Reduction of friction by recombinant human proteoglycan 4 in IL‐1α stimulated bovine cartilage explants
Bibliographic record
Abstract
A boundary lubricant attaches and protects sliding bearing surfaces by preventing interlocking asperity-asperity contact. Proteoglycan-4 (PRG4) is a boundary lubricant found in the synovial fluid that provides chondroprotection to articular surfaces. Inflammation of the diarthrodial joint modulates local PRG4 concentration. Thus, we measured the effects of inflammation, with Interleukin-1α (IL-1α) incubation, upon boundary lubrication and PRG4 expression in bovine cartilage explants. We further aimed to determine whether the addition of exogenous human recombinant PRG4 (rhPRG4) could mitigate the effects of inflammation on boundary lubrication and PRG4 expression in vitro. Cartilage explants, following a 7 day incubation with IL-1α, were tested in a disc-on-disc configuration using either rhPRG4 or saline (PBS control) as a lubricant. Following mechanical testing, explants were studied immunohistochemically or underwent RNA extraction for real-time polymerase chain reaction (RT-PCR). We found that static coefficient of friction (COF) significantly decreased to 0.14 ± 0.065 from 0.21 ± 0.059 (p = 0.014) in IL-1α stimulated explants lubricated with rhPRG4, as compared to PBS. PRG4 expression was significantly up regulated from 30.8 ± 19 copies in control explants lubricated with PBS to 3330 ± 1760 copies in control explants lubricated with rhPRG4 (p < 0.001). Explants stimulated with IL-1α displayed no increase in PRG4 expression upon lubrication with rhPRG4, but with PBS as the lubricant, IL-1α stimulation significantly increased PRG4 expression compared to the control condition from 30.8 ± 19 copies to 401 ± 340 copies (p = 0.015). Overall, these data suggest that exogenous rhPRG4 may provide a therapeutic option for reducing friction in transient inflammatory conditions and increasing PRG4 expression. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:580-589, 2017.
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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) | 0.001 | 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".