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Record W4414015727 · doi:10.11159/icbes25.123

Investigating the Cytotoxic and Calcium Signalling Impacts of Yellow 5 and Methyl Yellow Dyes in Mouse Nerve Cells

2025· article· en· W4414015727 on OpenAlexvenueno aff
Ryan Wen Liu, Sarah Chen, Chengbiao Wu, Linda Shi, Veronica Gomez‐Godinez

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsnot available
FundersUniversity of California, San Diego
KeywordsCytotoxic T cellNerve cellsCalciumCell biologyChemistryBiologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

The widespread use of artificial food dyes in consumables and cosmetics has raised critical concerns regarding their potential adverse effects on human health.This study evaluates the neuro-cytotoxic and calcium signaling impacts of Tartrazine (Yellow 5), a commonly used dye, and Methyl Yellow, a dye banned in the 1930s due to its toxicity.Using cultured mouse hippocampal nerve cells as a model, we assessed cell viability through Ethidium Homodimer III/Dead Red staining and fluorescence imaging after exposure to varying concentrations of Yellow 5 and Methyl Yellow.Calcium imaging with Fluo-4 revealed disruptions in calcium buffering and signaling pathways, which are critical for cellular homeostasis.These findings underscore the potential of artificial food dyes to compromise cellular function and amplify environmental stressors.In light of California's 2024 legislation banning six artificial food dyes, including Yellow 5, this study contributes new insights into food additive safety and emphasizes the necessity of rigorous oversight to safeguard consumer health.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicDye analysis and toxicityFrench-language works237,207