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Record W4224271180 · doi:10.1021/acs.estlett.2c00186

Maternal Exposure to Polystyrene Micro- and Nanoplastics Causes Fetal Growth Restriction in Mice

2022· article· en· W4224271180 on OpenAlexafffund
Zahra Aghaei, John G. Sled, John‏ Kingdom, Ahmet Baschat, Paul A. Helm, Karl J. Jobst, Lindsay S. Cahill

Bibliographic record

VenueEnvironmental Science & Technology Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsMount Sinai HospitalHospital for Sick ChildrenUniversity of TorontoMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaBanting Research Foundation
KeywordsMicroplasticsFetusFetal growthGestationPregnancyPolystyreneAndrologyPhysiologyChemistryMedicineToxicologyBiologyEnvironmental chemistryPolymer

Abstract

fetched live from OpenAlex

Plastics are ubiquitous and, when released into the environment, break down into smaller particles termed microplastics (MPs) and nanoplastics (NPs). These MPs and NPs can be ingested by organisms and potentially accumulate in tissues and organs. Recently, MPs were found in the placentas of healthy women, raising the concern that exposure to plastics may have an impact on pregnancy and fetal development. In this study, we investigated the effect of maternal exposure to plastics on fetal and placental growth using experimental mice. The dams exposed to plastics received either 5 μm or 50 nm polystyrene plastics in filtered drinking water at one of three concentrations (10 2, 10 4, or 10 6 ng/L). In late gestation, MP- and NP-exposed fetuses were significantly growth restricted, with a 12% decrease in fetal weight at the highest exposure concentration. This study represents a crucial first step toward evaluating the risks to human pregnancies posed by exposure to plastics.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.003
GPT teacher head0.168
Teacher spread0.165 · 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

Citations101
Published2022
Admission routes2
Has abstractyes

Explore more

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