Effect of counterface on cartilage boundary lubricating ability by proteoglycan 4 and hyaluronan: Cartilage‐glass versus cartilage–cartilage
Bibliographic record
Abstract
The objective of this study was to determine the effect of different sliding interface materials (counterface) on the cartilage lubricating ability of proteoglycan 4 (PRG4) and hyaluronan (HA) by measuring the kinetic coefficient of friction on cartilage-glass and cartilage-cartilage interfaces over a wide range of sliding velocities. The lubrication properties of PRG4 and HA were assessed at cartilage-glass and cartilage-cartilage interfaces using a previously described test setup with a stationary area of contact. Samples were articulated at varying effective sliding velocities of 10, 3, 1, 0.3, 0.1, and 0.01 mm/s. The response of PRG4 and HA as effective friction-reducing cartilage boundary lubricants was varied and was dependent primarily on the test counterface. At a physiological cartilage-cartilage interface both HA and PRG4 effectively reduced friction compared to PBS at slower speeds while at higher speeds PRG4 was similar to PBS, and HA similar to SF. Conversely, at a cartilage-glass interface HA demonstrated no friction reducing ability compared to PBS, and PRG4 appeared just as effective as SF. Cartilage-glass friction coefficients were also significantly greater than cartilage-cartilage friction coefficients. These results indicate the in vitro friction coefficient of putative cartilage boundary lubricants can be affected by the test counterface and suggest that use of synthetic surfaces in studying cartilage boundary lubrication may not always be appropriate for all molecules of interest. As such, care should be taken when interpreting such data, specifically when comparing to in vitro data obtained at a cartilage-cartilage interface, and especially when extrapolating to in vivo situations. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2923-2931, 2018.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".