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Record W2052208249 · doi:10.1115/1.4000933

Changes in Collagen With Aging Maintain Molecular Stability After Overload: Evidence From an In Vitro Tendon Model

2010· article· en· W2052208249 on OpenAlexafffund
Thomas L. Willett, Rosalind S. Labow, Ian G. Aldous, Nick Avery, J. Michael Lee

Bibliographic record

VenueJournal of Biomechanical Engineering · 2010
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsDalhousie UniversityUniversity of OttawaLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDalhousie UniversityPaulson Institute
KeywordsTendonDifferential scanning calorimetryThermal stabilityIsometric exerciseSoft tissueChemistryAnatomyMedicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Soft tissue injuries are poorly understood at the molecular level. Previous work using differential scanning calorimetry (DSC) has shown that tendon collagen becomes less thermally stable with rupture. However, most soft tissue injuries do not result in complete tissue rupture but in damaging fiber overextension. Covalent crosslinking, which increases with animal maturity and age, plays an important role in collagenous fiber mechanics. It is also a determinant of tissue strength and is hypothesized to inhibit the loss of thermal stability of collagen due to mechanical damage. Controlled overextension without rupture was investigated to determine if overextension was sufficient to reduce the thermal stability of collagen in the bovine tail tendon (BTT) model and to examine the effects of aging on the phenomenon. Baseline data from DSC and hydrothermal isometric tension (HIT) techniques were compared between two groups: steers aged 24-30 months (young group), and skeletally mature bulls and oxen aged greater than five years (old group). Covalent crosslinks were quantified by ion exchange chromatography. Overextension resulted in reduced collagen thermal stability in the BTT model. The Young specimens, showing detectably lower tissue thermomechanical competence, lost more thermal stability with overextension than did the old specimens. The effect on old specimens, while smaller, was detectable. Multiple overextension cycles increased the loss of stability in the young group. Compositional differences in covalent crosslinking corresponded with tissue thermomechanical competence and therefore inversely with the loss of thermal stability. HIT testing gave thermal denaturation temperatures similar to those measured with DSC. The thermal stability of collagen was reduced by overextension of the tendon--without tissue rupture--and this effect was amplified by increased cycles of overextension. Increased tissue thermomechanical competence with aging seemed to mitigate the loss of collagen stability due to mechanical overextension. Surprisingly, the higher tissue thermomechanical competence did not directly correlate with the concentration of endogenous enzymatically derived covalent crosslinking on a mole per mole of collagen basis. It did, however, correlate with the percentage of mature and thermally stable crosslinks. Compositional changes in fibrous collagens that occur with aging affect fibrous collagen mechanics and partially determine the nature of mechanical damage at the intermolecular level. As techniques develop and improve, this new information may lead to important future studies concerning improved detection, prediction, and modeling of mechanical damage at much finer levels of tissue hierarchy than currently possible.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 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

Citations40
Published2010
Admission routes2
Has abstractyes

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