Citations

Abstract Glioblastoma (GBM) remains the most lethal primary brain cancer due to its remarkable metabolic plasticity and therapeutic resistance. Here, we identify cholesterol dependency as a therapeutically exploitable vulnerability in GBM using two FDA-approved drugs: the H1 histamine antagonist clemastine and the retinoid X receptor agonist bexarotene. Combined treatment induces potent synergistic anti-tumor activity across patient-derived glioma models, suppressing proliferation, stemness, and survival at sub-IC50 concentrations. Mechanistically, this therapy disrupts cholesterol biosynthesis, transport, and homeostasis, triggering endoplasmic reticulum stress and activation of the unfolded protein response, ultimately leading to autophagy and apoptotic cell death. Orthotopic patient-derived glioma models recapitulate these mechanisms in vivo, where local intracranial administration significantly reduces tumor progression and prolongs survival using fourfold lower doses than systemic intraperitoneal delivery. Single-cell RNA sequencing revealed activation of regeneration and plasticity programs, accompanied by immune microenvironment remodeling and enhanced inflammatory signaling. Importantly, syngeneic models preserved immune cell composition, supporting future integration with immunotherapeutic strategies. Together, these findings establish cholesterol dysregulation–induced metabolic collapse as a promising therapeutic approach for GBM.

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.

ABSTRACT Chemotherapy-induced peripheral neuropathy (CIPN) is a common, debilitating complication of cancer therapy. Although axonal degeneration defines CIPN, the initiating cellular events remain unknown. Here, we show that CIPN is initiated by senescent peripheral fibroblasts rather than by the neuron itself. Across the mechanistically distinct chemotherapeutic agents paclitaxel and cisplatin, chemotherapy-induced senescence is unexpectedly restricted to peripheral fibroblasts rather than neurons and acts upstream of neuronal SARM1 activation. Genetic or pharmacological ablation of senescent fibroblasts prevents neuropathy and reverses established disease, demonstrating that these cells are required for both disease initiation and maintenance. Mechanistically, senescent fibroblasts drive neuropathic injury through an MK2-dependent senescence-associated secretory phenotype (SASP), and genetic or pharmacological inhibition of MK2 suppresses the SASP, preserves peripheral innervation and restores sensory function. Together, these findings redefine the cellular origin of CIPN and identify MK2-dependent fibroblast senescence as a therapeutic target.

Abstract Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20–30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq®, BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84–1.0) for identifying engraftment fevers with a mean AUROC of 0.88 ± 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning–based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.

Abstract Introduction Colony stimulating factor 1 receptor (CSF1R)-related leukoencephalopathy is an early onset neurodegenerative disease characterized by microglial deficits, abnormal glial responses, white matter pathology, and severe motor and cognitive decline. Methods At 8 months, mice lacking two copies of the Csf1r gene Fms-intronic regulatory element (FIRE) recapitulate some of the brain pathological features of CSF1R-related leukoencephalopathy, namely the deficits in microglia, astrogliosis, and demyelination. Of note, we detected deficits in lymphatic vasculature and altered macrophage activation in the meninges of Csf1rΔFIRE/ΔFIRE mice by flow, prior to the appearance of brain gliosis, neuronal dystrophy, and fine motor impairment. Results Eliminating the remainder CSF1R-expressing innate immune cells, including border-associated macrophages (BAMs), with PLX5622 at 2 months reshaped the immune crosstalk at the meningeal dura and restored lymphatic vascular expansion in Csf1rΔFIRE/ΔFIRE mice Conclusion Ongoing experiments in the lab will clarify the exact mechanisms through which dysfunctional innate immunity at the brain-meningeal interface and lack of meningeal lymphatic vasculature contribute to the development of brain pathology and impaired motor behavior in this model of CSF1R-related leukoencephalopathy. This work has unveiled novel molecular insights involving the meningeal lymphatic system that might be of therapeutic relevance to canonical central nervous system white matter diseases. Funding Source n/a Topic Categories Neuroimmunology (NEUR)

