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ChAT-ChR2-EYFP Mice Have Enhanced Motor Endurance But Show Deficits in Attention and Several Additional Cognitive Domains

2013· article· en· W2079810269 on OpenAlexafffund
Benjamin Kolisnyk, Marianny del Carmen Guzmán, Sanda Raulic, Jue Fan, Ana C. Magalhães, Guoping Feng, Robert Gros, Vânia F. Prado, Marco A. M. Prado

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsCognitionNeurosciencePsychologyCognitive psychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Acetylcholine (ACh) is an important neuromodulator in the nervous system implicated in many forms of cognitive and motor processing. Recent studies have used bacterial artificial chromosome (BAC) transgenic mice expressing channelrhodopsin-2 (ChR2) protein under the control of the choline acetyltransferase (ChAT) promoter (ChAT-ChR2-EYFP) to dissect cholinergic circuit connectivity and function using optogenetic approaches. We report that a mouse line used for this purpose also carries several copies of the vesicular acetylcholine transporter gene (VAChT), which leads to overexpression of functional VAChT and consequently increased cholinergic tone. We demonstrate that these mice have marked improvement in motor endurance. However, they also present severe cognitive deficits, including attention deficits and dysfunction in working memory and spatial memory. These results suggest that increased VAChT expression may disrupt critical steps in information processing. Our studies demonstrate that ChAT-ChR2-EYFP mice show altered cholinergic tone that fundamentally differentiates them from wild-type 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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

Citations129
Published2013
Admission routes2
Has abstractyes

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