Bone mechanical properties were altered in a mouse model of multiple myeloma bone disease
Bibliographic record
Abstract
Multiple myeloma bone disease (MMBD) is characterized by the growth of malignant plasma cells in bone marrow, leading to an imbalance in bone (re)modeling favoring excessive resorption. Loss of bone mass and altered microstructure characterize MMBD in humans and preclinical animal models, although, no study to date has examined bone composition or material properties. We hypothesized that MMBD alters bone composition, mineral crystal properties and mechanical properties in the MOPC315.BM.Luc model after intra-tibial injection of myeloma cells and three weeks of daily in vivo tibial loading. Decreased cortical bone elastic modulus and hardness measured by nanoindentation of tibiae were observed in MM-injected mice compared to PBS-injected mice, whereas cortical bone composition, mineral crystal properties measured by Fourier-transform infrared imaging or small angle X-ray scattering, respectively remained unchanged. However, MM-injected mice had thinner cancellous bone mineral particles compared to PBS-injected mice. Mechanical loading did not lead to altered cortical bone composition, mineral structure, or mechanical properties in the context of MM. Unexpectedly, we observed the intra-tibial injection itself altered the material composition of bone, manifested by increased matrix mineralization and crystal size of the hydroxyapatite crystals in the bone matrix. In conclusion, our data suggest that mechanical stimuli can be used as an adjuvant bone anabolic therapy in patients with MMBD to rebuild bone with unaltered composition and mineral structure to reduce subsequent fracture risk.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".