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Enregistrement W3017189925 · doi:10.1096/fasebj.2020.34.s1.03899

Effect of Cold Exposure and Exercise on Carbohydrate and Lipid Metabolism in Persons with Cervical Spinal Cord Injury

2020· article· en· W3017189925 sur OpenAlexaff
Kazunari Nishiyama, Yoshi‐ichiro Kamijo, Tomonori Nakata, Jan W. van der Scheer, Fumihiro Tajima

Notice bibliographique

RevueThe FASEB Journal · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueExercise and Physiological Responses
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésSupine positionMedicineInternal medicineGlycogenSpinal cord injurySpinal cordEndocrinologyAnesthesiaAnimal scienceBiology

Résumé

récupéré en direct d'OpenAlex

Objective Rates of retiring for Oita International Wheelchair Marathon in the last decade were higher with the lower atmospheric temperature below 22°C. The tendency was stronger in persons with cervical spinal cord injury (CSCI) than the other classes ( unpublished data ), which may be associated with deteriorated lipid utilization and a faster depletion of glycogen in a whole body. Our purpose was to examine whether serum profile of lipid in CSCI during cold stress and exercise was different from able‐body persons (AB) or varied dependent on the injury level. Methods [Protocol1] Nine CSCI and 11 AB wore a water‐perfused suit and took a supine position then 33‐°C water was perfused into the suit. After 10‐min measurement of thermoneutral condition, 25‐°C water was perfused for 20min (CS), then perfused 33‐°C water for 60min, while monitoring esophageal (T es ) and mean skin temperatures (T sk ). Blood samples were taken before, just after, 60‐ and 120min after CS. [Protocol2] Six of each CSCI and AB did a 30‐min arm crank exercise at 50% VO 2peak then took a sitting position for 60min as a recovery. Blood samples were taken before, just after and an hour after exercise. [Protocol3] Bloods were sampled from 5 CSCI and 9 thoracic and lumbar SCI (LSCI), who completed a half marathon, before, just after and an hour after the race. Plasma concentrations of adrenaline ([Ad] p ), noradrenaline ([Nor] p ), and glucose ([Glc] p ), and serum concentrations of insulin ([Ins] s ), free fatty acid ([FFA] s ), and total ketone bodies ([tKB] s ) were assessed in all protocols. Results [Protocol1] Tsk decreased by ~2 °C during CS and Tes decreased by ~0.2 °C after CS with no significant differences between groups. VO 2 was similar between groups and remained unchanged throughout the study. Respiratory quotient was significantly decreased in AB after CS but remained unchanged in CSCI. [Ad] p and [Nor] p were lower in CSCI than AB and [Nor] p significantly increased only in AB during recovery. [Glc] p before, immediately and one hour after CS was higher in CSCI but it decreased to the same level as AB 2 hours later. [FFA] s did not differ between groups and decreased after CS in only AB. [tKB] s significantly increased after CS in both groups, but the increase started from 1‐hour recovery in CSCI. [Protocol2] [Ad] p and [Nor] p increased just after exercise in AB but not in CSCI. [Glc] p remained unchanged in both groups but [Ins]s started to decrease just after exercise in CSCI, earlier than AB. [tKB] s increased from the baseline an hour after exercise in CSCI but not in AB, while [FFA] s did not differ between groups. [Protocol3] [Ad] p and [Nor] p was lower in CSCI than LSCI and increased just after the race only in LSCI and returned to the baseline at an hour after the race. [FFA] s increased just after the race in CSCI and remained the higher level at an hour after race, then [tKB] s increased an hour after the race only in CSCI. Both lipid profiles remained unchanged throughout the study in LSCI. Conclusion It is suggested that lipolysis and ketogenesis are likely to enhance in CSCI during cold or exercise. Lipid and glucose metabolisms vary dependent on the injury level with SCI. Support or Funding Information This work was funded by Nachikatsuura Research foundation and Kyoten, Wakayama Med. Univ.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,017
Tête enseignante GPT0,281
Écart entre enseignants0,264 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2020
Routes d'admission1
Résumé présentoui

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