Calf serum constituent fractions influence polyethylene wear and microbial growth in knee simulator testing
Bibliographic record
Abstract
Calf serum lubricants consisting of various polypeptide constituent fractions are routinely used in knee wear simulators as part of the standardized test protocol. Three calf sera (bovine, new-born and alpha) were diluted as per the recommendation of ISO 14243-3 and used in displacement-controlled knee wear simulators to investigate their effects on polyethylene wear. Biochemical analyses included measuring total polypeptide degradation, electrophoretic profiles and low-molecular weight polypeptide concentrations to elucidate their involvement in the wear process. The effects of the various calf sera constituent fractions on microbial growth were also explored. The polyethylene wear rates and the results from the biochemical analyses for the three calf serum lubricants were all found to be statistically significantly different from each other. The lubricant derived from the alpha-calf serum was closest in constituent fractions to human synovial fluid. It also showed the lowest polyethylene wear rate (14.38 +/- 0.85 mm3/million cycles) and the lowest amount of polypeptide degradation (7.77 +/- 3.87%). Furthermore, the alpha-calf serum lubricant was associated with the least amount of change in the electrophoretic profile, the least change in low-molecular weight polypeptide concentration, and the lowest microbial growth in the presence of sodium azide (a microbial inhibitor conventionally used in implant wear testing). Replacing sodium azide with a broad spectrum antibiotic-antimycotic eradicated the microbial growth. Some speculation was entertained regarding the effect of alpha-calf serum on colloid-mediated boundary lubrication. Based on the results, it was recommended that ISO 14243-3 be modified to include guidelines on calf serum constituent fractions that would favour using alpha-calf serum in order to improve the fidelity of the simulation in knee implant wear testing.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".