Citations

ABSTRACT Background The brain–heart axis is central to vascular health, yet no imaging biomarkers capture integrated dysfunction across neural and coronary microvascular networks. Although coronary microvascular dysfunction links to cognitive decline, neural correlates connecting cerebral efficiency with coronary physiology remain unclear. Objectives To determine whether the Unified Structural and Functional Connectivity (USFC)—a multimodal magnetic resonance imaging (MRI) “traffic map” of brain network efficiency—predicts coronary endothelial function and cognition in women with ischemia and no obstructive coronary artery disease (INOCA). Methods Thirty‐three women with suspected INOCA from the Women's Ischemia Syndrome Evaluation (WISE) study (NCT03876223) underwent invasive coronary function testing, cardiac MRI, cognitive evaluation, and multimodal brain MRI. USFC, structural connectivity (SC), and functional connectivity (FC) were computed for predefined 10 backbone pathways. Support vector regression and logistic classification assessed predictive performance. Results USFC explained 16%–20% more variance in coronary endothelial function, myocardial perfusion reserve, and cognition than SC or FC alone ( p < 0.05). Connectivity between the left caudate–superior medial orbital gyrus and right calcarine–inferior occipital gyrus emerged as robust predictors of crystallized cognition ( r = –0.78, p FDR < 0.05) and coronary endothelial function ( r = 0.70, p FDR < 0.05), respectively. USFC also best discriminated low versus high coronary blood flow (area under the ROC curve [AUC]: USFC 0.622 vs. SC 0.349 and FC 0.425; p < 0.05). Conclusions USFC identifies neuro–cardiac pathways linking cerebral efficiency with coronary endothelial function. These results introduce a sensitive biomarker of systemic vulnerability, highlighting occipital and frontostriatal pathways as shared substrates of dysfunction. USFC offers a mechanistic framework for detecting vascular risk across metabolically demanding tissues. Trial Registration ClinicalTrials.gov identifier: NCT03876223

ABSTRACT Cholangiocarcinoma (CCA) is a lethal biliary cancer in which chemoresistance is nearly universal, and its tumor immune microenvironment is dominated by immunosuppressive tumor-associated macrophages (TAMs) that exclude cytotoxic CD8 + T cells. How tumor cells sustain this immunosuppressive state during chemotherapy is undefined. Here we demonstrate that therapy-induced senescent-like (Sen-L) cancer cells accumulate after gemcitabine/cisplatin in human and murine CCA, are predominantly cancer cells, and predict shorter survival. Genetic elimination of Sen-L cancer cells reduces tumor burden, lowers TAM abundance, and restores intratumoral CD8 + T cells, establishing them as causal drivers. Growth differentiation factor 15 (GDF-15) is the dominant Sen-L-secreted factor and reprograms macrophages to suppress CD8 + T cells through the non-canonical receptor TGFBR2 and STAT6, and p16-restricted Gdf15 silencing phenocopies Sen-L elimination. Combined with chemotherapy, Sen-L elimination improves survival beyond chemotherapy alone. These findings establish Sen-L cancer cells and their GDF-15 output as causal, targetable drivers of macrophage-mediated immune evasion in CCA. SIGNIFICANCE STATEMENT Therapy-induced senescent-like cancer cells, not stromal cells, are the dominant senescent-like and immunosuppressive population in cholangiocarcinoma, and their elimination restores antitumor immunity. GDF-15 is their dominant secreted effector and engages a non-canonical macrophage receptor, TGFBR2, identifying a cancer-cell-to-macrophage axis and a Sen-L-elimination strategy to restore chemosensitivity. Abstract Figure Graphical abstract. Senescent-like CCA cells promote tumor immunosuppression through TAMs polarization by GDF-15

Importance Head and neck cancer (HNC) accounts for nearly 1 million new cases and approximately half a million deaths annually worldwide, representing a substantial global health burden. Despite an overall decline in mortality in many regions, patterns vary across anatomical subsites and countries. Objective To evaluate international and national mortality trends in HNC and its major anatomical subsites from 2001 to 2023. Design, Setting, and Participants This observational epidemiologic study analyzed population-based mortality data from the World Health Organization (WHO) Mortality Database for 73 countries between 2001 and 2023. Data collection was conducted in May 2025, and the data were analyzed between May and October 2025. Eligible countries had medium- to high-quality vital registration data. Age-standardized mortality rates (ASRs) were calculated using the new WHO World Standard Population. Country-specific trends from 2010 to 2023 were assessed using joinpoint regression among countries with at least 7 years of available data. Main Outcomes and Measures ASRs and average annual percentage change in HNC mortality overall and by anatomical subsite using population-based analysis. Results From 2001 to 2023, 2 000 066 HNC-related deaths (450 657 females; 1 549 409 males) were recorded. Internationally, ASRs decreased by 38.9%, with greater reductions among male individuals than among female individuals. Substantial divergence was observed across subsites: laryngeal cancer mortality decreased markedly (−50.1%), whereas increasing trends in oropharyngeal cancer mortality were observed in many countries in recent years, particularly in high-income countries such as the UK (average annual percentage change, 4.30%) and the US (average annual percentage change, 3.26%). Conclusions and Relevance Results of this study suggest that, although overall HNC-related ASRs have declined internationally, marked variation across subsites and countries persists. The contrasting trends between laryngeal and oropharyngeal cancers underscore the need for subtype-specific prevention strategies and continued global surveillance.

