Citations

Background Carotid artery calcification represents a common feature of atherosclerotic plaques. However, the geometric relationships of calcific clusters have not been systematically investigated. This study aimed to develop a geometry-based atlas, independent of symptom status, characterizing the three-dimensional properties of calcium clusters within carotid plaques and quantifying population-level distributions and data-driven couplings between size, shape, and density. Methods Standardized three-dimensional calcium masks of clinical CT scans were analyzed using connected-component labeling. Extracted features included cluster volume, aspect ratio, eccentricity, compactness, and CT attenuation values (mean μ, standard deviation σ). Associations between cluster features were assessed using Spearman correlations with Benjamini–Hochberg false discovery rate correction. Independent associations were determined using partial Spearman correlation and restricted cubic spline regression. Results Among 107 plaques, 149 distinct clusters were identified, most frequently presenting as a single elongated cluster (aspect ratio = 2.23). Independent associations (all p < 0.001) were identified for: eccentricity with μ (ρ = −0.39), volume with μ (ρ = 0.47), compactness with μ (ρ = 0.39), and volume with eccentricity (ρ = 0.82). Multi-cluster plaques exhibited smaller mean cluster volume (76.1 vs 359.5 mm 3 ; p < 0.001) and lower eccentricity (0.5 vs 1.6; p < 0.001) compared with single-cluster plaques. Conclusion Carotid calcification most frequently manifests as a single elongated cluster, with robust couplings between cluster size, shape, and density. These geometric archetypes provide a quantifiable framework for future biomechanical and biological studies of plaque vulnerability.

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.

Inflammatory bowel disease (IBD) is driven by a breakdown in immune regulation and epithelial barrier function, yet the contribution of eosinophils to this process has remained poorly defined and controversial. While eosinophils infiltrate the intestinal mucosa during both flares and remission, their role in shaping disease outcomes is unclear. Our RNA-seq analyses of colonic eosinophils isolated from dextran sulfate sodium (DSS)-treated mice revealed a significant upregulation of cyclooxygenase (COX)-2 (gene name, Ptgs2 ). Eosinophil-specific deletion of COX-2 (Ptgs2 fl/fl eoCre +/− ) reduced IL-22 production and exacerbated DSS- and trinitrobenzene sulfonic acid (TNBS)-induced colitis, characterized by greater weight loss, higher disease activity, colon shortening, and epithelial injury. Administration of recombinant IL-22 reversed these phenotypes. Mechanistically, eosinophil-derived COX-2 enhanced IL-22 production by type 3 Innate lymphoid cells (ILC3s) through prostaglandin E2 (PGE 2 ) signaling. Consistently, Ptgs2 fl/fl eoCre +/− mice exhibited reduced colonic PGE 2 levels, while PGE 2 analog treatment restored IL-22 production and mucosal protection. Our findings identify eosinophil-derived COX-2 and PGE 2 as a critical regulator of IL-22 production during colitis, uncovering a eosinophil–ILC3 cross talk that safeguards the intestinal barrier and represents a promising therapeutic target in IBD.

Purpose: To evaluate the impact of PSMA PET versus conventional imaging for staging, and the effect of dose escalation (DE) to metastatic lymph nodes in cN1 prostate cancer patients treated with EBRT and ADT. Methods: A retrospective multicenter analysis included 197 propensity score-matched patients (77 staged with PSMA PET, 120 with conventional imaging) treated with EBRT and ADT, with or without DE to lymph node metastases. Matching criteria were T stage, nodal involvement, imaging modality, Gleason score, and ADT duration. Primary endpoint was metastasis-free survival (MFS); secondary endpoints included overall survival (OS), locoregional control (LRC), and toxicity. Results: Median follow-up was 42 months (IQR: 25–59). Median EQD2 (alpha/beta=1.5) to elective nodes was 46 Gy, and to metastatic nodes, 66 Gy (PSMA PET) and 63 Gy (conventional imaging). The 3-year MFS was 80.5% (95% CI: 71.3–90.8%) for PSMA PET and 77.2% (95% CI: 69.5–85.8%) for conventional imaging; 5-year MFS was 59.3% versus 65.8% ( P =0.57). OS at 5 years was 82.2% (PSMA PET) and 77.4% (conventional imaging) ( P =0.72). On multivariable analysis, ISUP grade group 3–5 predicted earlier metastasis (HR: 4.03, 95% CI: 1.61–10.06; P =0.003); higher prostate dose (EQD2 ≥113 Gy) improved MFS (HR: 0.53, 95% CI: 0.30–0.95; P =0.033); long-term ADT (≥24 mo) improved OS (HR: 0.38, 95% CI: 0.19–0.78; P =0.009). No significant difference in toxicity was observed between groups. Conclusions: PSMA PET staging and nodal dose escalation >60 Gy were safe but did not improve MFS, OS, or LRC. ISUP grade group 3–5 and higher prostate dose significantly influenced outcomes.

Background Grade group 5 (GG5) prostate cancer (PCa) carries a less favorable prognosis after standard-of-care (SOC) therapy, necessitating novel therapeutic approaches. High-dose rate brachytherapy (HDRBT) and androgen deprivation therapy (ADT) may modulate immune response in GG5 PCa, particularly in tumors with increased immune content. This study evaluated whether the addition of nivolumab to SOC was associated with improved disease control in patients with high-volume GG5 PCa, including those with oligometastatic disease. Methods In this non-randomized phase II trial, 31 patients with localized or oligometastatic GG5 PCa and >30% positive biopsy cores were evaluated between September 2018 and April 2021. Patients received four doses of nivolumab (240 mg every 2 weeks) beginning 4 weeks prior to HDRBT, alongside ADT, HDRBT, and external beam radiation. The primary endpoint was to evaluate whether the 2-year freedom from biochemical recurrence (FFBR) rate would exceed a prespecified historical control rate of 75%. Results Among the 31 patients, the median follow-up was 38.8 months (IQR 31.0–46.5 months). The addition of nivolumab to SOC RT with ADT was associated with a 2-year FFBR rate of 90.3% (95% CI 74.3% to 98.0%) (median FFBR not reached), exceeding the prespecified historical control rate of 75% (one-sided p value from binomial test=0.024). Definitive and probable nivolumab-related toxicity included 6.3% acute grade 2 and 6.3% acute grade 3 adverse events (AEs), with no grade 4+ AEs observed. A higher Decipher immunosuppression score at diagnosis correlated with early pathologic response (p=0.005) and was independently associated with time to metastatic failure (p=0.044). Conclusions Nivolumab combined with SOC was associated with encouraging FFBR in this high-risk GG5 PCa population and may represent a promising therapeutic intensification strategy. The Decipher immunosuppression score may serve as a predictive biomarker for response. These findings warrant further investigation in randomized trials.

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.