Effects of digestion protocols on the isolation and characterization of metal-metal wear particles. I. Analysis of particle size and shape
Bibliographic record
Abstract
Isolation of metal wear particles from hip simulator lubricants or tissues surrounding implants is a challenging problem because of small particle size, their tendency to agglomerate, and their potential for chemical degradation by digestion reagents. To provide realistic measurements of size, shape, and composition of metal wear particles, it is important to optimize particle isolation and minimize particle changes due to the effects of the reagents. In this study (Part I of II), transmission electron microscopy (TEM) was used to examine and compare the effects of different isolation protocols, using enzymes or alkaline solutions, on the size and shape of three different types of cobalt-based alloy particles produced from metal-metal bearings. The effect on particle composition was examined in a subsequent study (Part II). Large particles (<1200 nm) were generated by dry abrasion of CoCrMo alloy against itself and small particles (<300 nm) were generated by hip simulator testing of a metal-metal implant pair in the presence of either distilled-deionized water or a 95% bovine serum solution. The reagents changed particle size and to a lesser extent particle shape. For both large particles and small particles generated in water, the changes in size were more extensive after alkaline than after enzymatic protocols and increased with alkaline concentration and time in solution, up to twofold at 2 h and threefold at 48 h. However, when isolating particles from 95% serum, an initial protective effect of serum proteins and/or lipids was observed. Because of this protective effect, there was no significant difference in particle size and shape for both oval and needle-shaped particles after 2 h in 2N KOH and after enzymatic treatments. However, round particles were significantly smaller after 2 h in 2N KOH than after enzymatic treatments. Particle composition may also have been affected by the 2N KOH treatment, as suggested by a difference in particle contrast under TEM, an issue examined in detail in Part II.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".