Citations

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 Background Lupus nephritis remains a major cause of chronic kidney disease and kidney failure in systemic lupus erythematosus. Glucocorticoids are central to treatment but are limited by systemic toxicity. We evaluated whether a previously characterized collagen IV α3-targeted liposomal nanoparticle formulation carrying low-dose prednisolone could attenuate established lupus nephritis in MRL/lpr mice. Methods Female MRL/lpr mice with disease present at treatment initiation and C57BL/6J control mice received saline or collagen IV α3-targeted prednisolone-loaded nanoparticles (Col4-α3-Pred-NPs). Renal outcomes were assessed by longitudinal proteinuria, glomerular filtration rate (GFR), survival, kidney histopathology, renal IgG and C3d deposition, dUTP/TUNEL-associated injury staining, and renal cytokine/chemokine profiling. Body weight, food and water intake, and blood glucose were monitored as measures of general condition and preliminary tolerability. Results Col4-α3-Pred-NPs improved survival in MRL/lpr mice, reduced cumulative proteinuria burden, and attenuated terminal GFR decline compared with saline-treated MRL/lpr controls. Treatment reduced glomerular and tubulointerstitial injury, lowered composite EGTI histopathology scores, decreased terminal kidney enlargement, reduced glomerular IgG deposition and renal dUTP-positive injury signals, and reduced renal signals for IL-28A/B, IL-7, PD-ECGF, IL-11, CCL6/C10, and IL-15. C3d deposition was not significantly altered. Nanoparticle treatment was not associated with sustained treatment-related increases in blood glucose or body-weight loss during the measured study period. Conclusions Collagen IV α3-targeted liposomal delivery of low-dose prednisolone attenuated established lupus nephritis in MRL/lpr mice and improved renal structural, functional, inflammatory, and survival outcomes. These findings support further evaluation of glomerulus-targeted nanotherapy as a potential strategy to improve the precision and therapeutic index of glucocorticoid treatment in lupus nephritis.

Abstract Lupus nephritis (LN) requires long-term immunosuppressive therapy, which is often associated with severe systemic side effects. Therefore, new therapeutic strategies that maintain high efficacy while minimizing adverse effects are essential. Although nanomedicine has advanced systemic and kidney-targeted drug delivery, a reliable method for glomerulus-specific delivery is lacking. Collagen IV (Col4)-alpha 3, located in the glomerular basement membrane at the blood–tissue interface through fenestrated capillary endothelium, represents an ideal target for glomeruli delivery. Herein, we developed a novel liposomal nanoparticle conjugated with a Col4-alpha 3-binding peptide (Col4-α3-NPs) for selective glomerular targeting. Prednisolone-loaded Col4-α3-NPs were administered to lupus-prone mice twice weekly for 8 weeks. Kidney injury and function were evaluated biweekly, and renal immune cell populations were analyzed by flow cytometry at study completion. The results show that rhodamine-labeled NPs predominantly accumulate in kidney glomeruli 48 h after intravenous injection. The Col4-NP system demonstrated stable and prolonged release of the encapsulated drug for over 48 h. Lupus-prone mice treated with prednisolone-loaded Col4-NPs showed significantly improved renal function and histology, including a 30% increase in glomerular filtration rate, a 56% reduction in proteinuria, and decreased IgG deposition and fibrosis. Notably, treatment also enhanced renal regulatory T cell populations. These findings suggest that glomerulus-targeted Col4-α3-NPs hold significant translational promise. This platform may offer an effective, site-specific treatment for LN while minimizing systemic side effects and could be adapted for other glomerular diseases requiring targeted therapy.

Heart transplantation remains the definitive treatment for end-stage heart failure. Preservation of donor hearts is a critical determinant of graft function and post-transplant outcomes. Traditional static cold storage (SCS) has limitations in ischemia tolerance and functional assessment, particularly for marginal and donation after circulatory death (DCD) hearts. Emerging strategies, including normothermic ex vivo perfusion (NEHP) and hypothermic oxygenated perfusion (HOPE), enable extended preservation, functional evaluation, and improved utilization of high-risk donor organs. This review summarizes current preservation techniques, discusses mechanistic strategies to mitigate ischemia-reperfusion injury, highlights emerging trends and controversies, and identifies future research directions to optimize outcomes in heart transplantation.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

ABSTRACT As a principal metabolite of ginsenosides, compound K (CK) exhibits remarkable antioxidant, anti‐inflammatory, antidiabetic, and anticancer bioactivities. However, CK is naturally scarce in ginseng and its industrial application is limited by the lack of efficient deglycosylating enzymes for large‐scale CK bioproduction. Here, we report a synthetic biology‐driven approach for high‐yield CK production via recombinant β‐glycosidase‐mediated bioconversion of the polar ginsenoside Rb1. The β‑glycosidase‐encoding gene was introduced into Escherichia coli BL21 (DE3), and the resulting engineered strain produced 2.1 g/L β‑glycosidase in shake flasks and 10.1 g/L in a 5‐L fermentor. The crude β‐glycosidase exhibited excellent enzymatic activity; at a concentration of 1.5 g/L, it converted 95% of the Rb1 substrate (initial concentration 10 g/L) to CK within 4 h. In vitro studies demonstrated CK's high cytocompatibility and potent anti‐aging effects, including suppression of reactive oxygen species ( via upregulation of superoxide dismutase, catalase, and glutathione peroxidase), enhanced collagen I, Ⅲ and elastin synthesis, and matrix metalloproteinase‐1 inhibition. These findings highlight CK as a promising candidate for anti‐wrinkle formulations, supported by the feasibility of scalable bioproduction.

