MétaCan
Menu
Back to cohort
Record W4281257503 · doi:10.1111/wrr.13029

Local problems need global solutions: The metabolic needs of regenerating organisms

2022· review· en· W4281257503 on OpenAlexfundno aff
Ines C. Kübler, Jenny Kretzschmar, Marko Brankatschk, Tatiana Sandoval‐Guzmán

Bibliographic record

VenueWound Repair and Regeneration · 2022
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaDeutsche ForschungsgemeinschaftUK Research and Innovation
KeywordsRegeneration (biology)Signalling pathwaysWound healingBiologyTissue repairBioinformaticsIntensive care medicineCell biologyMedicineSignal transductionImmunology

Abstract

fetched live from OpenAlex

The vast majority of species that belong to the plant or animal kingdom evolved with two main strategies to counter tissue damage-scar formation and regeneration. Whereas scar formation provides a fast and cost-effective repair to exit life-threatening conditions, complete tissue regeneration is time-consuming and requires vast resources to reinstall functionality of affected organs or structures. Local environments in wound healing are widely studied and findings have provided important biomedical applications. Less well understood are organismic physiological parameters and signalling circuits essential to maintain effective tissue repair. Here, we review accumulated evidence that positions the interplay of local and systemic changes in metabolism as essential variables modulating the injury response. We particularly emphasise the role of lipids and lipid-like molecules as significant components long overlooked.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.054
GPT teacher head0.317
Teacher spread0.263 · 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 designNot applicable
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueWound Repair and RegenerationSame topicDietary Effects on HealthFrench-language works237,207