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Record W4295648533 · doi:10.1016/j.actbio.2022.09.002

The intrinsic quality of proteoglycans, but not collagen fibres, degrades in osteoarthritic cartilage

2022· article· en· W4295648533 on OpenAlexaff
Eng Kuan Moo, Mohammadhossein Ebrahimi, Scott C. Sibole, Petri Tanska, Rami K. Korhonen

Bibliographic record

VenueActa Biomaterialia · 2022
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Calgary
FundersH2020 Marie Skłodowska-Curie ActionsPohjois-Savon RahastoItä-Suomen YliopistoSuomen KulttuurirahastoEmil Aaltosen SäätiöEuropean CommissionMaire Lisko FoundationAcademy of Finland
KeywordsMaterials scienceCartilageProteoglycanOsteoarthritisSwellingMatrix (chemical analysis)Biomedical engineeringExtracellular matrixBiophysicsFibrilComposite materialMatrix metalloproteinaseSofteningStress relaxationCollagen fibrilDegradation (telecommunications)ChemistryAnatomyPathologyBiochemistryBiologyCreepMedicine

Abstract

fetched live from OpenAlex

The function of articular cartilage as a load-bearing connective tissue is derived primarily from a balanced interaction between the swelling proteoglycan (PG) matrix and tension-resistant collagen fibrous network. Such balance is compromised during joint disease such as osteoarthritis (OA) due to degradation to PGs and/or collagens. While the PG degradation is generally thought to be related to a loss of protein abundance, the collagenous degradation is more complex as it can be caused independently by a decrease of collagen content, disorganisation of fibrous structure and softening of individual collagen fibrils. A comprehensive understanding of the initial trajectories of degradation of PGs and collagen network can improve our chance of finding potential therapeutic solutions for OA. Here, we developed geometrically, structurally, and compositionally realistic and sample-specific Finite Element (FE) models under the framework of multiphasic mixture theory, from which the elastic moduli of collagen fibres and the PG load-bearing quality in healthy and diseased cartilages were estimated by numerical optimisation of the multi-step indentation stress relaxation force-time curves. We found the intrinsic quality of collagen fibres, measured by their elastic moduli, to stay constant for healthy and diseased cartilages. Combining with previous findings which show unaltered collagen content during early stages of OA, our results suggest the disorganisation of collagen fibrous network as the first form of collagenous degradation in osteoarthritic cartilage. We also found that PG degradation involves not only a loss of protein abundance, but also the quality of the remaining PGs in generating sufficient osmotic pressure for load bearing. This study sheds light on the mechanism of OA pathogenesis and highlights the restoration of collageneous organisation in cartilage as key medical intervention for OA. STATEMENT OF SIGNIFICANCE: Collagen network in articular cartilage consists of individual fibres that are organised into depth-dependent structure specialised for joint load-bearing and lubrication. During osteoarthritis, the collagen network undergoes mechanical degradation, but it is unclear if a loss of content, disorganisation of fibrous structure, or softening of individual fibres causes this degeneration. Using mechanical indentation, Finite Element modelling, and numerical optimisation methods, we determined that individual fibres did not soften in early disease stage. Together with previous findings showing unaltered collagen content, our results pinpoint the disorganisation of collagen structure as the main culprit for early collagenous degradation in osteoarthritic cartilage. Thus, early restoration in cartilage of collagen organisation, instead of individual fibre quality, may be key to slow osteoarthritis development.

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 score0.014
Threshold uncertainty score0.581

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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

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