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Record W3207642527 · doi:10.1038/s41536-021-00175-7

Pain sensing neurons promote tissue regeneration in adult mice

2021· article· en· W3207642527 on OpenAlexaff
Lise Rabiller, Elodie Labit, Christophe Guissard, Silveric Gilardi, Bruno P. Guiard, Lionel Moulédous, Marine Silva, Gilles Mithieux, Luc Pénicaud, Anne Lorsignol, Louis Casteilla, Cécile Dromard

Bibliographic record

Venuenpj Regenerative Medicine · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsUniversity of CalgaryMcGill University
FundersMinistère de l'Enseignement Supérieur et de la RechercheFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche Médicale
KeywordsRegeneration (biology)NociceptionBiologyAdipose tissueCalcitonin gene-related peptideZebrafishNeuropeptideCell biologyNeuroscienceRegenerative medicineAnatomyReceptorStem cellEndocrinology

Abstract

fetched live from OpenAlex

Tissue repair after injury in adult mammals, usually results in scarring and loss of function in contrast to lower vertebrates such as the newt and zebrafish that regenerate. Understanding the regulatory processes that guide the outcome of tissue repair is therefore a concerning challenge for regenerative medicine. In multiple regenerative animal species, the nerve dependence of regeneration is well established, but the nature of the innervation required for tissue regeneration remains largely undefined. Using our model of induced adipose tissue regeneration in adult mice, we demonstrate here that nociceptive nerves promote regeneration and their removal impairs tissue regeneration. We also show that blocking the receptor for the nociceptive neuropeptide calcitonin gene-related peptide (CGRP) inhibits regeneration, whereas CGRP administration induces regeneration. These findings reveal that peptidergic nociceptive neurons are required for adult mice tissue regeneration.

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)0.003
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 score0.015
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

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