-level socioeconomic status by Yost Index was associated with worse CRC outcomes in both multivariable models. In this national cancer registry analysis, marked heterogeneity was observed in CRC stage and survival across disaggregated Asian American groups. Disaggregated analyses are key to accurately survey CRC trends, identify groups at highest risk, and develop culturally relevant approaches to CRC prevention and care.

Mounting evidence suggests that ferroptosis is closely involved in the development of severe acute pancreatitis (SAP). This study aimed to investigate the association between abnormal lipid metabolism and ferroptosis and elucidate the role of the Keap1/Nrf2/SLC7A11/GPX4 pathway in pancreatic acinar cell injury during SAP. Rats were divided into four experimental groups: sham, hyperlipidemia (HL), SAP, and HL-SAP. Sprague-Dawley rats were adopted to establish HL-SAP model through administering high-fat emulsions via gastric infusion for 14 consecutive days and sodium taurocholic injection. Measure serum amylase, blood lipid, and inflammatory cytokine levels, perform histological analysis, determine the expression levels of proteins in the Keap1/Nrf2/SLC7A11/GPX4 signaling pathway, and evaluate ferroptosis-related changes and oxidative stress. High-fat emulsion feeding successfully induced hyperlipidemia with elevated blood lipids, while injection of 3.5% sodium taurocholate triggered SAP accompanied by increased serum amylase. The combined intervention effectively established the HL-SAP model. Compared with the SAP rats, the HL-SAP rats exhibited more severe pancreatic damage (72h mortality: 80 vs. 50%, respectively, plus elevated amylase, inflammation, histopathology scores). Higher amylase levels, intensified inflammation, and increased histopathological scores were also observed in HL-SAP rats. Moreover, HL-SAP rats showed markedly enhanced oxidative stress and ferroptosis-related phenotypes, including increased MDA, ROS and Fe2⁺ levels, as well as decreased GSH and SOD levels. In both SAP and HL-SAP groups, abnormal lipid metabolism was associated with downregulated Nrf2, SLC7A11, and GPX4 expression and upregulated Keap1 expression, and these alterations were more prominent in the HL-SAP group. Abnormal lipid metabolism aggravates oxidative stress, inflammation, and pancreatic acinar cell injury in SAP, and promotes ferroptosis. These effects may be attributed to reduced activity of the Keap1/Nrf2/SLC7A11/GPX4 pathway.

Intestinal ultrasound (IUS) has been shown to be an accurate, non-invasive, point-of-care tool for monitoring disease activity in inflammatory bowel disease (IBD). While its diagnostic performance is well-established, there is a notable gap in research related to patient perspectives regarding the role of IUS in IBD management, including its impact on the patient-provider relationship, which has not been previously studied. We conducted a quantitative study with a 21-question Likert scale (0-10) survey administered to patients undergoing IUS during routine IBD clinic visits. Survey questions assessed four domains: (1) patient experience with IUS, (2) preference of IUS versus other diagnostic modalities, (3) impact on the patient-provider relationship, and (4) the enhancement of disease understanding. Fifty-seven patients completed the survey. Overall, patients reported high satisfaction with IUS (mean score: 9.15), preferred IUS over bloodwork, stool testing, CT/MRI, and colonoscopy (mean score: 7.64), and felt it positively impacted patient-provider relationship (mean score: 8.07), and disease understanding (mean score: 7.74). IUS was strongly preferred over stool testing and colonoscopy (mean scores: 10 for both). Patients on steroids reported greater enhancement of the patient-provider relationship (8.70 vs. 7.82, p = 0.03), and those with Crohn's disease reported greater improvement in disease understanding compared to ulcerative colitis (8.16 vs. 7.18, p = 0.04). Patients expressed high satisfaction with IUS across all survey domains. These findings support the broader use of IUS into IBD care, highlighting its value not only as a diagnostic tool but also in enhancing patient experience and engagement.

