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Record W2907217366 · doi:10.1021/acs.biomac.8b01678

Probing the Molecular Interactions and Lubrication Mechanisms of Purified Full-Length Recombinant Human Proteoglycan 4 (rhPRG4) and Hyaluronic Acid (HA)

2019· article· en· W2907217366 on OpenAlexafffund
Jun Huang, Xiaoyong Qiu, Lei Xie, Gregory D. Jay, Tannin A. Schmidt, Hongbo Zeng

Bibliographic record

VenueBiomacromolecules · 2019
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationShandong University
KeywordsMicaLubricationHyaluronic acidLubricantSurface forces apparatusAdhesionAdsorptionMaterials scienceProteoglycanBoundary lubricationCartilageChemistryComposite materialChemical engineeringBiochemistryAnatomyExtracellular matrixOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.207
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2019
Admission routes2
Has abstractyes

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