Surfactant dysfunction due to e-cigarette aerosol exposure with and without additional insults
Notice bibliographique
Résumé
Introduction: The use of e-cigarettes (ECs) remains a popular habit for young populations, in part due to the variety of appealing flavours available. However, EC use has been associated with acute lung injury via unknown mechanisms. During a deep inhalation, one of the first compounds the EC aerosol comes into contact within the lungs is pulmonary surfactant. This complex mixture of lipids and surfactant associated proteins lines the alveolar surface and reduces surface tension to near zero values upon exhalation. Surfactant dysfunction, associated with serum protein leak and oxidative stress, contributes to lung injury. We hypothesized that exposure to EC aerosol impairs pulmonary surfactant function, and thereby increases its susceptibility to protein inhibition or oxidative stress. Methods: Bovine lipid extract surfactant (BLES) was used as a model exogenous surfactant. 2ml of BLES (2mg/ml) was placed in a syringe (30ml) attached to an EC, drawing in and expelling the aerosol 30 times. Vehicle e-liquid (VG:PG 50:50) as well as e-liquid containing flavouring additives and nicotine were utilized. Two models of injury were used, the first being addition of serum containing plasma proteins and the second oxidization by hypochlorous acid following aerosol exposure. Surface tension reduction of all samples after exposure was performed using a constrained drop surfactometer (CDS), where samples underwent 20 dynamic compression and expansion cycles. Results: Minimum surfaces tensions were significantly higher after exposure to EC aerosol across 20 compression/expansion cycles. Menthol and red wedding flavoured aerosol exposure resulted in significantly increased minimum surface tensions compared to unflavoured vehicle e-liquid, although nicotine had no additional effects beyond that of the vehicle e-liquid. The addition of plasma containing serum proteins significantly increased minimum surface tensions in aerosol exposed samples compared to those unexposed to serum and air controls. Oxidized surfactant had higher minimum surface tensions compared to control, however EC aerosol exposure had no additional effect on the inhibition of the surfactant’s function. Conclusion: EC aerosols alter surfactant function through increases in minimum surface tension. Variability in the severity of inhibition exists between flavouring additives, however the base common across all e-liquids is able to effectively inhibit surfactant. This inhibition is amplified in the presence of serum proteins. From these results we conclude that vaping impairs the pulmonary surfactant system and increases susceptibility to damage by secondary insults. Lawson Health Research Institute, NSERC This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».