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Record W2209978983 · doi:10.1152/jappl.2000.89.5.1928

Downhill running preferentially increases CGRP in fast glycolytic muscle fibers

2000· article· en· W2209978983 on OpenAlexaff
Darlene A. Homonko, Elizabeth Theriault

Bibliographic record

VenueJournal of Applied Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsCalcitonin gene-related peptideGlycolysisChemistryBiophysicsEndocrinologyAnatomyBiologyBiochemistryMetabolismNeuropeptide

Abstract

fetched live from OpenAlex

Calcitonin gene-related peptide (CGRP) is present in some spinal cord motoneurons and at neuromuscular junctions in skeletal muscle. We previously reported increased numbers of CGRP-positive (CGRP+) motoneurons supplying hindlimb extensors after downhill exercise (Homonko DA and Theriault E, Inter J Sport Med 18: 1-7, 1997). The present study identifies the responding population with respect to muscle and motoneuron pool and correlates changes in CGRP with muscle fiber type-identified end plates. Twenty seven rats were divided into the following groups: control and 72 h and 2 wk postexercise. FluoroGold was injected into the soleus, lateral gastrocnemius, and the proximal (mixed fiber type) or distal (fast-twitch glycolytic) regions of the medial gastrocnemius (MG). Untrained animals ran downhill on a treadmill for 30 min. The number of FluoroGold/CGRP+ motoneurons within proximal and distal MG increased by 72 h postexercise (P<0.05). No significant changes were observed in soleus or lateral gastrocnemius motoneurons postexercise. The number of alpha-bungarotoxin/CGRP+ motor end plates in the MG increased exclusively at fast-twitch glycolytic muscle fibers 72 h and 2 wk postexercise (P<0.05). One interpretation of these results is that unaccustomed exercise preferentially activates fast-twitch glycolytic muscle fibers in the MG.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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.

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".

Quick stats

Citations14
Published2000
Admission routes1
Has abstractyes

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