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Record W1992571420 · doi:10.1371/journal.pone.0042117

Methylene Blue Protects against TDP-43 and FUS Neuronal Toxicity in C. elegans and D. rerio

2012· article· en· W1992571420 on OpenAlexafffund
Alexandra Vaccaro, Shunmoogum A. Patten, Sorana Ciura, Claudia Maios, Martine Therrien, Pierre Drapeau, Edor Kabashi, J. Alex Parker

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchInstitut National de la Santé et de la Recherche MédicaleALS Society of Canada
KeywordsZebrafishAmyotrophic lateral sclerosisBiologyNeuroprotectionCaenorhabditis elegansToxicityMethylene blueCell biologyTransgeneMolecular biologyPharmacologyChemistryBiochemistryPathologyMedicineGene

Abstract

fetched live from OpenAlex

The DNA/RNA-binding proteins TDP-43 and FUS are found in protein aggregates in a growing number of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and related dementia, but little is known about the neurotoxic mechanisms. We have generated Caenorhabditis elegans and zebrafish animal models expressing mutant human TDP-43 (A315T or G348C) or FUS (S57Δ or R521H) that reflect certain aspects of ALS including motor neuron degeneration, axonal deficits, and progressive paralysis. To explore the potential of our humanized transgenic C. elegans and zebrafish in identifying chemical suppressors of mutant TDP-43 and FUS neuronal toxicity, we tested three compounds with potential neuroprotective properties: lithium chloride, methylene blue and riluzole. We identified methylene blue as a potent suppressor of TDP-43 and FUS toxicity in both our models. Our results indicate that methylene blue can rescue toxic phenotypes associated with mutant TDP-43 and FUS including neuronal dysfunction and oxidative stress.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.461

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 head0.070
GPT teacher head0.275
Teacher spread0.205 · 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

Citations103
Published2012
Admission routes2
Has abstractyes

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