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The anatomy and histology of wound healing following tail loss in the leopard gecko <i>Eublepharis macularius</i>

2010· article· en· W2296608904 on OpenAlexaff
Matthew K. Vickaryous, Stephanie Delorme, Katherine E McLean, Christopher L Zweerman

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHistologyAnatomyAutotomyWound healingConnective tissueGranulation tissueMedicineBiologyPathologySurgery

Abstract

fetched live from OpenAlex

Many lizards are able to voluntarily self‐detach (autotomize) a portion of the tail and then regenerate a functional replacement. Autotomy occurs at an intravertebral fracture plane and ruptures all the major tissue types of the tail, resulting in an open wound with various tissues exposed. The objectives of this study were to investigate the anatomy and histology of tail loss and wound healing in the leopard gecko, Eublepharis macularius . Microcomputed tomography and serial histology reveals the structure of the fracture plane and associated tissues, including a persistent notochord and sphincter muscles of the caudal artery. Following tail loss the remaining skin collapses over the wound site, the spinal cord is retracted into the original tail stump, and a clot is formed. Epithelial cells adjacent to the wound site begin to proliferate and migrate deep to the clot. Once the wound epithelium completely spans the wound site the clot drops off to reveal the developing blastema. Unlike most invasive wounds, fibrous scar tissue is not formed. Ongoing studies of wound healing studies in Eublepharis provide an important complement for ongoing regenerative research by expanding the comparative framework.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.283
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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