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Record W1975514450 · doi:10.1115/1.4001422

A Mechanism to Explain Physiological Lubrication

2010· article· en· W1975514450 on OpenAlexafffund
David F. James, Garret M. Fick, W. Douglas Baines

Bibliographic record

VenueJournal of Biomechanical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLubricationHyaluronic acidAsperity (geotechnical engineering)Synovial fluidViscosityMechanism (biology)Materials scienceLubricantSynovial jointChemistryMechanicsFlow (mathematics)CompressibilityComposite materialChemical physicsBiomedical engineeringArticular cartilageOsteoarthritisAnatomyPhysicsEngineeringMedicine

Abstract

fetched live from OpenAlex

A new mechanism of physiological lubrication is proposed to explain how low-viscosity synovial fluid prevents articular surfaces from contacting and wearing. The new mechanism is based on the hypothesis that the hyaluronic acid chains in synovial fluid bind to the cartilage surfaces through electrostatic charges, with the phospholipid layer on an articular surface supplying the necessary attractive charges. The stationary hyaluronic acid network causes a large hydrodynamic resistance to outward flow from the gap. To determine the effectiveness of the network in preventing contact, squeeze-film flow between two incompressible, permeable disks is analyzed when a constant load is suddenly applied, and the solvent--synovial fluid minus the hyaluronic acid--escapes through the network and through the permeable disks. The analysis yields the approximate time for the gap distance to decrease to asperity size. For realistic physiological parameters, the time for the surfaces to contact is a minimum of several minutes and likely much longer. The role of albumin in the synovial fluid is included because the large protein molecules are trapped by the small openings in the hyaluronic acid network, which increases the flow resistance of the network and thereby delays contact of the surfaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.404
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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 teacher head, 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

Citations4
Published2010
Admission routes2
Has abstractyes

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