Citations

Introduction/Objective: Immune checkpoint inhibitors (ICIs) have limited efficacy in prostate cancer. Radiation therapy (RT) combined with ICIs and PI3K inhibition may enhance anti-tumor immune activity. Methods: RT-induced cytosolic double-stranded DNA (dsDNA) was measured in human and mouse prostate cancer cell lines as the surrogate upstream marker consistent with cGAS–STING engagement. In a syngeneic mouse model for castration-resistant prostate cancer (Myc-CaP in FVB mice), triple therapies were studied: (1) anti-CTLA-4, anti-PD-1, and RT, and (2) PIM kinase inhibitor PIM447, anti-PD-1, and RT. Mass cytometry (CyTOF) was used to measure the tumor-immune profile. Results: The peak induction of cytosolic dsDNA was at an RT dose of 13 Gy. In the mouse model, triple therapy with anti-CTLA-4, anti-PD-1, and RT doubled the median survival compared to monotherapy (32 days vs 11 to 22 days, p<0.006). Triple therapy with the PIM kinase inhibitor PIM447, anti-PD-1, and RT nearly tripled the median survival compared to PIM447 monotherapy (82 days vs 29 days, p=0.002). Mass cytometry analysis revealed that the combination of anti-PD-1 and RT reduced myeloid-derived suppressor cells and tissue-associated macrophages and enhanced CD8+ T-cell infiltration. Discussion: Although prostate cancer is an immunocold entity, RT can trigger immune activation, consistent with engagement of the cGAS–STING signaling pathway (cytosolic dsDNA serving as a surrogate upstream marker). In triple therapy, RT can enhance the efficacy of ICI and PI3K-targeted drug therapy, significantly improving overall survival in a mouse model of CRPC. Conclusions: A combination of RT, ICIs, and PIM kinase inhibition may help overcome immune resistance in prostate cancer. This combination therapy approach supports further preclinical validation and careful clinical evaluation and warrants further clinical investigation to optimize treatment strategies for CRPC.

Abstract Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional brain regions. Yet, many neuroanatomical boundaries remain invisible to current imaging and electrophysiology methods, limiting precision and contributing to suboptimal patient outcomes. Here, we introduce a self-supervised artificial intelligence (AI) framework that learns to delineate functional subregions directly from the spectral content of intraoperative local field potential (LFP) recordings, without the need for predefined biomarkers or anatomical labels. The framework identifies physiologic structure across the full spectrum of the signal and, through explainable AI (XAI), reveals the specific frequency components underlying these distinctions. Validated in the subthalamic nucleus (STN), the model aligned with clinically defined borders and rediscovered known beta oscillations. Applied to the motor thalamus in tremor patients, it consistently identified functional transitions corresponding to the ventral oralis posterior (Vop) and ventral intermediate (Vim) nuclei—regions where conventional methods fail to provide reliable boundaries. To assess clinical relevance, physiologically defined clusters were functionally evaluated using monopolar review data at their first DBS clinic postoperative visit, demonstrating distinct stimulation-response profiles across clusters and linking electrophysiologic segmentation to clinically meaningful programming outcomes. These findings demonstrate that intraoperative LFP recordings can be transformed into both a real-time guidance resource and a data-driven platform for biomarker discovery, establishing a foundation for more precise, individualized neuromodulation therapies and advancing our understanding of functional brain organization.

