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Record W2095209894 · doi:10.1002/dvdy.23898

Slit/Robo‐mediated chemorepulsion of vagal sensory axons in the fetal gut

2012· article· en· W2095209894 on OpenAlexafffund
David P. Goldberg, Rajka Borojevic, Monique Anderson, Jason J. Chen, Michael D. Gershon, Elyanne M. Ratcliffe

Bibliographic record

VenueDevelopmental Dynamics · 2012
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsMcMaster UniversityPopulation Health Research InstituteWestern University
FundersPeking UniversityNational Institute of Neurological Disorders and StrokeJ.P. Bickell FoundationMcMaster University
KeywordsNetrinBiologyAxon guidanceNodose GanglionVagus nerveSensory systemSlitAxonNeuroscienceAnatomyCell biology

Abstract

fetched live from OpenAlex

BACKGROUND: The vagus nerve descends from the brain to the gut during fetal life to reach specific targets in the bowel wall. Vagal sensory axons have been shown to respond to the axon guidance molecule netrin and to its receptor, deleted in colorectal cancer (DCC). As there are regions of the gut wall into which vagal axons do and do not extend, it is likely that a combination of attractive and repellent cues are involved in how vagal axons reach specific targets. We tested the hypothesis that Slit/Robo chemorepulsion can contribute to the restriction of vagal sensory axons to specific targets in the gut wall. RESULTS: Transcripts encoding Robo1 and Robo2 were expressed in the nodose ganglia throughout development and mRNA encoding the Robo ligands Slit1, Slit2, and Slit3 were all found in the fetal and adult bowel. Slit2 protein was located in the outer gut mesenchyme in regions that partially overlap with the secretion of netrin-1. Neurites extending from explanted nodose ganglia were repelled by Slit2. CONCLUSIONS: These observations suggest that vagal sensory axons are responsive to Slit proteins and are thus repelled by Slits secreted in the gut wall and prevented from reaching inappropriate targets.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.488

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 head0.042
GPT teacher head0.265
Teacher spread0.223 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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