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Record W1971268547 · doi:10.1152/ajpregu.00339.2001

Adaptation to lengthening contractions is independent of voluntary muscle recruitment but relies on inflammation

2002· article· en· W1971268547 on OpenAlexaff
Benoît M. Lapointe, Pierre Frémont, Claude H. Côt́e

Bibliographic record

VenueAmerican Journal of Physiology-Regulatory, Integrative and Comparative Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInflammationDiclofenacInternal medicineEndocrinologyPlaceboMedicineAnkleAnesthesiaAnatomyPathology

Abstract

fetched live from OpenAlex

Lengthening contractions trigger an adaptive response decreasing the susceptibility to exercise-induced muscle damage (EIMD). We hypothesized that 1) this adaptation can be observed when voluntary muscle recruitment is bypassed and 2) inflammation repression lessens the adaptive response. Rat ankle dorsiflexors were submitted to two bouts of elicited lengthening contractions 14 days apart; in vitro force production and macrophage concentrations were obtained before and 2 days after each bout in rats treated or not for 2 or 7 days with diclofenac. The first bout caused a 45% force deficit in the placebo group vs. 25% in the diclofenac group, whereas the ED1+ macrophage concentration increased by 10- and 5-fold, respectively. After the second bout, only diclofenac-treated rats (2 or 7 days) presented significant force deficits and increases in ED1+ and ED2+ macrophage concentrations, but this was more pronounced in the 7-day group. We conclude that adaptation to lengthening contractions does not depend on neural components but is likely mediated by strengthening of muscle structural/cellular elements and that inflammation is important for this process.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.087
GPT teacher head0.332
Teacher spread0.245 · 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

Citations75
Published2002
Admission routes1
Has abstractyes

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