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Enregistrement W2807484049 · doi:10.1113/jp276078

Probing under pressure: a look inside the compartmental haemodynamics of skeletal muscle during rest and contraction

2018· letter· en· W2807484049 sur OpenAlexafffund
Carolina Arana, Brittney Swanson, Samantha L. Kuzyk

Notice bibliographique

RevueThe Journal of Physiology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueCardiovascular and exercise physiology
Établissements canadiensOkanagan University CollegeUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British ColumbiaInterior Health
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésRest (music)Contraction (grammar)Skeletal muscleHemodynamicsMuscle contractionInternal medicineCardiologyChemistryMedicine

Résumé

récupéré en direct d'OpenAlex

The cardiovascular system supplies oxygen (O2) and nutrients to the muscle to meet increasing physiological demands during exercise. Small O2 stores in living species require a formulated and specific balance between O2 delivery () and utilization (). This tightly regulated matching of and is a critical determinant of the driving pressure of O2 () into the myocyte. Through passive diffusion, O2 travels from the atmosphere and into the lungs, diffusing into the relatively low present in mixed pulmonary arterial-venous blood at the alveoli. Once O2 binds to red blood cells, the oxygenated blood travels through the cardiovascular tree, and eventually offloads O2 into the mitochondria within skeletal muscle. The differences in can be detected across the blood and muscle cell boundary – a gradient that is necessary for the effective diffusion of O2. The dynamic flow within the system is also determined by the capacity for tissue O2 diffusion (), which is directly proportional to the area of the tissue and inversely proportional to its thickness. This relationship is known as Fick's Law of Diffusion ( = (Δ)); where Δ is the difference between the partial pressure of the microvascular () and interstitial spaces () within the skeletal muscle. Therefore, elevations in imposed by muscle contractions must be matched by effective O2 delivery and diffusion. Facilitated diffusion through the capillary network into the plasma, endothelium and interstitium of the muscle is compromised due to greater resistance to O2 flux. This region, also known as the carrier-free region (CFR), is a transport barrier for transcapillary O2 flux. The impedance is thought to be caused by the absence of an O2 carrier and discrepancies in the surface area involved in the diffusion process. Hence, a functionally large gradient between and is thought to reside in these areas. measurement within different compartments can be performed using the phosphorescence quenching technique due to its excellent temporal resolution and precision during transcapillary Δ alterations. To further explore the use of this technique, a recent publication in The Journal of Physiology by Hirai et al. (2018) assessed the dynamics of skeletal muscle and in the exposed rat spinotrapezius to determine the transcapillary O2 gradients during the rest–contraction transient. Multiple experimental methods including phosphorescence quenching, transmission electron microscopy (TEM) and computer simulations were used to illustrate the diffusion pathway between red blood cells and the adjacent sarcolemma. The authors’ estimable discoveries were the following: (1) a substantial gradient was sustained during rest and submaximal twitch contractions; (2) the CFR was the site where the gradient resided; (3) the elevation in O2 transcapillary flow during contractions was caused by modulated enhancements of , since Δ was not altered. This biomechanical diffusion system was quantified and microscopically illustrated using an impressive array of techniques. It is important to note that phosphorescence lifetime microscopy has been used previously to measure changes through phosphorescence of exogenous probes. Oxyphor G4 belongs to the group of dendritic O2 probes that are highly soluble in aqueous environments and do not permeate biological membranes (Koga et al. 2012). They are also able to operate in both albumin-rich (blood plasma) and albumin-free (interstitial space) environments at physiological O2 pressures (Koga et al. 2012). Clear profiles of muscle deoxygenation during the transition from rest to contraction may be obtained through formulated phosphorescence quenching relationships by allowing continual assessment of Δ during exercise, making it ideal for the current study design (Koga et al. 2012). Manipulation of the exogenous probes was thoroughly described in the methodology of the study by Hirai et al. (2018). Phosphorescence quenching by O2 followed the Stern–Volmer relationship. Proper calibration of the probes involved determining the dependencies of the phosphorescence lifetime in the absence of O2 (τ°), and the quenching constant (kQ), on variables like temperature and pH under experimental conditions such as albumin concentration (Dunphy et al. 2002). It should be noted that simple, yet important methodological details such as pH and temperature were reported by the