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Iron Biochemistry is Correlated with Amyloid Plaque Morphology in an Established Mouse Model of Alzheimer's Disease

2017· article· en· W2751555622 on OpenAlexafffund
Neil D. Telling, James Everett, Joanna F. Collingwood, Jon Dobson, G. van der Laan, Joseph J. Gallagher, Jian Wang, Adam P. Hitchcock

Bibliographic record

VenueCell chemical biology · 2017
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcMaster UniversityCanadian Light Source (Canada)
FundersGovernment of SaskatchewanWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaEngineering and Physical Sciences Research CouncilCanadian Institutes of Health ResearchScience Foundation IrelandNational Research Council CanadaCanada Foundation for InnovationDiamond Light SourceHealth Research BoardUniversity of Saskatchewan
KeywordsAmyloid (mycology)PathologyChemistryNeurotoxicityFibrilIn vivoMorphology (biology)BiophysicsBiochemistryBiologyMedicineToxicity

Abstract

fetched live from OpenAlex

peptide can in part be explained by metal ion binding interactions. Using advanced X-ray microscopy techniques at sub-micron resolution, we investigated relationships between iron biochemistry and AD pathology in intact cortex from an established mouse model over-producing Aβ. We found a direct correlation of amyloid plaque morphology with iron, and evidence for the formation of an iron-amyloid complex. We also show that iron biomineral deposits in the cortical tissue contain the mineral magnetite, and provide evidence that Aβ-induced chemical reduction of iron could occur in vivo. Our observations point to the specific role of iron in amyloid deposition and AD pathology, and may impact development of iron-modifying therapeutics for AD.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.771

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 studies0.0000.001
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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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

Citations164
Published2017
Admission routes2
Has abstractyes

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