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Record W2753875201 · doi:10.4196/kjpp.2017.21.5.487

Inhibition of anterior cingulate cortex excitatory neuronal activity induces conditioned place preference in a mouse model of chronic inflammatory pain

2017· article· en· W2753875201 on OpenAlexaff
SukJae Joshua Kang, Siyong Kim, Jaehyun Lee, Chuljung Kwak, Kyungmin Lee, Min Zhuo, Bong‐Kiun Kaang

Bibliographic record

VenueKorean Journal of Physiology and Pharmacology · 2017
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsOptogeneticsExcitatory postsynaptic potentialNeuroscienceAnterior cingulate cortexInhibitory postsynaptic potentialHyperalgesiaNociceptionConditioned place preferencePremovement neuronal activityMedicineChronic painPsychologyInternal medicineDopamineCognitionReceptor

Abstract

fetched live from OpenAlex

The anterior cingulate cortex (ACC) is known for its role in perception of nociceptive signals and the associated emotional responses. Recent optogenetic studies, involving modulation of neuronal activity in the ACC, show that the ACC can modulate mechanical hyperalgesia. In the present study, we used optogenetic techniques to selectively modulate excitatory pyramidal neurons and inhibitory interneurons in the ACC in a model of chronic inflammatory pain to assess their motivational effect in the conditioned place preference (CPP) test. Selective inhibition of pyramidal neurons induced preference during the CPP test, while activation of parvalbumin (PV)-specific neurons did not. Moreover, chemogenetic inhibition of the excitatory pyramidal neurons alleviated mechanical hyperalgesia, consistent with our previous result. Our results provide evidence for the analgesic effect of inhibition of ACC excitatory pyramidal neurons and a prospective treatment for chronic pain.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.031
GPT teacher head0.302
Teacher spread0.271 · 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 teacher head, 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

Citations19
Published2017
Admission routes1
Has abstractyes

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