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Record W4416209097 · doi:10.1302/1358-992x.2025.13.036

SYSTEMIC TREATMENTS IMPACT BONE QUALITY IN A RAT MODEL OF MIXED FEMORAL METASTASES

2025· article· en· W4416209097 on OpenAlexaff
Azin Mirzajavadkhan, Liza Abraham, Margarete K. Akens, Michael Hardisty, Cari Whyne

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZoledronic acidDocetaxelH&E stainRat modelProstate cancerFemurIn vivoCancerBone remodeling

Abstract

fetched live from OpenAlex

Skeletal metastases impact the bone remodeling process, compromising the mechanical integrity of the bone, and increasing the risk of pathological fracture. Cancer treatments have additionally been shown to influence bone quality in vertebrae with osteolytic metastases. This study aims to quantify the effect of systemic treatments (zoledronic acid (ZA) or docetaxel (DTX)) on bone quality in a preclinical model of mixed femoral metastases. Eleven six-week-old athymic male rats (Hsd: RH-Foxn1rnu, Envigo, USA) were inoculated with luciferase-transfected ACE-1 canine prostate cancer cells via an intracardiac injection after a week of acclimation (day 0). Institutional approval was obtained, and the ARRIVE guidelines were followed. The animals were randomly assigned to the following groups: untreated (n=3), zoledronic acid treated (n=4), and docetaxel treated (n=4). Zoledronic acid (Zometa® Norvartis; 60 μg/kg) or docetaxel (Aventis Pharma; 5 mg/kg) was administered on day 10 post inoculation. In vivo bioluminescence imaging (day 14, day 21) was used to assess tumor burden. All animals were euthanized on day 21. Femora excised bilaterally (n=22) underwent μCT scanning (μCT100, Scanco, Switzerland) and microstructural analysis was conducted on the trabecular bone within the distal femora (Amira). For histological analysis, the right femora (n=11) were stained with hematoxylin and eosin (H&E) to assess bone histoarchitecture. Tumor presence was detected using an anti-wide cytokeratin antibody stain. The left distal femora (n=11) were cut to a 1cm length using a diamond wafering blade on a low-speed saw (Isomet 1000, USA). The samples were stained with BaSO4 and µCT imaged (90kVp, 44µA, 4.9µm) to visualize microdamage location and volume. Damage volume fraction was calculated as the ratio of BaSO4 stain volume (SV) to bone volume (BV). Voxels representative of SV and BV were segmented with constant global thresholds of 10000 HU (~2400 mgHA/cm3) and 3000 HU (~736 mgHA/cm3), respectively. Finally, the samples were loaded to failure under axial compression and force-displacement data recorded. Differences in microstructural parameters, damage volume fraction, and load to failure were compared between treatment groups using one-way ANOVAs; post hoc analyses were performed using Tukey HSD. Docetaxel significantly reduced the mean bone volume (BV/TV, p=0.041) compared to untreated controls. Zoledronic acid effectively diminished tumor-induced osteolysis, consistent with its known capacity to inhibit osteoclast activity resulting in increased bone mineral density (BMD) and BV/TV compared to untreated controls (p=0.022, p=0.025 respectively) and DTX-treated animals (p<.0001 for BMD and BV/TV). For microdamage analysis, both DTX (p=0.006) and ZA (p=0.017) significantly decreased the mean damage volume fraction compared to the untreated group. Compared to the untreated controls, load to failure was significantly increased in ZA treated animals (p=0.024), with DTX treated animals exhibiting a positive trend (p=0.061). This study reinforces the positive role of ZA in preserving bone health. DTX, despite lower BV/TV had less damage volume fraction leading to a trend towards improved load to failure, suggesting better bone quality. Multiple treatment options are available for skeletal metastases, quantifying the impact of such treatments on metastatically affected bone is crucial for comprehending the consequences of cancer therapies and directing treatment delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.354
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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