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Surfactant dysfunction due to e-cigarette aerosol exposure with and without additional insults

2023· article· en· W4378648699 on OpenAlexaff
Emma Graham, Lynda McCaig, Akash Tejura, Gloria Shui-Kei Lau, Anne Cao, Ruud A. W. Veldhuizen

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPulmonary surfactantAerosolChemistrySurface tensionOxidative stressPulmonary complianceLungInhalationChromatographyBiochemistryAnesthesiaInternal medicineMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.247
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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