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Neurotoxicity and Memory Deficits Induced by Soluble Low-Molecular-Weight Amyloid- 1-42 Oligomers Are Revealed In Vivo by Using a Novel Animal Model

2012· article· en· W2090349395 on OpenAlexfundno aff
Jonathan Brouillette, Raphaëlle Caillierez, Nadège Zommer, Claire Alves‐Pires, Iryna Benilova, David Blum, Bart De Strooper, Luc Buée

Bibliographic record

VenueJournal of Neuroscience · 2012
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchVlaamse regeringUniversity of TwenteAgence Nationale de la Recherche
KeywordsNeurotoxicityIn vivoNeurodegenerationHippocampal formationNeuroscienceHyperphosphorylationHippocampusNMDA receptorAmyloid (mycology)ChemistryBiologyBiochemistryReceptorMedicinePathologyToxicityPhosphorylationDisease

Abstract

fetched live from OpenAlex

Neuronal and synaptic degeneration are the best pathological correlates for memory decline in Alzheimer's disease (AD). Although the accumulation of soluble low-molecular-weight amyloid-β (Aβ) oligomers has been suggested to trigger neurodegeneration in AD, animal models overexpressing or infused with Aβ lack neuronal loss at the onset of memory deficits. Using a novel in vivo approach, we found that repeated hippocampal injections of small soluble Aβ(1-42) oligomers in awake, freely moving mice were able to induce marked neuronal loss, tau hyperphosphorylation, and deficits in hippocampus-dependent memory. The neurotoxicity of small Aβ(1-42) species was observed in vivo as well as in vitro in association with increased caspase-3 activity and reduced levels of the NMDA receptor subunit NR2B. We found that the sequestering agent transthyretin is able to bind the toxic Aβ(1-42) species and attenuated the loss of neurons and memory deficits. Our novel mouse model provides evidence that small, soluble Aβ(1-42) oligomers are able to induce extensive neuronal loss in vivo and initiate a cascade of events that mimic the key neuropathological hallmarks of 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 score0.230
Threshold uncertainty score0.677

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 communication0.0000.001
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 head0.054
GPT teacher head0.323
Teacher spread0.269 · 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

Citations171
Published2012
Admission routes1
Has abstractyes

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