MétaCan
Menu
Back to cohort
Record W1969390199 · doi:10.1080/10255842.2010.493512

A depth-dependent model of the pericellular microenvironment of chondrocytes in articular cartilage

2010· article· en· W1969390199 on OpenAlexafffund
Sang‐Kuy Han, Salvatore Federico, Walter Herzog

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2010
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsChondrocyteExtracellular matrixCartilageMatrix (chemical analysis)BiophysicsIn situChemistryArticular cartilageStiffnessSpheroidDeformation (meteorology)Bone matrixCell biologyAnatomyMaterials scienceBiomedical engineeringIn vitroOsteoarthritisComposite materialBiologyPathologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Experimental studies suggest that the magnitude of chondrocyte deformation is much smaller than expected based on the material properties of extracellular matrix (ECM) and cells, and that this result could be explained by a structural unit, the chondron, that is thought to protect chondrocytes from large deformations in situ. We extended an existing numerical model of chondrocyte, ECM and pericellular matrix (PCM) to include depth-dependent structural information. Our results suggest that superficial zone chondrocytes, which lack a pericellular capsule (PC), are relatively stiff, and therefore are protected from excessive deformations, whereas middle and deep zone chondrocytes are softer but are protected by the PC that limits cell deformations in these regions. We conclude that cell deformations sensitively depend on the immediate structural environment of the PCM in a depth-dependent manner, and that the functional stiffness of chondrocytes in situ is much larger than experiments on isolated cells would suggest.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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 designSimulation or modeling
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

Citations23
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueComputer Methods in Biomechanics & Biomedical EngineeringSame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207