Radiation-induced heart injury is one of the major side effects of radiotherapy for thoracic malignancies. Previous studies have shown that radiotherapy induced myocardial fibrosis and intensified myocardial remodeling. In this study, we investigated whether atorvastatin could inhibit radiation-induced heart fibrosis in Sprague-Dawley rats, which were randomly divided into six groups: control; radiation only; and four treatment groups receiving atorvastatin plus radiation (E1, E2, E3 and E4). All rats, except the control group, received local heart irradiation in 7 daily fractions of 3 Gy for a total of 21 Gy. Rats in groups E1 (10 mg/kg/day) and E2 (20 mg/kg/day) received atorvastatin and radiation treatment until week 12 after exposure. Rats in groups E3 (10 mg/kg/day) and E4 (20 mg/kg/day) received atorvastatin treatment from 3 months before irradiation to week 12 after irradiation. The expressions of TGF-β1, Smad2, Smad3, fibronectin, ROCK I and p-Akt in heart tissues were evaluated using real-time PC...

Radiation-induced brain injury (RIBI) is a common complication of radiotherapy that leads to neurological symptoms, significantly impairing patients' daily functioning and long-term rehabilitation. Consequently, the development of effective therapeutic strategies has received considerable attention. As an emerging approach in the management of neurological disorders, brain-computer interface (BCI) technology shows substantial potential for both the assessment and treatment of RIBI. This review synthesizes evidence retrieved from electronic databases and examines RIBI from the perspectives of its mechanisms and clinical manifestations. Current findings suggest that BCI technology holds promise for several applications in RIBI, including early diagnosis, mitigation of neuroinflammation, alleviation of associated symptoms, and prediction and management of complications. The implementation of BCI is likely to play a significant role in early assessment and treatment processes for RIBI. Furthermore, with ongoing technological advancements, the development of next-generation BCI is expected to enable more targeted treatment that address additional pathological mechanisms of RIBI, thereby progressively improving the quality of life for affected patients.

Accurate and efficient dose computation lies at the heart of modern radiotherapy planning, yet practitioners are still pulled in two opposing directions: high-fidelity solvers - Monte Carlo simulation and deterministic linear Boltzmann transport equation (LBTE) solvers such as Acuros XB - deliver the physical accuracy that adaptive workflows demand, but the runtime they impose remains hard to reconcile with on-couch decision-making. The present study introduces a physics-informed neural network (PINN) framework that embeds radiation-transport-derived constraints into a 3D encoder-decoder backbone for rapid voxel-wise dose prediction and verification. The network ingests preprocessed CT volumes together with plan-specific information - gantry, collimator and couch angles, projected MLC fluence maps, and per-beam monitor units - and outputs the full 3D dose distribution. We formulate a physics residual loss inspired by the LBTE under simplifying assumptions, applied as a soft regularizer rather than as a replacement for the supervised target, so that training data calculated by the Anisotropic Analytical Algorithm (AAA, Eclipse v15.6) remains the primary supervisory signal while physical plausibility is encouraged across the volume. To overcome the gradient competition between data-driven and physics-driven terms, a momentum-based adaptive weighting strategy continuously rebalances their contributions through training. An end-to-end verification pipeline integrating gamma analysis, dose-volume histogram (DVH) evaluation, and energy-conservation checks completes prediction-plus-quality assurance in under 15 s per patient. On a held-out cohort of 27 head-and-neck and thoracic patients, the proposed system achieved mean global gamma passing rates of 97.8% at 3%/3 mm and 93.7% at 2%/2 mm (10% low-dose threshold) against the AAA reference, outperforming both a conventional 3D U-Net and a static-weight PINN; cross-checking against a subset recomputed with Acuros XB and EGSnrc-based Monte Carlo confirmed the robustness of the gain. Notably, the physics-constrained model trained on only half of the available cases matched the data-only baseline using the full training corpus - an indicator of meaningful sample efficiency. Taken together, the results suggest that combining transport-inspired regularization with adaptive training dynamics offers a practical route toward clinically deployable real-time dose verification in adaptive radiotherapy.

High-quality radiotherapy dose distribution prediction for linear accelerators remains a labor-intensive process constrained by inter-planner variability and institutional data silos. While deep learning has shown promise in automating dose distribution prediction, most existing methods are trained on single-center datasets, limiting their generalizability. Direct multi-center data pooling is hindered by stringent privacy regulations and heterogeneous clinical protocols. This paper proposes a federated learning-driven framework that enables collaborative model training across geographically distributed institutions without exchanging raw patient data. The framework comprises a multi-scale attention U-Net for three-dimensional dose prediction and an adaptive weighted federated aggregation strategy that dynamically balances data volume and local model quality to address non-independent and non-identically distributed data challenges. A layered privacy protection mechanism integrating gradient-clipped differential privacy with secure aggregation provides privacy-enhancing protections with quantifiable bounds during parameter exchange. Experiments conducted across four clinical centers on head-and-neck and abdominal IMRT cases demonstrate that the proposed approach achieves a mean Gamma pass rate of 96.8%, closely approaching the centralized training upper bound of 97.5% while significantly outperforming single-center models and standard federated averaging. Ablation studies confirm the individual contributions of adaptive weighting and dual attention modules, and robustness analyses validate fault tolerance under client dropout and adversarial conditions. The proposed framework offers a practical and privacy-preserving pathway for breaking data silos in AI-driven radiotherapy research.

BACKGROUND: The relationship between childhood and adulthood obesity and the risk of gestational diabetes mellitus (GDM) remains unclear. To clarify the independent and joint effects of childhood and adulthood body size on GDM risk, and explore inflammation’s role. METHODS: Using female-specific UK Biobank genome-wide association study data, genetic instruments for childhood/adult body size (“thinner,” “about average,” “plumper”) and C-reactive protein (CRP) were identified. GDM variants came from FinnGen. Univariable and multivariable Mendelian randomization (MR) assessed causality and mediation. RESULTS: Univariable MR analyses provided strong evidence for genetically predicted effects of both childhood body size (odds ratio [OR] per category = 1.72, 95% CI: 1.42–2.09, P < 0.001) and adulthood body size (OR = 1.59, 95% CI: 1.42–1.79, P < 0.001) on GDM risk. However, in multivariable MR analysis, the effect of childhood body size was attenuated and no longer significant after adjusting for adulthood body size (OR = 1.19, 95% CI: 0.91–1.48, P = 0.221), whereas the effect of adulthood body size remained significant even after controlling for birth weight, childhood body size, and age at menarche (OR = 1.42, 95% CI: 1.15–1.68, P = 0.011). Further analysis indicated that CRP partially mediated the effect of adulthood body size on GDM risk. CONCLUSIONS: Our findings suggest that childhood obesity increases the future risk of GDM primarily through its persistence into adulthood, and that inflammation, as indicated by elevated CRP levels, partially mediates the effect of adult obesity on GDM risk. These results highlight the importance of early obesity prevention and intervention, as well as inflammation control, to reduce the risk of GDM later in life.