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UNILATERAL TENDON INJURY ACCELERATES TENDON MINERALISATION BILATERALLY AND RESULTS IN ALTERED TENDON FAILURE AND VISCOELASTIC PROPERTIES

2013· article· en· W1981835394 on OpenAlexaff
E. J. O. O'Brien, Cyril B. Frank, Nigel G. Shrive, Benedikt Hallgrímsson, David A. Hart

Bibliographic record

VenueBritish Journal of Sports Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTendonMedicineAchilles tendonBiomechanicsHindlimbCreepAnatomyStiffnessMaterials science

Abstract

fetched live from OpenAlex

Introduction In humans, mineralised tendons are known to be weaker, possibly as a result of underuse due to pain. Underuse is also reported to cause an increase in hysteresis and a decrease in tendon stiffness. However, the impact of mineralisation on these other tendon properties is unknown. We have developed a needle-injury model which accelerates the spontaneous peritendinous mineralisation of murine Achilles tendons.1 Plain radiography suggests that unilateral injury accelerates mineralisation bilaterally. We hypothesised, therefore, that needle-injured murine Achilles tendons would show reduced strength and stiffness and increased creep (related to hysteresis) compared with contralateral non-injured tendons and tendons from non-injured controls. In addition, we hypothesised that unilateral injury would accelerate mineralisation bilaterally. Methods Ten week old male C57Bl/6 mice (Charles River) underwent left hind (LH) needle-injury and were sacrificed after 20 weeks along with non-injured controls. Hind limbs were examined by micro CT before testing isolated Achilles tendons using the following protocol: 30 cycles of loading between 0.23 and 4.5 N; 300 s dwell at 4.5 N; 495 s dwell at 0.23 N; and a ramp to failure at 5 mm/s. Creep was calculated as the percentage change in gauge length between the first loading peak and the end of the dwell at 4.5 N. Tangent stiffness was calculated from the slope of the force/displacement ramp to failure plot over the range 40–80% of failure force. Within and between group differences were examined using paired T and exact Mann-Whitney U tests respectively. Results Figure 1 shows that within injured individuals the volume of mineralisation was greater (p=0.01) in the LH compared with the right hind (RH) leg, and within non-injured individuals there was no difference between hind limbs (p=0.4). The volume of mineralisation was greater in the LH and RH of injured individuals compared with the respective limbs in non-injured individuals (both p=0.017). The mean failure force was lower (14.8±1.7 vs 16.7±0.7 N) and stiffness (22.9±2.7 vs 19.6±1.5N/mm) and creep (1.9±0.3 vs 3.1±1.2%) were higher in LH compared with RH limbs in six injured individuals (all P≤0.048). Differences between the mean values for seven LH limbs from injured (I) and three control individuals were not statistically significant for failure force (I=1.2 N lower, p=0.28), stiffness (I=3.5 N/mm higher, p=0.067) and creep (I=0.4% higher, p=0.067). All tendons failed close to the insertion except one injured tendon which failed partly in the mid substance and at the insertion. Figure 1. Mean volume (mm 3 ) of peri-Achilles tendon mineralisation in injured (I, n=7) and control (C, n=3) animals. Discussion Unilateral needle-injury accelerated tendon mineralisation bilaterally. In humans, tendon mineralisation and other orthopaedic diseases often occur bilaterally, which may partly relate to interaction between contralateral limbs such as by altered loading of a non-injured limb, signal circulation or bilateral neurological effects of injury. The model presented here is therefore an excellent one to study this phenomenon. Needle-injured mineralised tendons were weaker (consistent with human clinical data), but stiffer and showed less creep than contralateral controls. This suggests that in patients, tendon mineralisation may alter limb function unless muscular adaptation occurs. Most injured and control tendons failed close to the insertion, in contrast to a report of similar tests in which most tendons from adult C57Bl/6J mice failed in the mid substance,2 perhaps explained by potential sub strain differences.

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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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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