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Record W2965278946 · doi:10.1016/j.celrep.2019.07.048

Recruitment of Spinoparabrachial Neurons by Dorsal Horn Calretinin Neurons

2019· article· en· W2965278946 on OpenAlexafffund
Hugues Petitjean, Farin B. Bourojeni, D. Tsao, Albena Davidova, Susana G. Sotocinal, Jeffrey S. Mogil, Artur Kania, Reza Sharif‐Naeini

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill Genome CentreMontreal Clinical Research InstituteMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaAllen FoundationCanadian Institutes of Health ResearchRita Allen Foundation
KeywordsNeuroscienceCalretininNociceptionOptogeneticsSpinal cordExcitatory postsynaptic potentialSomatosensory systemInhibitory postsynaptic potentialFrench hornDorsumStimulationBiologyAnatomyPsychologyReceptor

Abstract

fetched live from OpenAlex

The dorsal horn of the spinal cord is the first integration site of somatosensory inputs from the periphery. In the superficial layers of the dorsal horn, nociceptive inputs are processed by a complex network of excitatory and inhibitory interneurons whose function and connectivity remain poorly understood. We examined the role of calretinin-expressing interneurons (CR neurons) in such processing and show that they receive direct inputs from nociceptive fibers and polysynaptic inputs from touch-sensitive Aβ fibers. Their activation by chemogenetic or optogenetic stimulation produces mechanical allodynia and nocifensive responses. Furthermore, they monosynaptically engage spinoparabrachial (SPb) neurons in lamina I, suggesting CR neurons modulate one of the major ascending pain pathways of the dorsal horn. In conclusion, we propose a neuronal pathway in which CR neurons are positioned at the junction between nociceptive and innocuous circuits and directly control SPb neurons in lamina I.

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 score0.181
Threshold uncertainty score0.615

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 head0.024
GPT teacher head0.274
Teacher spread0.251 · 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

Citations49
Published2019
Admission routes2
Has abstractyes

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