Abstract The intestinal epithelium harbors a large population of microbiota-dependent CD8αβ + T cells whose antigen specificity and regulation are ill-defined. By identifying MHCIa-restricted TCRs and generating tetramers against the gut commensal Segmented Filamentous Bacteria, we demonstrate that a single commensal species drives a clonally expanded, antigen-specific CD8αβ + T cell within the intraepithelial lymphocyte compartment. Mechanistically, the intestinal epithelium coordinates coincident signals governing this population: peptide:MHC-dependent TCR engagement drives pIEL accumulation, while αvβ6-mediated TGFβ activation restraints effector cell differentiation. Perturbation of epithelial cell-mediated TGFβ activation diverts commensal-specific CD8 + T cells toward inflammatory differentiation states transcriptionally convergent with those observed in ulcerative colitis. The intestinal epithelium thus functions as a dual-signal organizer of commensal-specific CD8 + T cell responses, coupling differentiation to restraint through spatially coincident molecular cues.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Abstract Background Postoperative spinal implant infections (PSIIs) are a serious complication of instrumented spinal fusion. Although debridement with implant retention is standard for early infection, the optimal duration of antimicrobial therapy remains uncertain. Methods We conducted a multicenter retrospective cohort study across 10 US academic centers, including adults with early PSII (≤90 days after fusion surgery) from 2017 to 2021. We excluded patients who experienced treatment failure within 6 weeks of debridement. The primary outcome was treatment failure. Antibiotic therapy effect was analyzed as a time-varying exposure using adjusted Cox proportional hazards models. Secondary analyses included 12-week landmark and clone-censor-weighted models to compare durations ≤ 12 weeks versus &gt;12 weeks. Results A total of 116 patients out of 499 (23.2%) experienced treatment failure during a median follow-up of 3.2 years. Among patients who remain failure-free 6 weeks from debridement, being on antibiotics was not associated with treatment failure (adjusted hazard ratio [aHR] 0.71; 95% CI .48–1.07; P = .10). In a 12-week landmark analysis limited to 359 patients who were not on indefinite suppressive antibiotic therapy, treatment durations &gt; 12 weeks did not significantly reduce the risk of failure (aHR 1.01; 95% CI .62–1.64; P = .99). Results were consistent in the clone-censor-weight sensitivity analysis (aHR 0.85; 95% CI .62–1.16; P = .30). Conclusions Prolonged antibiotic therapy was not associated with improved outcomes in early PSII managed with debridement and implant retention. These results support shorter, individualized antibiotic courses in patients with adequate surgical source control.

Background: Mindfulness-based interventions may improve cardiovascular health (CVH) by supporting behavioral change across multiple risk factors. This study evaluated the impact of Mindfulness-Based Blood Pressure Reduction (MB-BP), a mindfulness program targeting hypertension-related behaviors ( Image 1 ), on CVH using the American Heart Association’s Life’s Essential 8 framework. Methods: This secondary analysis of parallel-group, phase 2 randomized clinical trial evaluated the effects of MB-BP on CVH in 201 participants with elevated office BP (≥120/80 mmHg). The MB-BP group (n=101) received an 8-week program focused on mindfulness training and education targeting diet, physical activity, medication adherence, alcohol use, and stress, whereas the control group (n=100) received enhanced usual care ( Image 2 ). CVH was assessed using available Life’s Essential 8 components: systolic blood pressure, body mass index (BMI), diet (DASH adherence), physical activity, smoking, and sleep duration. Generalized estimating equations evaluated intervention effects through six months. Results: At six months follow-up, MB-BP participants significantly improved composite CVH scores compared to controls (standardized mean difference: 0.144; 95% CI: 0.023, 0.266) ( Image 3 ). MB-BP influenced most CVH components in healthier directions at trend levels, with the strongest responses for physical activity (47.9 MET min/week; 95% CI: -16.5, 112.3), sleep (0.34 hours/night; 95% CI: -0.10, 0.78), systolic blood pressure (-4.95 mmHg; 95% CI: -10.34, 0.44) and DASH diet score (0.27, 95% CI: -0.15, 0.69). Conclusions: MB-BP led to modest but clinically significant improvements in CVH, driven by multiple Life’s Essential 8 components. These findings suggest that MB-BP may be an effective behavioral intervention to support CVH and reduce risk for cardiovascular disease. ClinicalTrials.gov Preregistration Identifiers: NCT03256890, NCT03859076

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.

Abstract Background The IEC exposure index (EI), deviation index (DI), and target exposure index (EI T ), represent critical standardized metrics for the evaluation of exposure and quality in radiographic imaging. Purpose This work develops and validates a systematic procedure to estimate the EI T for eight of the most common radiography imaging protocols utilizing automatic exposure control (AEC) from measurements acquired under reference conditions. Methods A model was developed to define the relationship between a systems AEC logic, and an estimation of the EI under flat‐field conditions (EIFFC). Separately, clinical data and acquisition protocol information for resultant EI during patient studies were also collected for the eight protocols studied: Chest posteroanterior (PA), Chest lateral, Abdomen anteroposterior (AP), Pelvis AP, L‐Spine AP, C‐Spine AP, T‐Spine AP, and Ribs AP. Data were collected from 41 x‐ray units spanning seven institutions. For each protocol on each unit the EIFFC was computed based on the acquisition protocol, as well as median EI from clinical exams to produce a scaling factor (SF). Kruskal–Wallis statistical tests were used to compare SF's between vendors and AEC cell configurations. Results SFs for eight radiographic imaging protocols have been produced per vendor and per AEC cell selection. A workflow has been established for end‐users to follow to apply these SFs to flat‐field measurements taken at their own locations to establish local EI T . Conclusions The study results show that in seven of the eight imaging protocols, the SFs for most units included in the study report SFs within 1 DI (± 25%) of their respective final vendor reported SF (218/228). The ribs protocol is the exception to this finding (n = 26). SFs have high utility for establishing EI T values on individual x‐ray units and normalizing EI value distributions for quality assurance purposes.

