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Record W2986217975 · doi:10.7554/elife.12056.010

Author response: Translational control of nicotine-evoked synaptic potentiation in mice and neuronal responses in human smokers by eIF2α

2016· peer-review· en· W2986217975 on OpenAlexaff
Andon N. Placzek, David L. Molfese, Sanjeev Khatiwada, Gonzalo Viana Di Prisco, Wei‐Chien Huang, Carmela Sidrauski, K. Krnjević, Christopher L Amos, Russell Ray, John A. Dani, Peter Walter, Ramiro Salas, Mauro Costa‐Mattioli

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsMcGill University
Fundersnot available
KeywordsNicotineAddictionVentral tegmental areaNeurosciencePsychologyLong-term potentiationExcitatory postsynaptic potentialNicotine withdrawalDopaminergicPharmacologyMedicineDopamineInternal medicineInhibitory postsynaptic potential

Abstract

fetched live from OpenAlex

Nicotine addiction is a serious public health problem. People who start using nicotine during adolescence are more likely to become addicted to it during adulthood, but the reasons for this are not well understood. Nicotine causes long-lasting changes in the brain that are responsible for the feelings of pleasure and reward. In particular, nicotine strengthens the connections between neurons at structures called synapses and increases communication between reward-related neurons in key reward areas of the brain. This hijacking of the natural reward system requires new proteins to be made. However, the relationship between protein synthesis and adolescents being particularly vulnerable to nicotine addiction was not known. In a related study, Huang et al. found that the reduced activity of a protein called eIF2α, which controls the production of new proteins, accounts for why adolescents are more likely to become addicted to cocaine than adults. Thus, Placzek et al. wanted to know whether the same was true for nicotine and whether the proteins controlled by eIF2α are involved in the way human nicotine addicts experience reward. Placzek et al. found that adolescent mice are more susceptible than adult mice to the changes in synaptic strength that are caused by nicotine. This increased susceptibility results from reduced activity levels of the protein eIF2α. Reducing the activity of eIF2α in adult mice made their synapses as likely to change strength in response to nicotine as the synapses of adolescent mice. Placzek et al. also used a technique called functional magnetic resonance imaging and found that compared to non-smokers, the brain activity of human smokers was significantly reduced when given a natural reward. Further studies revealed a variation in the gene encoding the eIF2α protein that affects how smokers respond to a reward, suggesting that this variant is linked to the likelihood that a person will be addicted to nicotine. This work raises several important questions. In addition to regulating the initial adaptive changes induced in the brain by nicotine, does eIF2α activity affect compulsive nicotine use? If so, could targeting parts of the eIF2α pathway help treat nicotine addiction? Finally, further studies could explore whether the gene variant identified by Placzek et al. affects how users of other drugs (such as cocaine or alcohol) respond to natural rewards.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.153
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1530.049

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 head0.016
GPT teacher head0.312
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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