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Record W2345373512 · doi:10.3920/wmj2015.1981

The metabolic responses of HepG2 cells to the exposure of mycotoxin deoxynivalenol

2016· article· en· W2345373512 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Mycotoxin Journal · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of ChinaShanghai Jiao Tong UniversityChinese Academy of Sciences
KeywordsMetabolomicsMycotoxinOxidative stressMetabolic pathwayBiologyMetabolomeToxicityToxicologyPhysiologyBiotechnologyBioinformaticsChemistryMetabolismBiochemistry

Abstract

fetched live from OpenAlex

As the number of reported deoxynivalenol (DON) contamination incidents increased steadily over the past decades, there has been a widespread interest in understanding the cellular mechanisms of the toxicological effects of DON using in vitro systems and omics technologies. The present investigation was conducted to understand the metabolomic changes in human hepatocellular carcinoma cells (HepG2) exposed to 10 μM DON for short term (4 h) and long term (12 h) periods, using a non-targeted metabolomics approach. Our results revealed a remarkable metabolic shift from short term to long term exposure to DON in HepG2 cells. Our metabolomics data also confirmed the role of DON induced oxidative stress in DON toxicity. Coupled with pattern recognition and pathway analysis, effects of DON on redox homeostasis, energy balance, lipid metabolism, and potential toxicological mechanisms were discussed, which would facilitate further studies on the risk assessment of the dietary mycotoxin DON.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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