authors – a transparent approach that should be mandatory across all related investigations. Nonetheless, regarding the effect of albumin on the previously mentioned calibration constants, further comment is necessary. In a similar study conducted by Dunphy et al. (2002), the kQ of the Oxyphor G2 was found to dramatically decrease with an increase in albumin concentration in the biological environment. However, Hirai et al. (2018) made great efforts to assure the elimination of albumin contamination in the study, as the kQ and varying τ° were adjusted based on the spinotrapezius muscle surface temperature. Thus, no significant alterations in either blood pH or albumin concentration were expected under their experimental conditions. Additionally, the effect of light on the phosphorescence quenching technique also deserves further comment. Importantly, Koga et al. (2012) stated that conducting these experiments in a dark space was a gold standard requirement when quantifying and via the Stern–Volmer relationship. Similar to the methods outlined previously (Hirai et al. 2013), Hirai et al. (2018) used a dark space to conduct these measurements, which prevents interference of polluting pigments introduced by ambient light. It is also important to note that the quenching of O2 in their experiments also causes some degree of O2 photoconsumption (i.e. photo-oxidation). However, no significant reductions in resting and were observed over time, which disproved the possibility of contamination by O2 photoconsumption in their experimental protocols. Furthermore, an additional methodology was outlined to rectify possible photo-oxidation, which involved a combination of a small excitation area, a low flash rate, and the alternation of the excitation spot. From an animal-to-human model standpoint, the rat model closely resembles the pulmonary and cardiovascular tree observed in humans. Hirai et al. (2018) used 12 young male (3–4 months) Sprague-Dawley rats, but preliminary experiments, used to confirm previous findings by others (e.g. Dunphy et al. 2002), eliminated four rats due to confinement of the G4 probe to the interstitial space. This caused a signal overlap which resulted in data for measurements being reported for only eight rats. In contrast, for TEM imaging, samples were obtained from 16 young male Sprague-Dawley rats to analyse the distance between the sarcolemma, the red blood cell, and the different volumes of interstitial, intracellular and microvascular spaces. It was determined that there was no difference between the gradients, and O2 transport between the blood and the myocyte was limited. Importantly, the rats in the current study were kept in a cage and were considered sedentary as reported in a study published by the same research group (Hirai et al. 2013). Maintaining similar physical activity levels across the rats used in the experiments is important since physical activity has previously been demonstrated to modulate skeletal muscle responses to the rest–contraction transient (Hirai et al. 2012). This undoubtedly landmark study by Hirai et al. (2018) is the first to determine and during rest and contraction in the rat's skeletal muscle. The authors demonstrated that the blood–myocyte interface provides a substantial resistance to O2 diffusion at rest and during contractions. This leads to the suggestion that modulations in microvascular haemodynamics and red blood cell distribution constitute the primary mechanisms driving the movement of O2 during contractions. Evidently, minimally invasive and rapid measurements of in living tissue can substantially increase our understanding of both normal and abnormal physiological phenomena. These include regulation of blood flow and clinical pathologies resulting from mismatch of delivery and utilization of O2 within the active muscle. Empirical evidence from this study also attempted to resolve these perturbations by demonstrating that adaptations along the O2 transport pathway can be promoted by exercise training. It was evident that training leads to a rise in responses following contractions through the observable rise in the upstream O2 diffusion pressure for transcapillary flux (Hirai et al. 2018). Importantly in this study, the physical activity of the rats, which has previously been demonstrated to have an impact on the skeletal muscle response to the rest–contraction transient (Hirai et al. 2012), was controlled. The study by Hirai et al. (2018) highlights the importance for future work of investigating the mechanisms related to increased transmural O2 flux with contractions. None declared. Ms Samantha L. Kuzyk is funded by an NSERC CGS master's grant. We would like to thank Mr Michael M. Tymko (UBC-Okanagan Campus) for guidance and insightful feedback on the current article.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,003
Communication savante0,0030,005
Science ouverte0,0010,002
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,015
Tête enseignante GPT0,251
Écart entre enseignants0,236 · 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
Publié2018
Routes d'admission2
Résumé présentoui

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