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Improvement of cholinergic function during normal and pathological aging

2013· article· en· W3167238749 on OpenAlexaff
Paul Michael Nagy, Isabelle Aubert

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsSunnybrook HospitalUniversity of TorontoOntario Brain InstituteSunnybrook Health Science Centre
Fundersnot available
KeywordsVesicular acetylcholine transporterCholinergicAcetylcholineCholinergic neuronCholine acetyltransferaseNeuroscienceNeurochemicalAcetylcholinesteraseNeurotransmitterWestern blotBiologyInternal medicineEndocrinologyCell biologyMedicineCentral nervous systemBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Cholinergic neurons produce the neurotransmitter acetylcholine and are essential to critical brain functions, including motor function, learning and memory. The vesicular acetylcholine transporter (VAChT) is expressed by cholinergic neurons and packages acetylcholine into vesicles to prepare its release to the synaptic cleft. Here, we report that increasing levels of VAChT is associated with neurochemical alterations that may improve motor behaviour and cognitive decline during normal and pathological aging. Using RT‐PCR, immunohistochemistry, and western blot, we show that B6.Cg‐Tg(RP23–268L19‐EGFP)2Mik/J mice exhibit enhanced VAChT mRNA and protein expression. These changes were sufficient to enhance acetylcholine release at cholinergic terminals, and elicit changes in behavioural functions including spontaneous and novelty‐induced activity and measures of cognition. Mechanisms for these behavioural features will be explored. Taken together, this study contributes to our fundamental understanding of cholinergic regulation, specifically in response to increased VAChT expression, and may implicate VAChT as a target to maintain adequate cholinergic function during aging.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.037
GPT teacher head0.294
Teacher spread0.258 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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