MétaCan
Menu
← Back to cohort

Amniotic Fluid Inflammatory Profile is Altered by Prenatal Cigarette Smoke Exposure

2025· article· en· W4410276229 on OpenAlexaff
Jorge Quintana Aguilar, Hala Mohamed, D.H.D. Mostafa, M. Sosa Henríquez, Ezra L. Clark, Ivan K. Domingo, Michaela A. Riddell, Christopher D. Pascoe

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of AlbertaUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsMedicineAmniotic fluidCigarette smokeSmokePregnancyFetusEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction: Fetal breathing movements permit the exchange of fetal lung fluid with amniotic fluid (AF), maintains optimal lung distention, and is vital for pulmonary cell differentiation and lung development. Elevated IL-1β, IL-5, and TNF-α in AF promotes offspring airway hyperreactivity and induces fetal lung inflammation, all of which are associated with chronic lung disease. Therefore, AF composition, and factors that alter it, may directly impact the developing lungs and could affect disease risk. AF may link the external environment and the developing lungs. Previously, we reported that the AF has a unique cytokine/chemokine profile when compared to maternal blood (MB), cord blood (CB), and placenta (PL). But whether this profile can be modified by external stimuli is unknown. Prenatal exposure to cigarette smoke (CS) increases risk for chronic lung disease; therefore, we hypothesize that maternal CS exposure alters the AF cytokine/chemokine profile. Methods: Matched AF, MB, CB, and PL were collected from patients undergoing a term caesarean delivery. Information on CS exposure was collected by questionnaire, and cotinine levels were measured using a direct ELISA to confirm exposure. Protein was isolated from PL samples using a RIPA buffer, and then all samples were assayed for 99 cytokines/chemokines using a multiplex array. Profiles were compared using partial least squares-discriminant analysis (PLS-DA) and differential abundance analysis. Data is presented as mean±SD. Results: There were no significant differences in the clinical measurements or demographics between cotinine negative or positive samples (Table 1). Gestational age was trending lower for cotinine positive samples and may require adjustment in downstream analyses. 27% of AF samples (n=22) had detectable cotinine (51.04±11.90 ng/mL) at levels 1.62-fold higher than CB (31.52±22.78 ng/mL, p<0.01) and 1.45-fold higher than MB (35.13±24.46 ng/mL, p<0.05). Cotinine was undetected in all PL samples. Cotinine-positive AF samples have higher levels of IP-10 (7552.08±6143.49 vs. 690.20±1103.64 pg/mL, p<0.001), CXCL9 (838.85±635.83 vs. 250.73±193.06 pg/mL, p<0.005), IL-10 (7.81±3.52 vs. 4.02±2.23 pg/mL, p<0.01), and IL-6 (992.37±557.50 vs. 513.23±430.35 pg/mL, p<0.05) relative to cotinine-negative samples. These cytokines did not differ in the CB cotinine-positive relative to -negative samples. Conclusions: CS exposure alters the AF profile, increasing the abundance of both pro- and anti-inflammatory mediators. Additionally, prenatal CS exposure leads to cotinine bioaccumulation in AF. These results provide evidence that the AF composition can be influenced and may be an important communication pathway between the external environment and the developing lungs.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Respiratory and Critical Care Medicine→Same topicAir Quality and Health Impacts→French-language works237,207→