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Record W1870284349 · doi:10.1002/jnr.23552

Motor activity in young <i>APP<sub>swe</sub> + PS1</i>/A246E bigenic mice as a predicting variable for memory decline

2015· article· en· W1870284349 on OpenAlexaff
Mohammed Filali, Robert Lalonde

Bibliographic record

VenueJournal of Neuroscience Research · 2015
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNeuropathologyOpen fieldNeuroscienceCognitionPsychologyMedicineRecognition memoryPhysiologyDevelopmental psychologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Reports of individuality in rodent species have been a subject of debate in pharmacology and other fields. In the current study, APPswe + PS1/A246E bigenic mice with Alzheimer's-like pathogenesis and wild-type controls were subdivided at 3 months of age into low, intermediate, and high responders in open-field activity. The mice were then evaluated longitudinally at 3 and 9 months for object recognition. Irrespective of genotype, mice with a high level of motor activity had better scores in object recognition. However, a significant correlation was established between open-field activity measured at 3 months of age and recognition memory measured at 9 months of age in the bigenic group only. These results indicate that motor activity in young mice with amyloid neuropathology may serve as a predicting variable for cognitive dysfunction in more mature mice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.425
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2015
Admission routes1
Has abstractyes

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