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Netrin-1 receptor DCC is required for the contralateral topography of lamina I anterolateral system neurons

2020· article· en· W3045138702 on OpenAlexafffund
Farin B. Bourojeni, Hanns Ulrich Zeilhofer, Artur Kania

Bibliographic record

VenuePain · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsMcGill UniversityMontreal Clinical Research Institute
FundersCanadian Institutes of Health ResearchFondation Brain Canada
KeywordsNetrinNeuroscienceDeleted in Colorectal CancerPeriaqueductal graySpinal cordBiologyNociceptionAnatomyNeuronAxon guidanceMedicineReceptorCentral nervous systemAxonColorectal cancer

Abstract

fetched live from OpenAlex

Anterolateral system (AS) neurons relay nociceptive information from the spinal cord to the brain, protecting the body from harm by evoking a variety of behaviours and autonomic responses. The developmental programs that guide the connectivity of AS neurons remain poorly understood. Spinofugal axons cross the spinal midline in response to Netrin-1 signalling through its receptor deleted in colorectal carcinoma (DCC); however, the relevance of this canonical pathway to AS neuron development has only been demonstrated recently. Here, we disrupted Netrin-1:DCC signalling developmentally in AS neurons and assessed the consequences on the path finding of the different classes of spinofugal neurons. Many lamina I AS neurons normally innervate the lateral parabrachial nucleus and periaqueductal gray on the contralateral side. The loss of DCC in the developing spinal cord resulted in increased frequency of ipsilateral projection of spinoparabrachial and spinoperiaqueductal gray neurons. Given that contralateral spinofugal projections are largely associated with somatotopic representation of the body, changes in the laterality of AS spinofugal projections may contribute to reduced precision in pain localization observed in mice and humans carrying Dcc mutations.

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)0.001
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 score0.100
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.255
Teacher spread0.205 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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