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Opioid-Induced Neuroplasticity: Insights from Animal Models

2024· article· en· W4402952082 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications in Humanities Research · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuroplasticityOpioidNeuroscienceAnimal modelPsychologyMedicineInternal medicineReceptor

Abstract

fetched live from OpenAlex

Synaptic plasticity is defined as the modification of the transmission of synapses. It has been proven to be strongly associated with learning. Thus, drug-evoked synaptic plasticity in brain reward circuits can establish persistent learning of addictive drugs, reflecting the neural basis underlying addiction. The mesolimbic dopamine pathway has been widely indicated to be strongly associated with opioid use disorder (OUD) and other drug addictions. The paper focuses on discussing the drug-evoked neural plasticity underlying two important stages called intoxication and withdrawal which are critical for addiction and reinstatement of drug use respectively. The paper first explores the neural basis of OUD, emphasizing drug-induced plasticity at glutamate and Gamma-aminobutyric acid (GABA) synapses on neurons of key substrates in the pathway and how they influence mesolimbic dopamine (DA) neuron transmission. Then, the review discussed withdrawal-induced neuroplasticity and reorganization of associated neuron circuits, which explain deficits led by withdrawal from opioid administration. An overall understanding of drug-evoked synaptic plasticity in key brain circuits in the development of addiction helps find possible therapeutic methods to prevent the initiation of OUD and reinstatement.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.523
GPT teacher head0.444
Teacher spread0.080 · 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