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Record W2524258384 · doi:10.1186/s12891-019-2469-4

Investigating the effect of proteoglycan 4 on hyaluronan solution properties using confocal fluorescence recovery after photobleaching

2019· article· en· W2524258384 on OpenAlexafffund
Adam K. Bloom, Michael Samsom, Suresh C. Regmi, Bridgett L. Steele, Tannin A. Schmidt

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluorescence recovery after photobleachingFluoresceinChemistryConfocal microscopyConfocalFluorescein isothiocyanateBiophysicsFluorescenceBiochemistryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to use confocal fluorescence recovery after photobleaching (FRAP) to examine the specific and dose-dependent effect of proteoglycan 4 (PRG4) on hyaluronan (HA) solutions of different molecular weight; and assess the effect of reduction and alkylation (R/A) of PRG4 on its effects on HA solutions. METHODS: ) through 1500 kDa and 500 kDa HA solutions (0-3.3 mg/ml) ± PRG4 or a control protein, bovine serum albumin (BSA), at physiological (450 μg/ml) or pathophysiological (45 μg/ml) concentrations. The effect of PRG4 or R/A PRG4 on 1500 kDa HA solutions was also investigated. Empirical constants obtained from fitting data to the universal scaling equation were used to calculate the average distribution of apparent mesh sizes. RESULTS: PRG4 at both 45 and 450 μg/ml slowed the diffusion of the FITC-dextran tracer for all concentrations of HA and caused a decrease in the apparent mesh size within the HA solution. This effect was specific to PRG4, not observed with BSA, but not dependent on its tertiary/quaternary structure as the effect remained after R/A of PRG4. CONCLUSIONS: These results demonstrate that PRG4 can significantly alter the solution properties of HA; PRG4 essentially reduced the permeability of the HA network. This effect may be due to PRG4 entangling HA molecules through binding and/or HA crowding PRG4 molecules into a self-assembled network. Collectively these findings contribute to the understanding of PRG4 and HA interaction(s) in solution and therefore the function of SF in diarthroidal joints.

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)0.001
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 score0.066
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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