MétaCan
Menu
Back to cohort

Dysregulation of Iron Homeostasis in the CNS Contributes to Disease Progression in a Mouse Model of Amyotrophic Lateral Sclerosis

2009· article· en· W2026095182 on OpenAlexafffund
Suh Young Jeong, Khizr I. Rathore, Katrin Schulz, Prem Ponka, Paolo Arosio, Samuel David

Bibliographic record

VenueJournal of Neuroscience · 2009
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsJewish General HospitalMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMultiple Sclerosis SocietyMultiple Sclerosis Society of CanadaMcGill University
KeywordsAmyotrophic lateral sclerosisSOD1Genetically modified mouseBiologyNeuroscienceMotor neuronTransgeneCell biologyMedicinePathologySpinal cordDiseaseGeneGenetics

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS), characterized by degeneration of spinal motor neurons, consists of sporadic and familial forms. One cause of familial ALS is missense mutations in the superoxide dismutase 1 (SOD1) gene. Iron accumulation occurs in the CNS of both forms of ALS; however, its contribution to the pathogenesis of ALS is not known. We examined the role of iron in a transgenic mouse line overexpressing the human SOD1(G37R) mutant. We show that multiple mechanisms may underlie the iron accumulation in neurons and glia in SOD1(G37R) transgenic mice. These include dysregulation of proteins involved in iron influx and sensing of intracellular iron; iron accumulation in ventral motor neurons secondary to blockage of anterograde axonal transport; and increased mitochondrial iron load in neurons and glia. We also show that treatment of SOD1(G37R) mice with an iron chelator extends life span by 5 weeks, accompanied by increased survival of spinal motor neurons and improved locomotor function. These data suggest that iron chelator therapy might be useful for the treatment of ALS.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.332
Teacher spread0.272 · 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 designObservational
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

Citations170
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeuroscienceSame topicAmyotrophic Lateral Sclerosis ResearchFrench-language works237,207