Background Subjective cognitive decline (SCD) is a preclinical condition associated with an increased risk of dementia and Alzheimer’s disease, with no consensus regarding effective therapeutic interventions to date. As the global burden of aging-related neurodegenerative diseases continues to rise, early intervention has become increasingly critical. Cognitive training has been proposed as a promising behavioral intervention; however, few studies have integrated multimodal assessments to elucidate its underlying neurobiological and psychological mechanisms, particularly within the educational system that shapes cognitive reserve and learning strategies across the lifespan. This pilot study aims to estimate effect sizes for future definitive trials, assess the feasibility and acceptability of the intervention and multimodal evaluations, and generate hypotheses regarding the relationship between recitation practice and clinical biomarkers. Methods A total of 60 individuals with subjective cognitive decline will be enrolled in a randomized, assessor-blinded, controlled pilot study. Participants will be randomly assigned to either an intervention group or a non-active control group prior to baseline assessment. The intervention group will undergo a six-month structured recitation training program. Both groups will undergo short-term follow-up after the intervention, and long-term follow-up annually post-intervention. Multimodal assessments will include neuropsychological testing, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), blood biomarker profiling, gut microbiota analysis, and fecal metabolomics. Discussion This study protocol outlines a randomized, assessor-blinded, controlled pilot study designed to advance understanding of the feasibility of Chinese Classics recitation training on both physiological and psychological outcomes in individuals with subjective cognitive decline. The findings are expected to provide mechanistic insights into non-pharmacological interventions and to address the current underrepresentation of Asian populations in neuroscience research. Clinical trial registration We registered this randomized controlled trial on ClinicalTrials.gov on March 11, 2026, under the identifier NCT07463391.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Background Cardiac magnetic resonance elastography (MRE) allows the non-invasive quantification of myocardial stiffness, but long-interval reproducibility data at 3.0 T single-frequency 140-Hz remain limited. Purpose To evaluate the short- and long-interval reproducibility of 3.0 T cardiac MRE for left ventricular (LV) myocardial stiffness across healthy volunteers and patients with conditions associated with elevated myocardial stiffness. Materials and Methods This prospective single-center study included 69 participants: 60 healthy volunteers and 9 with cardiovascular disease (6 with aortic stenosis and 3 with long-standing hypertension). All participants underwent baseline cardiac MRE (scanner: Signa Premier 3.0 T, GE Healthcare, driver: Resoundant), an immediate short-interval rescan (SIR) within 5 minutes, and a long-interval rescan (LIR) after approximately 1 month. Left ventricular myocardial stiffness and octahedral shear strain signal-to-noise ratio were measured independently by 2 readers. Reproducibility was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICCs), within-subject coefficient of variation, standard error of measurement, and smallest detectable change at the 95% confidence (SDC95) level. Results Baseline LV myocardial stiffness was higher in the disease group than in healthy volunteers, 11.73 ± 1.01 kPa and 7.80 ± 0.70 kPa (mean ± SD; P < .001), respectively. Inter-reader agreement was excellent across all timepoints, with ICCs of 0.970 at baseline (95% CI, 0.953-0.982), 0.966 for SIR (95% CI, 0.945-0.979), and 0.962 for LIR (95% CI, 0.939-0.976). Within-reader scan-rescan reproducibility was also excellent. For Reader 1, stiffness ICC was 0.979 for the SIR (95% CI, 0.966-0.987) and 0.957 for the LIR (95% CI, 0.932-0.973), with SDC95 values of 0.616 kPa (95% CI, 0.528-0.741) and 0.869 kPa (95% CI, 0.744-1.044), respectively. Reader 2 showed excellent reproducibility, with ICCs ≤0.943. Conclusions 3D single-frequency 140-Hz cardiac MRE at 3.0 T provides excellent short- and long-interval reproducibility for LV myocardial stiffness quantification. The SDC95 values may help distinguish true longitudinal stiffness changes from measurement variability.

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

Abstract Minimal residual disease (MRD) has been endorsed by the FDA Oncology Drugs Advisory Committee as an endpoint for accelerated approval in multiple myeloma based on individual patient data collected from randomized trials. However, emerging data from recent trials were not included. A novel artificial intelligence (AI)–assisted framework is proposed, which automates information identification and extraction, providing up-to-date analyses that confirm moderate trial-level and strong individual patient–level associations between MRD− rates at suspected complete response (MRD-CR) and survival endpoints in multiple myeloma. Specifically, this study utilized an AI-assisted framework that identifies relevant studies and filters critical information to analyze published data via 2 independent objectives. First, we examined the trial-level association using the coefficients of determination (R2) and its 95% confidence intervals (CI) based on published statistics of treatment effects on MRD and various endpoints. Next, we generated synthetic individual patient data with covariates through AI-curated tools to estimate the individual-level association. The AI tool searched for eligible randomized clinical trials (RCT). A total of 20 two-arm comparisons from 19 RCTs were analyzed. Trial-level analysis showed an R2 of 0.71 (95% CI, 0.52–0.89) pooling disease subpopulations. Furthermore, AI techniques were applied to create synthetic individual data, combining information extracted from Kaplan–Meier curves and subgroup analyses from published literatures. Using generated synthetic data, we estimated the individual-level correlation between MRD-CR rates and progression-free survival outcomes with a bivariate copula model and calculated a global odds ratio of 7.28 (95% CI, 5.60–8.95). Significance: (i) A novel AI-assisted framework is proposed, which automates information identification and extraction, providing rapid up-to-date analyses. (ii) Moderate trial-level and strong individual patient–level associations between MRD and various clinical endpoints in multiple myeloma are confirmed.