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Record W4415281826 · doi:10.1242/dev.205116

Regulation of tissue regeneration and repair by the peripheral nervous system

2025· review· en· W4415281826 on OpenAlexafffund
Griffen Wakelin, Adam P. W. Johnston

Bibliographic record

VenueDevelopment · 2025
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsRegeneration (biology)Regenerative medicineNervous tissueHomeostasisPeripheral nervous systemNervous systemMesenchymal stem cellCentral nervous systemTissue repair

Abstract

fetched live from OpenAlex

Amphibians display a remarkable capacity for tissue regeneration, with some able to regrow entire limbs. In mammals, while tissues such as skin, bones and skeletal muscles are capable of repair following injury, true multilineage regeneration is rare and restricted to the distal region of the digit tip. Although the mechanisms governing successful tissue repair and regeneration are still coming to light, it is now appreciated that innervation by local nerves is a necessary component of the regenerative microenvironment. In this Review, we examine the current state of the literature that identifies the role of axon-derived signals, Schwann cells and nerve-derived mesenchymal cells as direct and indirect supporters of tissue repair and regeneration. We will also discuss how these cells function under pathological conditions or situations of aberrant tissue repair. Altogether, these findings underscore the significance of elucidating the role of the peripheral nervous system in tissue homeostasis and repair, with potential implications for the development of targeted therapeutic interventions and regenerative medicine strategies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.562

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 head0.033
GPT teacher head0.298
Teacher spread0.265 · 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 designOther design
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes2
Has abstractyes

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