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Record W2054079587 · doi:10.1177/1091581810395332

Fetal and Neonatal Exposure to Nicotine Disrupts Postnatal Lung Development in Rats: Role of VEGF and Its Receptors

2011· article· en· W2054079587 on OpenAlexafffund
Maria A. Petre, Jim Petrik, Russ Ellis, Mark D. Inman, Alison C. Holloway, N. R. Labiris

Bibliographic record

VenueInternational Journal of Toxicology · 2011
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of GuelphMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsNicotineOffspringWeaningLungMedicineFetusPhysiologyPregnancyNicotine replacement therapyInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Many women are unable to quit smoking during pregnancy and therefore are prescribed drugs, including nicotine (nicotine replacement therapy [NRT]), to aid with smoking cessation. However, the consequences to the offspring of pregnant NRT users have not been well studied. The goals of this study were to determine the consequences of fetal and neonatal exposure to nicotine on lung development and function. Female rats were exposed to nicotine for 2 weeks prior to mating until weaning. Lungs were collected from saline and nicotine-treated rats from birth to adulthood to assess postnatal lung structure and function. Although nicotine exposure altered alveolarization at weaning, an effect that resolved by adulthood, it did not affect lung function at any of the ages investigated. However, nicotine exposure significantly decreased lung vascularization. The current study suggests that perinatal exposure to nicotine alters lung development, an effect which may be mediated via decreased vascular endothelial growth factor (VEGF) signaling.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.352
Teacher spread0.320 · 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".

Quick stats

Citations30
Published2011
Admission routes2
Has abstractyes

Explore more

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