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Record W2606510632 · doi:10.3390/metabo7020015

Analysis of Sub-Lethal Toxicity of Perfluorooctane Sulfonate (PFOS) to Daphnia magna Using 1H Nuclear Magnetic Resonance-Based Metabolomics

2017· article· en· W2606510632 on OpenAlexafffund
Martha Kariuki, Edward G. Nagato, Brian P. Lankadurai, André J. Simpson, Myrna J. Simpson

Bibliographic record

VenueMetabolites · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersKrembil Foundation
KeywordsDaphnia magnaMetabolomicsMetaboliteToxicityEnvironmental chemistryChemistryPerfluorooctanePollutantBiochemistryChromatographySulfonateSodium

Abstract

fetched live from OpenAlex

1H nuclear magnetic resonance (NMR)-based metabolomics was used to characterize the response of Daphnia magna after sub-lethal exposure to perfluorooctane sulfonate (PFOS), a commonly found environmental pollutant in freshwater ecosystems. Principal component analysis (PCA) scores plots showed significant separation in the exposed samples relative to the controls. Partial least squares (PLS) regression analysis revealed a strong linear correlation between the overall metabolic response and PFOS exposure concentration. More detailed analysis showed that the toxic mode of action is metabolite-specific with some metabolites exhibiting a non-monotonic response with higher PFOS exposure concentrations. Our study indicates that PFOS exposure disrupts various energy metabolism pathways and also enhances protein degradation. Overall, we identified several metabolites that are sensitive to PFOS exposure and may be used as bioindicators of D. magna health. In addition, this study also highlights the important utility of environmental metabolomic methods when attempting to elucidate acute and sub-lethal pollutant stressors on keystone organisms such as D. magna.

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 score0.005

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 integrity0.0000.000
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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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

Citations62
Published2017
Admission routes2
Has abstractyes

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