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Exogenously applied muscle metabolites synergistically evoke sensations of muscle fatigue and pain in human subjects

2013· article· en· W2156842389 on OpenAlexaff
Kelly A. Pollak, Jeffrey D. Swenson, Timothy A. VanHaitsma, Ronald W. Hughen, Dae Hyun Jo, Kathleen C. Light, Petra Schweinhardt, Markus Amann, Alan R. Light

Bibliographic record

VenueExperimental Physiology · 2013
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsMcGill University Health Centre
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Heart, Lung, and Blood Institute
KeywordsHuman muscleMuscle fatigueMedicineMuscle contractionNeurosciencePsychologyPhysical medicine and rehabilitationPharmacologyElectromyographyInternal medicineSkeletal muscle

Abstract

fetched live from OpenAlex

NEW FINDINGS: What is the central question of this study? Can physiological concentrations of metabolite combinations evoke sensations of fatigue and pain when injected into skeletal muscle? If so, what sensations are evoked? What is the main finding and its importance? Low concentrations of protons, lactate and ATP evoked sensations related to fatigue. Higher concentrations of these metabolites evoked pain. Single metabolites evoked no sensations. This suggests that the combination of an ASIC receptor and a purinergic P2X receptor is required for signalling fatigue and pain. The results also suggest that two types of sensory neurons encode metabolites; one detects low concentrations of metabolites and signals sensations of fatigue, whereas the other detects higher levels of metabolites and signals ache and hot. The perception of fatigue is common in many disease states; however, the mechanisms of sensory muscle fatigue are not understood. In mice, rats and cats, muscle afferents signal metabolite production in skeletal muscle using a complex of ASIC, P2X and TRPV1 receptors. Endogenous muscle agonists for these receptors are combinations of protons, lactate and ATP. Here we applied physiological concentrations of these agonists to muscle interstitium in human subjects to determine whether this combination could activate sensations and, if so, to determine how the subjects described these sensations. Ten volunteers received infusions (0.2 ml over 30 s) containing protons, lactate and ATP under the fascia of a thumb muscle, abductor pollicis brevis. Infusion of individual metabolites at maximal amounts evoked no fatigue or pain. Metabolite combinations found in resting muscles (pH 7.4 + 300 nm ATP + 1 mm lactate) also evoked no sensation. The infusion of a metabolite combination found in muscle during moderate endurance exercise (pH 7.3 + 400 nm ATP + 5 mm lactate) produced significant fatigue sensations. Infusion of a metabolite combination associated with vigorous exercise (pH 7.2 + 500 nm ATP + 10 mm lactate) produced stronger sensations of fatigue and some ache. Higher levels of metabolites (as found with ischaemic exercise) caused more ache but no additional fatigue sensation. Thus, in a dose-dependent manner, intramuscular infusion of combinations of protons, lactate and ATP leads to fatigue sensation and eventually pain, probably through activation of ASIC, P2X and TRPV1 receptors. This is the first demonstration in humans that metabolites normally produced by exercise act in combination to activate sensory neurons that signal sensations of fatigue and muscle pain.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.027
GPT teacher head0.301
Teacher spread0.274 · 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

Citations239
Published2013
Admission routes1
Has abstractyes

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