Abstract Roux‐en‐Y gastric bypass (RYGB) surgery is a highly effective technique for treating obesity. However, it may result in nutritional deficiencies. Here we report the case of a 55‐year‐old woman presenting with chronic diarrhea lasting 2 years, accompanied by lower limb paresthesia, macrocytic anemia, leukopenia, and thrombocytopenia. Her medical history included hypothyroidism treated with levothyroxine, and RYGB surgery performed in 2014, with intermittent micronutrient supplementation. The patient developed copper and zinc deficiencies. To our knowledge, this is the first report of a patient presenting with both deficiencies simultaneously with pancytopenia 10 years after RYGB. This case underscores the need to verify adherence to existing preoperative screening recommendations involving preoperative micronutrient measurements to assess micronutrient deficiency risks and multidisciplinary postoperative follow‐up, the importance of monitoring micronutrient levels, close follow‐up of both symptomatic and asymptomatic patients and evaluating adherence to supplementation regimens.

Abstract Objective Ceribell Inc.'s point‐of‐care electroencephalographic (EEG) system and artificial intelligence‐based Automated Seizure Burden Estimator (ASBE; ClarityPro) have US Food and Drug Administration clearance for diagnosing electrographic status epilepticus (ESE). The AccuRASE study using ASBE version 6 (V6) showed high negative predictive value (NPV) but limited sensitivity and positive predictive value (PPV) at certain thresholds. Version 8 (V8) is the updated algorithm trained with additional EEG samples. Methods We tested V8 on the previously used rapid‐response EEG test dataset (not used to train V8). Sensitivity, specificity, PPV, and NPV were compared against blinded expert annotations. ESE, ESE and possible ESE (ESE/pESE), electrographic seizures (ESz), and Esz with highly epileptiform patterns (Esz/HEPs) at burden thresholds of >1%, >10%, >20%, >50%, and >90% were analyzed. Additionally, we evaluated the built‐in ESE and ESE/pESE alerts (≥90% over 5 min, ≥20% over 1 h, or ≥10 continuous minutes of seizure). Results V8 showed 100% sensitivity at lower thresholds (>10%–20%) for ESE (V6 .86), without a specificity loss (V8 .9 vs. V6 .85), and retained NPV (V8 1.0 vs. V6 .99). Sensitivity at 50% threshold was .86 for ESE (V6 .71), and specificity was .94 (V6 .91); PPV for ESE was 30% for ESE and 80% for ESE/pESE. The specificity for ESE remained high for 90% burden (V8 .96, V6 .97) but the sensitivity dropped (.29 vs. V6 .43). Sensitivity was lower for ESz/HEPs (.54 → .44 at >10% threshold), although the algorithm is not designed to detect HEPs. Built‐in alert analysis showed significantly improved sensitivity for ESE (.57 → .86); specificity and NPV remained high (>.94). Significance ASBE V8 showed meaningful improvements in sensitivity, continued excellent NPV, and improved built‐in alert accuracy for ESE and ESE/pESE. The best seizure burden threshold for ruling out ESE now is 20% (vs. 10% with V6) and that for ruling in ESE/pESE with reasonable certainty is 50% (vs. 90% with V6).