Background Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor rate of survival. Objective To develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework to identify subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. Designs REDMOD was trained on a multi-institutional cohort (n=969; 156 pre-diagnostic, 813 control) and tested on an independent set (n=493; 63 pre-diagnostic, 430 control), simulating a low prevalence (~1:6) early detection paradigm. The fully automated framework couples AI-driven segmentation with a heterogeneous ensemble architecture trained on a 40-feature radiomic signature derived from Synthetic Minority Over-sampling Technique (SMOTE)-balanced data. A tunable Youden Index-optimised classification threshold enables performance calibration without retraining. Validation included direct comparison with radiologists, longitudinal test–retest analysis and external specificity validation across two independent cohorts (n=539 and n=80). Results On an independent test set (n=493), REDMOD identified occult PDA (AUC 0.82; 73.0% sensitivity) at a median 475-day lead time. This represented nearly twofold higher sensitivity than radiologists (38.9%; p<0.001), which grew to nearly threefold (68.0% vs 23.0%) at >24 months lead time. REDMOD showed strong longitudinal stability (90–92% concordance) and generalisable specificity across multi-institutional (81.3%; n=539) and public (87.5%; n=80) datasets. Mechanistic analyses confirmed predictive power derived principally from multi-scale wavelet-filtered textural features (90% of selected signature), which outperformed unfiltered features (AUC 0.82 vs 0.74; p=0.007) in capturing subvisual architectural disruptions. Conclusions REDMOD is an automated, mechanistically grounded, longitudinally stable, externally validated AI that surpasses radiologists for PDA detection at its visually occult pre-diagnostic stage. These attributes position it for prospective validation in high-risk cohorts, a necessary step towards shifting the paradigm from late-stage symptomatic diagnosis to proactive pre-clinical interception.

ABSTRACT Aims To evaluate the real‐world associations between semaglutide 2.4 mg and cardiometabolic comorbidities, biomarkers and cardiovascular risk among adults with overweight or obesity. Methods This retrospective cohort study used US claims data and laboratory measurements from the Komodo Research Database (2016–2024). Adults with obesity or with overweight and ≥ 1 obesity‐related comorbidity were included. Patients on semaglutide 2.4 mg for ≥ 12 months were compared with non‐users (including users of other obesity medications) using propensity score weighting with residual imbalances adjusted in regression model. Outcomes included time to onset of cardiometabolic comorbidities, change in cardiometabolic biomarkers and changes in 10‐year ASCVD risk score at 12 months. Results 25 783 semaglutide 2.4 mg users and 8 948 913 non‐users were identified. Semaglutide 2.4 mg was associated with significantly lower risks for incident prediabetes or type 2 diabetes, type 2 diabetes only, hypertension, ASCVD, heart failure and CVD (HRs 0.45–0.75; all p < 0.05), as well as greater improvements in BMI (−4.1 kg/m 2 ), SBP (−6.2 mmHg), HbA1c (−0.4%), LDL cholesterol (−7.7 mg/dL), triglycerides (−28.6 mg/dL) and HDL cholesterol (+1.9 mg/dL; all p < 0.001). At 12 months, semaglutide 2.4 mg users ( N = 161) demonstrated a greater reduction in ASCVD risk score (−0.7% vs. +0.3%; between‐group difference −1.0%, p < 0.001), with a decrease in the proportion at intermediate‐high ASCVD risk (14.9%–9.3% vs. 17.7%–18.7%; OR 0.3 [95% CI: 0.1, 0.6], p < 0.001). Conclusions Semaglutide 2.4 mg was associated with lower risk of multiple cardiometabolic comorbidities, favourable changes in biomarkers, and lower estimated ASCVD risk in this population.