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Record W2526011084

EFFECT OF COUNTERFACE ON CARTILAGE BOUNDARY LUBRICATING ABILITY BY HYALURONAN AND PROTEOGLYCAN 4: CARTILAGE-CARTILAGE VS CARTILAGE-GLASS

2014· article· en· W2526011084 on OpenAlexvenueno aff
Allison McPeak, Saleem Abubacker, Tannin A. Schmidt

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsCartilageSynovial fluidMaterials scienceBiomedical engineeringOsteoarthritisHyaluronic acidChemistryComposite materialAnatomyMedicinePathology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Proteoglycan 4 (PRG4), also known as lubricin, is a mucin-like glycoprotein found in synovial fluid (SF) [1]. Hyaluronan (HA) is another constituent of SF that works synergistically with PRG4 to promote joint lubrication required for joint health [2]. Current in vitro friction tests used to analyze these lubricants have found similar trends but varying friction coefficient (m) magnitudes. These effects may be attributed to different testing protocols, and in particular the various interfaces [3,4,5,6,7,8]. Previous studies have used cartilage-cartilage [7,8] and cartilage-glass [6] friction tests to assess SF lubricants. However, few studies have examined the friction reducing ability of SF lubricants at various velocities and different interfaces. The objective of this study was to determine HA and PRG4’s lubricating ability at cartilage-glass and cartilage-cartilage biointerfaces at various velocities. METHODS HA (1.5 MDa, Lifecore Biomedical) was prepared at 3.3 mg/mL with phosphate buffered saline (PBS) [9]. PRG4 was purified from media conditioned bovine cartilage explants, and prepared at 450 μg/mL in PBS [9]. Two sets of tests were conducted using a modified boundary lubrication test protocol [7]. For cartilage-glass, a 6mm radius glass piece, with a root mean square surface roughness of 6.061±0.7554 nm, acted as the base core with a cartilage annuli [7]. Three lubricants were tested over three days, PBS (n=8), HA or PRG4 (n=4), and SF (n=8). Cartilage-cartilage underwent the same test sequence. Both sets of samples underwent the same testing protocol; samples were compressed to 18% of the total cartilage thickness with a 40 minute stress relaxation [9]. Samples were then rotated at effective sliding velocities of 10, 3, 1, 0.3, 0.1, and 0.01 mm/s with a 120s pre-sliding duration. Kinetic μ was calculated with instantaneous ( ) load values. A two-factor ANOVA was used to determine effects of lubricant and velocity, with Tukey post-hoc testing. RESULTS At the cartilage-glass interface ( Fig. 1A ), varied with test lubricant and velocity (p varied with test lubricant and velocity (p , and also affect the magnitude of the value. This agrees with previous research; showing that stiff and impermeable surfaces versus hydrated and permeable surfaces results in different [3]. This data illustrates that HA is indeed a cartilage boundary lubricant and reduces friction at the cartilage-cartilage interface, but not at a cartilage-glass set up. Overall, different test systems are suitable for characterizing lubrication properties, but direct values should not be compared.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.322
Teacher spread0.304 · 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.

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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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