Synergistic Interactions of a Synthetic Lubricin-Mimetic with Fibronectin for Enhanced Wear Protection
Bibliographic record
Abstract
Lubricin, a major mucinous glycoprotein of mammalian synovial fluids, is believed to provide excellent lubrication to cartilage surfaces. Consequently, when joint disease or replacement leads to increased friction and surface damage in the joint, robust synthetic lubricin alternatives that could be used therapeutically to improve lubrication and surface protection are needed. Here, we report the characterization of a lubricating multiblock bottle-brush polymer whose architecture was inspired by lubricin, and we investigate the role of fibronectin (FN), a glycoprotein found in the superficial zone of cartilage, in mediating the tribological properties of the polymer upon shear between mica surfaces. Our surface force apparatus (SFA) normal force measurements indicate that the lubricin mimetic could be kept anchored between mica surfaces, even under high contact pressures, when an intermediate layer of FN was present. Additional SFA friction measurements shows that FN would also extend the wearless friction regime of the polymer up to pressures of 3.4 MPa while ensuring stable friction coefficients (µ ≈ 0.28). These results demonstrate synergistic interactions between our lubricin mimetic and FN in assisting the lubrication and wear protection of ideal (mica) substrates upon shear. Collectively, these findings suggest that our proposed lubricin mimetic might be a promising alternative to lubricin, as similar mechanisms could also potentially facilitate the interaction between the polymer and cartilage surfaces in articular joints and prosthetic implants in vivo.
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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) | 0.000 | 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".