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Record W4411495252 · doi:10.3390/nano15130954

Sublethal Toxicity and Gene Expression Changes in Hydra vulgaris Exposed to Polyethylene and Polypropylene Nanoparticles

2025· article· en· W4411495252 on OpenAlexaff
J. L. Auclair, C. André, François Gagné

Bibliographic record

VenueNanomaterials · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsPolypropyleneToxicityLernaean HydraPolyethyleneNanoparticleGene expressionChemistryGeneChemical engineeringCell biologyBiologyMaterials scienceBiochemistryNanotechnologyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Plastic nanoparticles (NPs) released from plastic breakdown pervade aquatic ecosystems, raising concerns about their long-term toxic effects in aquatic organisms. The purpose of this study was to examine the sublethal toxicity of polyethylene (PeNPs) and polypropylene (PpNPs) nanoparticles of the same size (50 nm diameter) in Hydra vulgaris. Hydras were exposed to increasing concentrations of PeNPs and PpNPs (0.3–10 mg/L) for 96 h at 20 °C. Toxicity was determined based on the characteristic morphological changes and gene expression analysis of genes involved in oxidative stress, DNA repair, protein salvaging and autophagy, neural activity and regeneration. The data revealed that PpNPs produced morphological changes (50% effects concentration EC50 = 7 mg/L), while PeNPs did not. Exposure to both nanoplastics produced changes in gene expression in all gene targets and at concentrations less than 0.3 mg/L in some cases. PpNPs generally produced stronger effects than PeNPs. The mode of action of these plastic polymers differed based on the intensity of responses in oxidative stress (superoxide dismutase, catalase), DNA repair of oxidized DNA, regeneration and circadian rhythms. In conclusion, both plastics’ nanoparticles produced effects at concentrations well below the appearance of morphological changes and at concentrations found in highly contaminated environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.048
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.217
Teacher spread0.208 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

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