ABSTRACT Lupus nephritis requires long-term immunosuppressive therapy, which is often associated with severe systemic side effects. Therefore, new therapeutic strategies that maintain high efficacy while minimizing adverse effects is essential. Although nanomedicine has advanced systemic and kidney-targeted drug delivery, a reliable method for glomerulus-specific delivery is lacking. Collagen IV (Col4)-alpha 3, located in the glomerular basement membrane (GBM) at the blood–tissue interface through fenestrated capillary endothelium, represents an ideal target for glomeruli delivery. Herein, we developed a novel liposomal nanoparticle conjugated with a Col4-alpha 3 binding peptide (Col4-α3-NPs) for selective glomerular targeting. Prednisolone-loaded Col4-α3-NPs were administered to lupus-prone mice twice weekly for 8 weeks. Kidney injury and function were evaluated biweekly, and renal immune cell populations were analyzed by flow cytometry at study completion. The results show that rhodamine-labeled NPs predominantly accumulate in kidney glomeruli 48 hours after intravenous injection. The Col4-NP system demonstrated stable and prolonged release of the encapsulated drug for over 48 hours. Lupus-prone mice treated with prednisolone-loaded Col4-NPs showed significantly improved renal function and histology, including a 30% increase in glomerular filtration rate (GFR), a 56% reduction in proteinuria, and decreased IgG deposition and fibrosis. Notably, treatment also enhanced renal regulatory T cell (Treg) populations. These findings suggest that glomerulus-targeted Col4-α3-NPs hold significant translational promise. This platform may offer an effective, site-specific treatment for lupus nephritis while minimizing systemic side effects and could be adapted for other glomerular diseases requiring targeted therapy.

Importance Ischemic heart disease (IHD), the leading cause of death in the US, is predominantly due to modifiable risk factors. Estimates of IHD mortality attributable to risk factors provide evidence for health policy decision-making. Objective To estimate the burden of IHD death attributable to risk factors in the US from 1990-2023. Design, Setting, and Participants The Global Burden of Disease Study 2023 (GBD 2023) used vital records and a broad set of epidemiologic data to estimate IHD death rates, risk factor exposure, and relative risk curves for risk-outcome pairs for 1990-2023 for the general population. Data analysis was performed from October 2024 to December 2025. Exposure Twelve metabolic, behavioral, and environmental risk factors. Main Outcomes and Measures The primary outcomes were IHD death rates per 100 000 persons, counts, and attributable risks from 1990-2023 by age, sex, and US state. Estimates include 95% uncertainty intervals (UI). IHD death rates were estimated using ensemble modeling methods. Risk exposures were estimated using bayesian meta-regression methods. Relative risks were estimated following the Burden of Proof framework. Results In 2023, there were 473 000 IHD deaths (95% UI, 414 000-510 000) in the US, a decrease of 58.7% (95% UI, 56.8%-61.0%) in age-standardized rate since 1990. Between 2010 and 2023, there was a 19.0% decrease (95% UI, 15.0%-22.9%) for males and a 24.5% decrease (95% UI, 20.3%-29.7%) for females in IHD death rates. High systolic blood pressure (SBP), dietary risks, and high low-density lipoprotein cholesterol (LDL-C) were the leading risk factors for IHD deaths in 2023, accounting for 47.2% (95% UI, 36.4%-57.0%), 38.6% (95% UI, 17.2%-56.8%), and 28.5% (95% UI, 19.3%-39.6%) of IHD deaths, respectively. Increased exposure to several risk factors substantially increased their attributable burden for IHD deaths in 2023, with high fasting plasma glucose (FPG) increasing 38.8% (95% UI, 11.5%-81.1%) and high body mass index (BMI) increasing 54.5% (95% UI, 41.8%-66.3%) since 1990. Smoking and particulate matter pollution had the greatest decrease in attributable IHD mortality since 1990, at 33.3% (95% UI, 23.6%-41.7%) and 74.9% (95% UI, 46.7%-88.8%), respectively. Conclusion and Relevance Per the results of this systematic analysis of GBD 2023, a total of 88.7% (95% UI, 83.4%-92.5%) of IHD deaths were attributable to modifiable risk factors in the US in 2023, with high SBP, dietary risks, and high LDL-C being the greatest contributors. High BMI and high FPG showed the largest attribution increases, while exposure to other risks did not increase significantly for the population.

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.