Wear‐particle–induced osteoclast osteolysis: The role of particulates and mechanical strain
Bibliographic record
Abstract
Periprosthetic osteolysis involves osteoclast activation by wear particulates and their exposure to mechanical perturbation through exposure to shear forces generated by periprosthetic fluid as well as interface micromotion. This study aimed to determine the interactions between wear particulates, mechanical stimulation, and osteoclasts. In static cultures, wear particulates increased osteoclast differentiation. Addition of neutralizing antibodies to RANKL (receptor activator of nuclear factor kappa ligand) inhibited the particle-induced increase in osteoclast numbers. Cyclic 5000 microstrains were applied with the use of a custom-built device to marrow-derived cultures to assess the effect on osteoclast differentiation. Mechanical strain application alone decreased osteoclast differentiation, which was further decreased by the addition of particles despite increases in the soluble RANKL to osteoprotegerin (OPG) ratio. Mechanical strain alone induced mature osteoclast apoptosis in a dose-dependent manner. In contrast, in the mature osteoclast model, the addition of nonmetal particulates protected the osteoclasts from becoming apoptopic. Titanium (Ti) and cobalt chromium (CoCr) particles, however, induced osteoclast apoptosis, whereas polyethylene (PE) and polymethylmethacrylate (PMMA) did not. Wear particulates and mechanical stimulation interact via an eicosanoid-dependent pathway to alter osteoclast function and survival. The addition of mechanical perturbation to a particle-laden system thus appears to enhance the potential for osteolytic activity by enhancing osteoclast survival.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".