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Record W2735082025 · doi:10.7554/elife.26991

LTP and memory impairment caused by extracellular Aβ and Tau oligomers is APP-dependent

2017· article· en· W2735082025 on OpenAlexafffund
Daniela Puzzo, Roberto Piacentini, Mauro Fà, Walter Gulisano, Domenica Donatella Li Puma, Agnes Staniszewski, Hong Zhang, Maria Rosaria Tropea, Sara Cocco, Agostino Palmeri, Paul E. Fraser, Luciano D'adamio, Claudio Grassi, Ottavio Arancio

Bibliographic record

VenueeLife · 2017
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsLong-term potentiationExtracellularNeuroscienceAmyloid precursor proteinAmyloid (mycology)Amyloid betaChemistryAmyloid precursor protein secretaseAlzheimer's diseaseMemory impairmentCell biologyBiologyBiochemistryMedicineDiseaseInternal medicineCognition

Abstract

fetched live from OpenAlex

The concurrent application of subtoxic doses of soluble oligomeric forms of human amyloid-beta (oAβ) and Tau (oTau) proteins impairs memory and its electrophysiological surrogate long-term potentiation (LTP), effects that may be mediated by intra-neuronal oligomers uptake. Intrigued by these findings, we investigated whether oAβ and oTau share a common mechanism when they impair memory and LTP in mice. We found that as already shown for oAβ, also oTau can bind to amyloid precursor protein (APP). Moreover, efficient intra-neuronal uptake of oAβ and oTau requires expression of APP. Finally, the toxic effect of both extracellular oAβ and oTau on memory and LTP is dependent upon APP since APP-KO mice were resistant to oAβ- and oTau-induced defects in spatial/associative memory and LTP. Thus, APP might serve as a common therapeutic target against Alzheimer's Disease (AD) and a host of other neurodegenerative diseases characterized by abnormal levels of Aβ and/or Tau.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.541

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 head0.022
GPT teacher head0.309
Teacher spread0.287 · 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

Citations182
Published2017
Admission routes2
Has abstractyes

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