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Enregistrement W2887919367 · doi:10.1113/jp275978

Reply from Luca Ruggiero, Alexandra F. Yacyshyn, Jane Nettleton and Chris J. McNeil

2018· letter· en· W2887919367 sur OpenAlexafffund
Luca Ruggiero, Alexandra F. Yacyshyn, Jane Nettleton, Chris J. McNeil

Notice bibliographique

RevueThe Journal of Physiology · 2018
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueHigh Altitude and Hypoxia
Établissements canadiensUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInterior Health
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésPsychologyMedicineHypoxia (environmental)HumanitiesPhysical medicine and rehabilitationArtChemistryOxygen

Résumé

récupéré en direct d'OpenAlex

In their letter to the Editor regarding our recent paper, Finn and colleagues propose that the heightened motoneurone responsiveness in chronic hypoxia (CH) compared to acute hypoxia (AH) may relate primarily to greater peripheral fatigue in AH than CH, rather than motoneuronal adaptations to hypoxia. This is an astute observation and, based on the reduction of maximal torque and increase in EMG at the end of the fatigue protocol, an interpretation that could have been addressed in the original article. However, for the reasons described below, we do not believe peripheral fatigue to be a driving influence for our results. We also take this opportunity to discuss the issue of study design (i.e. matched torque vs. matched EMG). Of note, all of the values we report refer to a relative increase or decrease from baseline. After 3 min of exercise, there is a ∼35% reduction in motoneurone excitability (as measured by the cervicomedullary motor evoked potential, CMEP) during a matched EMG contraction in both AH and normoxia (N), whereas the CMEP in CH is reduced only 3% (see Fig. 5B of Ruggiero et al. 2018). Just prior to this time point, the absolute increase in integrated EMG (iEMG) during a matched torque contraction is 13% for AH, 5% for N, and 4% for CH. This AH iEMG is similar to the end-exercise value in CH (+11%), yet the reduction in the CMEP is 36% for AH at 3 min but only 23% for CH at 16 min. Further, despite the absence of a meaningful increase in iEMG from 3 min to 5 min (+3%) in CH, the CMEP at 5 min is reduced 18% and relatively stable thereafter. In N, the CMEP is stable beyond 3 min, despite a continued, gradual increase in iEMG (22% at 16 min). In AH, the CMEP nears its lowest value at 7 min, when the increase in iEMG (28%) is less than half that at task termination (58%). Based on these findings, it is clear that the size of the CMEP is influenced strongly by factors other than the level of peripheral fatigue inferred by EMG. Moreover, the (non-significant) greater reduction in maximal torque in CH compared to N, without a decrease in CMEP size in CH, weakens the link between motoneurone excitability and this alternative index of fatigue. The iEMG data described above argue against a tight relationship between the CMEP and peripheral fatigue; this argument is strengthened if the voluntary EMG data (either integrated or root mean square) are normalized to account for fatigue-related changes to the maximal M-wave (Mmax). When normalized in this fashion, the increase in EMG is equivalent for CH and N and the separation between these conditions and AH is reduced. Although none of the changes are statistically significant (see Fig. 5A of Ruggiero et al. 2018), Mmax size increases with fatigue in N and AH but decreases in CH. To compensate for this reduction in peripheral excitability, greater descending drive would be required in CH compared to N and AH to achieve a targeted level of iEMG. Hence, in a protocol of matched EMG instead of torque, there is the potential to simply flip which condition is likely to experience the greatest fatigue-related increase in motoneurone activation. In the work that compared CMEPs of different sizes (McNeil et al. 2011), the conditions of the fatigue task were identical across the two sessions. With the current study, there were three different conditions (N, AH and CH) and, given the dearth of similar experiments, a limited ability to tailor our design according to previous findings. The nature of the expedition to 5050 m permitted only one attempt to address our research question. After weighing the benefits and limitations of various experimental protocols, we opted for a design that aligned with the acclimatization studies of whole-body exercise (e.g. Goodall et al. 2014); i.e. we chose a submaximal, intermittent protocol with a target (torque) that relates to muscle performance outside of a laboratory setting. As suggested by Finn and colleagues (2018), we agree that it would be prudent for future work to investigate the impact of hypoxia (acute and chronic) on motoneurone excitability during a fatiguing task with only an EMG target. However, based on our novel findings, it is advisable to adjust the EMG target to account for real-time changes to the Mmax. This would be true not only for the environmental conditions considered here, but for any experimental design in which multiple conditions are compared. We thank Finn and colleagues for their interest in our study. It provided us the opportunity to expand on our rationale and interpretations of the data and also make a recommendation for future study design. None declared.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,025
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0030,004
Science ouverte0,0020,002
Intégrité de la recherche0,0180,030
Charge utile insuffisante (le modèle a refusé de juger)0,0070,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,232
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission2
Résumé présentoui

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