The immunosuppressive tumor microenvironment in hepatocellular carcinoma (HCC) limits therapeutic efficacy. This study aimed to develop an immune-related signature for risk stratification and to identify key molecules that drive immune evasion. Transcriptomic data from HCC were analyzed to identify immune-related genes. A prognostic risk signature was constructed using Cox and least absolute shrinkage and selection operator regression and validated in an independent external cohort. Immune cell infiltration, immune checkpoints, and interleukin (IL)-17RA expression were characterized. Serum IL-17 levels were measured in patients with HCC, patients with cirrhosis, and healthy controls. Mechanistic studies included analyses of cell proliferation, programmed death-ligand 1 (PD-L1) expression, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation, and the effects of IL-17RA knockdown. A six-gene risk signature stratified patients into high- and low-risk groups. The high-risk group exhibited poorer prognosis and a more immunosuppressive microenvironment. External validation confirmed model robustness, with the risk score as an independent prognostic factor (multivariable Cox, P < 0.05). IL-17RA was overexpressed in HCC and correlated with poor prognosis, immune checkpoints, and M2 macrophage infiltration. Serum IL-17 levels were elevated in patients with HCC, had diagnostic value (area under the curve [AUC] = 0.738), and predicted vascular invasion (AUC = 0.891). Mechanistically, IL-17 activated NF-κB, increased p-p65, induced p65 nuclear translocation, upregulated PD-L1, and stimulated HCC cell proliferation. Pharmacological NF-κB inhibition or genetic silencing of IL-17RA abrogated IL-17-induced PD-L1 upregulation. The six-gene prognostic signature and external validation suggest clinical utility. The IL-17/ IL-17 receptor alpha axis may drive immune escape by upregulating PD-L1 via NF-κB activation; therefore, it represents a potential prognostic biomarker and therapeutic target.

Acute severe ulcerative colitis (ASUC) is a life-threatening medical emergency necessitating immediate hospitalization and rapid initiation of therapy. For decades, intravenous (IV) corticosteroids have remained first-line, with rescue therapies reserved for patients who are corticosteroid-refractory. However, the therapeutic landscape has evolved, shifting toward earlier initiation of advanced therapy. This study analyzes factors influencing patient decision-making for treatment of ASUC. In this prospective, mixed-methods study, patients with ASUC who were infliximab and Janus kinase inhibitor naïve were enrolled. Participants were presented with educational resources on IV corticosteroids, upadacitinib, and infliximab and then asked to choose between continuing IV corticosteroids alone or starting early initiation of advanced therapy in addition to IV corticosteroids. Factors influencing their preferences were captured via semi-structured interviews, and clinical outcomes were followed. Eleven patients were prospectively enrolled. Analysis of semi-structured interviews revealed five dominant themes influencing treatment decision-making: (1) reliance on physician expertise, (2) contextualized treatment selection, (3) concrete risk assessment, (4) empathetic individualization, and (5) strong preference for oral therapy. Notably, we observed significant discordance between patient treatment preferences and medication initiated following discussion between the participant and their primary clinical team, particularly among participants favoring upadacitinib. These five principal themes offer actionable opportunities to assist patients in making well-informed decisions regarding ASUC therapy. Clinicians should utilize these insights to develop evidence-based patient decision aids that specifically address patient priorities. While patients often prioritize oral therapy, their choices often diverge from the therapy initiated after discussing with their primary clinical team. Future efforts should focus on bridging this discordance by integrating patient priorities with available evidence into the clinical decision-making process.

Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether noninvasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of noninvasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only noninvasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naïve Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps 10mm in size, and/or the presence of 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015 to 2022 used for internal validation and 1,562 patients from 2023 to 2024 used for external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall area under the curve (PR-AUC), accuracy, precision, recall, and F1 score. Model interpretability and feature contribution were assessed using SHapley Additive exPlanations (SHAP). Overall predictive performance was moderate using noninvasive pre-colonoscopy features. The neural network demonstrated the strongest overall discrimination, achieving the highest internal validation performance (ROC-AUC 0.78, PR-AUC 0.75, accuracy 0.72), but showed reduced performance in the external cohort (ROC-AUC 0.67, accuracy 0.66), suggesting potential overfitting or temporal feature drift. In contrast, simpler models including Naïve Bayes, SVM, and XGBoost exhibited lower internal performance (ROC-AUC 0.54-0.59) but more stable generalization to the external cohort (ROC-AUC 0.52-0.63; accuracy approximately 0.53-0.60). Model interpretability analysis using SHAP identified age, smoking status, sex, occupation, race, colonoscopy indication, and family history of colorectal cancer as the most influential predictors, highlighting contributions from both traditional clinical and sociodemographic factors. Prediction of HRP using routine pre-colonoscopy data is feasible but demonstrates limited generalizability across cohorts. These findings highlight the clinical potential and limitations of pre-procedural risk modeling, especially in diverse, underserved populations. Integration of additional data modalities may be required to achieve clinically robust and equitable prediction tools.