The Capillary‐Pericyte Relationship in Skeletal Muscle: Implications for Blood Flow Control
Notice bibliographique
Résumé
Introduction Pericytes, mural cells of the vasculature, are found adjacent to endothelial cells of capillaries and venules. In the brain, pericytes have been shown to be involved in blood flow control to active nerve tissue. In skeletal muscle, capillaries have been found to be important in directing blood flow to active skeletal muscle fibres. Active skeletal muscle fibres signal capillary endothelial cells to initiate signals that are transmitted upstream, along the blood vessel wall, to cause vasodilation of the upstream arteriole controlling the perfusion of the stimulated capillary. Pericytes have been shown to be associated with the microvasculature in skeletal muscle. If pericytes are closely associated with the capillary wall and capillary endothelial cells, they may be involved in the transmission of the upstream arteriolar vasodilatory signals that control capillary perfusion and blood flow to active skeletal muscle fibres. In order for this to be possible pericytes would have to have to form a continuous, connected layer of cells adjacent to the capillary network such that signals could be passed from pericyte to pericyte upstream to associated arterioles. In order to investigate the potential role of pericytes in blood flow control to skeletal muscle capillaries we investigated 1) whether pericytes were associated with capillaries in skeletal muscle and 2) whether pericytes form a continuous layer adjacent to the capillary network. Methods We used histological techniques, sectioning male mouse slow‐oxidative (soleus (SOL)), and fast‐oxidative‐glycolytic muscle (diaphragm (DIA)) both cross sectionally and longitudinally in order to identify capillary endothelial cells (using isolectin) in cross section and to view capillaries along their length. We then double stained these sections with neural‐glial antigen 2 (NG2) to identify the association of pericytes with capillary endothelial cells. Results Cross sectional analysis of 3 SOL muscles (273 capillaries counted) showed that 63.2+/‐4.9% (avg+/‐ std. dev.) of capillaries were associated with pericytes while 36.8+/‐4.9% of capillaries were not associated with pericytes. Cross sectional analysis of 2 DIA muscles (208 capillaries counted) showed that 53.6+/‐7.7% of capillaries were associated with pericytes while 46.4+/‐7.7% of capillaries were not associated with pericytes. In both muscle types, analysis of longitudinal sections, where capillaries could be viewed along their length, showed a lack of consistent association with pericytes. Conclusions We identified that, while there was an association between pericytes and capillary endothelial cells in skeletal muscle, not all capillary endothelial cells were associated with pericytes. Further, when capillaries were viewed along their length, pericytes were not consistently located adjacent to the capillary network. Our data indicate that pericytes may not form a continuous layer alongside the capillary network and may not be able to conduct vasodilatory signals from pericyte to pericyte to communicate with upstream arterioles and, therefore, may not be able to alter blood flow to active skeletal muscle fibres.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».