MétaCan
Menu
← Back to cohort
Record W2620802821

The Behavioural Characterization of Dopamine D2 Receptor-related Protein-protein Interactions

2013· dissertation· en· W2620802821 on OpenAlexfundno aff
Kai Ying Lai

Bibliographic record

VenueTSpace (University of Toronto) · 2013
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsDopamine receptor D2DopamineDopamine receptorCharacterization (materials science)NeuroscienceChemistryPsychologyBiologyMaterials scienceNanotechnology
DOInot available

Abstract

fetched live from OpenAlex

Dopamine is one of the prominent catecholamine neurotransmitters in mammalian central nervous system. Dopamine is critically involved in a wide range of physiological functions including movement, motivation, reward, learning and memory, etc. The dopaminergic system exerts or modulates these physiological actions through four major signaling pathways. Clinically, the dysfunctions of the dopaminergic system are implicated in the patho-physiology of disorders such as Parkinson’s disease (PD), schizophrenia, attention deficit/hyperactivity disorder (ADHD), etc. At the molecular level, dopamine exerts its actions via its binding to dopaminergic receptors such as dopamine D2 receptors (D2R). In this study, two D2R-related protein-protein interactions were examined in vivo regarding their behavioural effects. The disruption of D2R-DAT interaction was found to elevate voluntary movement in normal animals as well as in animals modeling acute dopamine depletion. On the other hand, the interference in the D2R-DISC1 protein complex exhibited anti-psychotic actions in two animal models of schizophrenia.

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)→Same topicReceptor Mechanisms and Signaling→French-language works237,207→