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Record W2044000270 · doi:10.1097/wnr.0b013e3283112703

Regional differential effects of chronic nicotine on brain α4-containing and α6-containing receptors

2008· article· en· W2044000270 on OpenAlexfundno aff
Naïla Even, Ana Cardona, Martine Soudant, Pierre-Jean Corringer, Jean‐Pierre Changeux, Isabelle Cloëz-Tayarani

Bibliographic record

VenueNeuroreport · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsnot available
FundersFédération pour la Recherche sur le CerveauCentre National de la Recherche ScientifiqueInstitut national de la recherche scientifique
KeywordsDownregulation and upregulationNucleus accumbensNicotineAcetylcholine receptorVentral tegmental areaNicotinic agonistNeuroscienceHippocampusReceptorChemistryEndocrinologyInternal medicineBiologyDopamineMedicineDopaminergicBiochemistry

Abstract

fetched live from OpenAlex

Chronic nicotine upregulates central nicotinic acetylcholine receptors (nAChRs), a plasticity process thought to contribute to its addictive properties. To analyze this process in vivo, we chronically exposed mice to nicotine using minipump delivering nicotine at concentrations close to those found in tobacco smokers. Binding studies show upregulation of high-affinity nAChRs after 21 days of treatment in cortical areas, caudate putamen, nucleus accumbens, hippocampus, ventral tegmental area, and superior colliculi. No upregulation was observed in thalamus and discrete cortical areas. Using wild type and alpha 6-/- mice, we observed a downregulation of alpha 6*-nAChRs in superior colliculi and no effects in other structures. The complex pattern of upregulation/downregulation observed in this study depends on both nAChR composition and regional distribution.

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 score0.004

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)0.0010.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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