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Record W2096727043 · doi:10.1186/1744-8069-8-90

Genetic Enhancement of Neuropathic and Inflammatory Pain by Forebrain Upregulation of CREB-Mediated Transcription

2012· article· en· W2096727043 on OpenAlexaff
Giannina Descalzi, Hotaka Fukushima, Akinobu Suzuki, Satoshi Kida, Min Zhuo

Bibliographic record

VenueMolecular Pain · 2012
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsCREBNeuropathic painNeuroscienceAllodyniaHyperalgesiaChronic painMedicineAnterior cingulate cortexRostral ventromedial medullaForebrainNerve injuryNociceptionPsychologyTranscription factorCentral nervous systemInternal medicineReceptorBiology

Abstract

fetched live from OpenAlex

CREB has been reported to be activated by injury and is commonly used as marker for pain-related plasticity changes in somatosensory pathways, including spinal dorsal horn neurons and the anterior cingulate cortex (ACC). However no evidence has been reported to support the direct role of activated CREB in injury-related behavioral sensitization (or allodynia). Here we report that genetic enhancement of CREB-mediated transcription selectively in forebrain areas enhanced behavioral responses to non-noxious stimuli after chronic inflammation (CFA model) or nerve injury. In contrast, behavioral acute responses to peripheral subcutaneous injection of formalin did not show any significant difference. Furthermore, acute pain responses to noxious thermal stimuli were also not affected. Our results thus provide direct evidence that cortical CREB-mediated transcription contributes to behavioral allodynia in animal models of chronic inflammatory or neuropathic 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 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0030.000

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations30
Published2012
Admission routes1
Has abstractyes

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