Abstract Background An accurate discrete spot scanning (DSS) beam delivery time (BDT) model is essential, particularly as 4D dynamic dose accumulation calculations rely on machine‐specific time models for reliable interplay evaluation. Purpose To derive a BDT model for a synchrotron‐based Hitachi Probeat PBS proton system from oscilloscope measurements of key BDT structures, including multi‐energy extraction (MEE) characteristics. Methods Key BDT structures were characterized using oscilloscope measurements, supplemented by delivery log files where appropriate. Spot‐to‐spot scanning trajectories were visualized using a CROSSmini 2D strip ionization chamber (Liverage Biomedical Inc, Taiwan) via the dose‐driven continuous scanning (DDCS) mode. The MEE design and MEE charge recapture efficiencies were also characterized. In addition, the impact of the number of delivered spots on both the extractable fraction of charge and MEE recapture efficiency was investigated. Results An improved synchrotron‐based BDT model was derived that incorporates dead times between radiofrequency knockout (RFK) and high‐speed steering magnet (HSST) signals. Spot‐to‐spot movement follows a “hockey‐stick”‐like behavior where the effective spot‐to‐spot move time is governed by the slower axis. An energy‐dependent variation in the number of MEE layers deliverable per spill was also observed, with fewer MEE layers per spill (1–3) was observed in low and high energy ranges compared to the mid‐energy range (4–5), deviating from the nominal vendor value of five MEE layers per spill per block. A reduction in the extractable fraction of charge was observed with an increasing number of spots per spill, while increasing the number of delivered spots in the preceding layer significantly reduced the MEE recapture efficiency. Conclusion MEE characteristics should be incorporated into synchrotron‐based time models for accurate beam delivery modeling. The methodology presented here also provides a framework for centers seeking to derive machine‐specific delivery time models for their proton systems.

ABSTRACT Background & Aims Metabolic dysfunction‐associated steatotic liver disease (MASLD) is highly prevalent and increasingly recognized as a multisystem cardiometabolic disorder. The global burden, incidence and prognostic implications of coexisting chronic kidney disease (CKD) in MASLD remain uncertain. Methods In this meta‐analysis, we searched MEDLINE and EMBASE from inception through September 25, 2025, for observational studies reporting CKD prevalence and/or incidence among adults (≥ 18 years) with MASLD. The primary outcomes were pooled CKD prevalence, estimated using random‐effects models on the logit scale with the Hartung–Knapp adjustment, and pooled CKD incidence (per 1000 person‐years), estimated using random‐effects models with exact Poisson confidence intervals. Secondary outcomes included all‐cause mortality and cardiovascular, renal and cancer outcomes. Results From 2811 records, 36 observational studies met the inclusion criteria. Twenty‐three studies contributed prevalence estimates (575 615 participants; 56 248 CKD cases) and thirteen studies contributed incidence estimates (27 996 new events). The pooled global CKD prevalence among individuals with MASLD was 15.22% (95% CI 9.69–23.12). The pooled CKD incidence was 22.17 per 1000 person‐years (95% CI 11.44–32.89) in the MASLD population. Hypertension (OR 1.49, 95% CI 1.29–1.72), dyslipidaemia (OR 1.22, 95% CI 1.20–1.24) and diabetes (OR 1.94, 95% CI 1.69–2.22) were associated with higher odds of CKD in the MASLD population. All‐cause mortality rates were 18.28 per 1000 person‐years (95% CI 3.45–33.11) in coexistent MASLD and CKD, more than double those in the MASLD‐only population (7.26 per 1000 person‐years (95% CI 2.97–11.56)). Conclusions Coexistent CKD affects one in seven individuals with MASLD worldwide, conferring a more than two‐fold increased risk of all‐cause mortality compared with individuals with MASLD alone.

Objective Data on cardiac index (CI) in patients undergoing coronary angiography (CAG) can enhance decision-making and improve clinical outcomes. However, this necessitates an additional invasive procedure, and therefore, it is frequently omitted. This study explores the novel application of convolutional neural networks (CNNs) to predict CI from routine CAGs. Methods and analysis Patients who underwent CAG and simultaneous right heart catheterisation at Mayo Clinic between 2002 and 2023 were included. A three-dimensional (3D)-CNN model (X3D architecture) was developed to predict binary CI categories (normal: CI≥2.2 vs abnormal: CI<2.2 L/min/m²) from CAG videos. CAGs were randomly split into training (70%), validation (15%) and test (15%) sets. All available videos from each study were entered into the model. The model’s performance was evaluated using the area under the curve (AUC), sensitivity, specificity and F1 score. Results A total of 15 297 CAG studies were considered. After applying a quality-check algorithm to exclude low-quality or irrelevant CAGs, 11 475 studies from 9103 patients were included. CI was abnormal in 25.2% of studies. The model achieved an AUC of 0.83 (95% CI 0.81 to 0.85), sensitivity of 0.72 (95% CI 0.68 to 0.77), specificity of 0.78 (95% CI 0.75 to 0.80) and F1 score of 0.62 (95% CI 0.58 to 0.65) on the test dataset. Limiting the input to 4, 3 or 2 prespecified CAG videos representing standard views did not significantly affect the model’s performance, supporting its applicability in varied clinical workflows. Conclusions This study demonstrates the feasibility of leveraging deep learning models to predict CI from CAG videos. The proposed 3D-CNN model shows promise as a tool for providing physicians with real-time data to aid decision-making during CAG procedures.