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102 Nutrition Affects Tendon Healing In A Rat Model

2014· article· en· W2321344320 on OpenAlexfundno aff
Stefanie Korntner, Christine Lehner, Andreas Traweger, Nadja Kunkel, Hans‐Christian Bauer, Herbert Resch, Peter Augat, Herbert Tempfer

Bibliographic record

VenueAbstracts · 2014
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTendonStimulationAchilles tendonGaitMedicineInsulinSignificant differenceEndocrinologySurgeryAnatomyInternal medicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Introduction We have previously shown that human and rat tendon cells produce insulin and secrete it upon glucose stimulation. Moreover, the level of tendon insulin production is affected by the amount of glucose taken up by nutrition [Lehner, 2012]. We now hypothesise that nutritional glucose affects tendon healing in a rat model. Methods In 60 female Lewis rats full thickness defects were created in one Achilles tendon and left unsutured. The rats were randomly assigned to three groups, one was fed a high glucose diet, one a diet with low glucose/high fat and one a control diet, for 2 weeks each. Before surgery, one and two weeks after, gait analysis was performed using a NoldusTM catwalk system. After two weeks the animals were sacrificed and tendon size was measured and tendons were biomechanically tested an evaluated by various histological methods. Results Gait Analysis revealed a significant difference between the three groups one week after surgery. The intermediate toe spread (the range between second and fourth toe, a measure for the load on the limb) of the high glucose group is significantly increased one week p.o. (0,49 cm ± 0,07; n = 20; p < 0.01) compared to the control group (0,42 cm ± 0,08; n = 19) and to the high fat group (0,40 cm ± 0,12; n = 20) (Figure 1). Measurement of length and thickness of the newly formed tissue revealed a significant (p < 0.001) difference in tendon thickness of the newly formed tissue between the high-glucose group (4,26 mm ± 0,29; n = 20) and the control group (3,66 mm ± 0,39; n = 19) as well as between the high glucose and the high-fat group (4,32 mm ± 0,20; n = 20). Biomechanical testing revealed no significant difference between the groups in maximum tensile load, however, the new fibrous tissue from the glucose group is significantly (p < 0.05) stiffer (20,82 N/mm ± 8.08; n = 14) compared to the control group (15,07 N/mm ± 4.32; n = 14) (Figure 2). The stiffness of these tendons was similar to the stiffness of intact tendon tissue of the control group (20,63 N/mm ± 10,96; n = 14). Discussion Newly formed tendon tissue quality is affected by nutritional glucose. This finding is relevant for understanding diabetes related tendinopathy. Nutritional parameters may account for the interindividual variation of tendon quality and regeneration. The underlying molecular mechanisms will be examined. Reference Lehner et al. Horm Metab Res. 2012;44:506–510

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)0.000
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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".

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Citations0
Published2014
Admission routes1
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