Citations

Abstract Background X‐ray Induced Acoustic Computed Tomography (XACT) is an emerging imaging technique that provides 3D volumetric images from a single projection by detecting X‐ray‐induced acoustic (XA) waves with ultrasound (US) transducers. Unlike MRI or CT, XACT enables real‐time 3D imaging without long acquisition times, 2D limitations, or motion artifacts from mechanical rotation. Furthermore, when integrated with conventional pulse‐echo US through time‐sequenced operation, XACT provides complementary soft‐tissue contrast, establishing it as a promising platform for dynamic interventional radiology (IR) guidance. Purpose This study aims to evaluate the feasibility of XACT as a novel imaging tool for IR, specifically for guiding needle placement and monitoring contrast agent dynamics. Methods We developed a 3D XACT imaging system equipped with a 256‐element 2D matrix array and a portable X‐ray tube to visualize needle insertion and contrast agent dynamics. To capture both needle position and soft tissue anatomy, we integrated a dual‐modality system combining XACT with pulse‐echo US using a 128‐element linear US array. Experiments were conducted on both tissue‐mimic phantoms and soft tissue samples. GPU‐accelerated algorithm has been developed for 3D XACT image reconstruction. Results The results demonstrate the 3D capabilities of XACT imaging for interventional procedures, including monitoring of needle placement and tracking of contrast agent dynamics. The imaging speed reached up to 3∼4 s per frame, constrained by the repetition rate of the X‐ray source and signal to noise ratio. The dual‐modality approach provided clear visualization of the needle's position and the surrounding soft tissue structures, achieving imaging resolution around 2 mm. Conclusions This study demonstrates that XACT imaging is feasible to be used for IR. Its 3D imaging capability with faster imaging speed would be an alternative to cone beam CT and its capability to combine US imaging will provide richer information for soft tissue

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Abstract Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT -associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro , and suppressed tumor growth in vivo , confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

Objective: This study aims to develop a contrast-free, high-sensitivity ultrasound method, denoted as Radon transform-based flow measurement (R-Flow), for in vivo mapping of microvascular flow vectors and for establishing R-Flow-derived vector-field metrics to noninvasively quantify microcirculatory patterns in liver cirrhosis. Impact Statement: R-Flow enables robust, contrast-free imaging of microvascular dynamics and demonstrates translational feasibility in the human liver. Its direction-aware indices offer pilot in vivo quantification of flow redistribution and remodeling, providing unique insights into hepatic flow dynamics. Introduction: Microvascular dysfunction is a hallmark of many diseases, yet noninvasive visualization and quantitative assessment of abnormal microcirculation remain limited. Methods: R-Flow leverages Radon transform to decode red blood cell dynamics from the spatiotemporal domain of ultrasound flow signals, reconstructing velocity vectors at microvascular scale. From these vector maps, unique direction-aware indices are further derived to characterize flow distribution and heterogeneity. Results: Validated across simulations, phantoms, and in vivo studies, R-Flow provides robust velocity estimation across a wide range (1 to 60 mm/s). Notably, it enables high-sensitivity microvascular flow vector mapping of human liver, showing strong agreement with references ( r > 0.9). In a rat model, these direction-aware indices revealed a shift from healthy multipath perfusion in control livers to directionally biased vascular pattern in cirrhotic livers, demonstrating significant correlation with pathological indicators. Conclusion: R-Flow enables noninvasive, contrast-free mapping of microvascular blood flow velocity and offers a promising approach for high-resolution assessment of microvascular flow characteristics.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Abstract Radiation therapy (RT) has long been included in the treatment of glioblastoma (GBM). However, radioresistance in cancer cells as well as toxicity in normal tissues are major obstacles to clinical efficacy. Improved understanding of the mechanisms of tumor microenvironment-induced radioresistance during and after radiation therapy can provide fundamental insights to improve clinical outcomes in GBM. Here, using three-dimensional engineered hydrogel models in vitro, we report the influence of extracellular matrix, hypoxia, and adjacent neuronal cells in radiotherapeutic sensitivity. We find that mechanical cues and oxygen availability regulate cellular response to radiation, with softer matrices allowing for more DNA damage. Hyaluronan fragments from the extracellular matrix also modulate rapid metabolic response to radiation, especially in hypoxic environments. We show that neuronal networks influence tumor metabolic activity and the inflammatory response. Overall, we demonstrate here that alternative radiation strategies, such as low dose rate radiation therapy and microenvironmental regulation, have the potential to be more effective in a specific subset of radiosensitive GBM tumors.

Immunotherapy has been effective in many cancer types but has failed in multiple clinical trials in prostate cancers, with the underlying mechanisms remaining largely unclear. Here, we demonstrate that androgen receptor pathway inhibitor (ARPI) plus irradiation (IR) triggered robust anticancer immunity in prostate cancers in both patients and mice. We show that androgen-activated AR suppressed innate immune signaling by inducing inhibitor of nuclear factor kappa-B kinase subunit epsilon ( IKBKE ) gene repression through HDAC2 interaction with an IKBKE enhancer RNA ( IKBKE eRNA, or IKBKE-e ). ARPI treatment caused IKBKE derepression and enhanced an IR-induced innate immune response via action of RIG-I and MDA5 dsRNA sensors. IKBKE-e ablation largely enhanced innate immunity in prostate cancer cells in culture and anticancer immunity in mice. Our results revealed AR, HDAC2, and IKBKE eRNA as critical intrinsic immune suppressors in prostate cancer cells, suggesting that rejuvenating inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKKε) signaling by targeting IKBKE-e is an actionable strategy to elicit synthetic anticancer immunity in immunologically “cold” cancers such as prostate cancer.

Facial pain disorders, most notoriously trigeminal neuralgia, cause substantial suffering, functional impairment, disability, and impaired quality of life. However, most pain medicine practitioners have had only limited education on the evaluation, diagnosis, and treatment of facial pain. Furthermore, this limited education often contains commonly held myths about facial pain diagnosis. These myths can result in misdiagnoses that lead to ineffective medications, interventions, or surgeries and prolonged pain. Therefore, the American Society of Regional Anesthesia and Pain Medicine’s Headache and Facial Pain Special Interest Group convened a multidisciplinary group of neurologists, anesthesiologists, and dentists to identify and dispel pervasive myths in facial pain diagnosis.