Probing the Molecular Interactions and Lubrication Mechanisms of Purified Full-Length Recombinant Human Proteoglycan 4 (rhPRG4) and Hyaluronic Acid (HA)
Bibliographic record
Abstract
Probing the adsorption and lubrication behavior of lubricin, also known as proteoglycan 4 (PRG4), is important for understanding the ultralow friction of cartilage lubrication. Most previous research has focused on native lubricin either purified from synovial fluid or articular cartilage explant culture media. In this work, the adsorption behavior and lubrication mechanism of full-length recombinant human PRG4 (rhPRG4) on mica as well as the effect of adding hyaluronic acid (HA, a polysaccharide) were systematically investigated using a surface forces apparatus (SFA) technique. A low friction coefficient (μ ∼ 0.04) was measured when multilayer rhPRG4 (∼31 nm) was confined in between mica surfaces, even when the load increased to ∼1.2 MPa. Intriguingly, a previously unreported ultralow friction coefficient (μ < 0.005) was observed at a low sliding velocity ( v = 0.14 μm/s) with the applied load P reaching ∼3.6 MPa when a diluted rhPRG4 solution (∼90 μg/mL) was used. The distinct friction behavior is likely due to the smooth and more close-packed lubricin coating, as made evident by the atomic force microscope imaging. Adding HA onto multilayer rhPRG4-coated mica increased the friction coefficient μ to ∼0.1; however, the load bearing property increased, indicating potential synergistic effect between rhPRG4 and HA, which was further demonstrated by the weak adhesion observed when separating rhPRG4-coated mica and HA-coated aminopropyltriethoxysilane-mica (APTES-mica). Alternatively, adding premixed rhPRG4-HA on mica had a friction coefficient (μ ∼ 0.1) close to that of injecting concentrated rhPRG4 (∼450 μg/mL) with lower load sustainability. Our results provide fundamental insights into the adsorption and lubrication behavior of lubricin and its interaction with HA, with useful implications for the underlying mechanism of ultralow friction provided by synovial fluid.
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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".