Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.07.14.738210

Palliative Irradiation Affects Temporal Degradation of Rodent Vertebral Bone Mechanics, Architecture, and Composition

Abstract

BackgroundPalliative radiation therapy (RT) for metastatic spine disease significantly increases the risk of vertebral fractures. However, the temporal mechanisms underlying radiation-induced vertebral bone fragility remain poorly understood. ObjectiveTo evaluate the longitudinal effects of a single high-dose irradiation, simulating palliative RT, on vertebral bone mechanical, architectural, and compositional properties in a healthy, skeletally mature rat model. MethodsThirty-one male Sprague Dawley rats received a single 15 Gy lumbar spine irradiation (IR). L4 vertebrae were assessed across all groups (irradiation: 7, 14, and 28 days post-IR, controls: at 0 and 28 days post-IR) for compressive strength and stiffness, micro-CT-derived bone composition and trabecular indices, serum bone turnover markers (NTX and BAP) and advanced glycation endproducts (AGEs). ResultsIrradiation induced progressive deterioration of vertebral bone mechanical properties, with strength decreasing up to 44% and stiffness up to 38% by 28 days post-IR, compared to 0- day controls. Trabecular bone exhibited reduced BMD, BV/TV, and Tb.N with increased Tb.Sp, a shift toward a more rod-like structure. Early post-IR changes suggested disrupted bone remodeling, characterized by elevated NTX and AGEs, but decreased BAP. Multivariable regression demonstrated that Tb.Th and AGEs were independent predictors of stiffness, collectively explaining 61% of its variance. DiscussionHigh-dose irradiation induces sustained temporal degradation of vertebral mechanical properties driven by both trabecular architectural deterioration and alterations in bone matrix quality. Measures of bone composition and non-enzymatic bone turnover suggest this early damage was driven by disruption of bone cellular homeostasis, favoring increased resorption over formation. These findings support that radiation impairs both structural integrity and pre-yield mechanical behavior, providing mechanistic insight into the elevated fracture risk observed clinically after irradiation for metastatic spine disease. Lay summaryThis study used a rat model to mimic palliative radiation therapy for cancer that has spread to the spine and evaluated the changes in bone quality up to 28 days post-therapy. We found that irradiation progressively weakened the structural integrity and composition of the bones in the spine and disrupted the normal balance of bone breakdown and repair, leading to greater bone loss and fragility. Our findings provide insight into the increased risk of fractures observed in patients receiving radiation therapy to the spine and may support efforts to better protect bone health during treatment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, C., Berardi, M., Martin, S., Brown, C., Soltani, Z., Keko, M., Rosa-Caldwell, M. E., Mortreux, M., Rutkove, S., Bailey, S., Alkalay, R. A.. 2026-07-15. Palliative Irradiation Affects Temporal Degradation of Rodent Vertebral Bone Mechanics, Architecture, and Composition. https://doi.org/10.64898/2026.07.14.738210

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

bioengineering↗

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