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Record W2788347959 · doi:10.1007/s12079-018-0453-7

Galectin-3 regulation of wound healing and fibrotic processes: insights for chronic skin wound therapeutics

2018· review· en· W2788347959 on OpenAlexafffund
Karrington McLeod, John T. Walker, Douglas W. Hamilton

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsWound healingMyofibroblastMatricellular proteinDermisFibrosisGalectin-3Skin repairInflammationGalectinAngiogenesisS100A9MedicineCell biologyMacrophage polarizationCancer researchFibroblastBiologyImmunologyPathologyMacrophageExtracellular matrixCell cultureGenetics

Abstract

fetched live from OpenAlex

A member of the lectin family, galectin-3 is a 250 amino-acid protein that contains a C-terminus carbohydrate recognition domain (CRD) that recognizes β-galactosides. Considered to have certain common properties associated with matricellular proteins, galectin-3 is expressed in the dermis and epidermis in healthy skin and is upregulated in skin healing, peaking at day 1 post wounding in mice. Galectin-3 has been implicated in several processes central to the wound healing response, specifically in the regulation of inflammation, macrophage polarization, angiogenesis, fibroblast to myofibroblast transition and re-epithelialization. However, it appears that many of the effects of Galectin-3 are highly tissue specific and context dependent. Genetic deletion of galectin-3 shows different effects in skin compared to lung, heart, and kidney remodeling. In this review, we will compare galectin-3 functions in these tissues. Furthermore, we will discuss, based on its identified regulation of cell processes, whether in an exogenous form, galectin-3 could represent a novel therapeutic for impaired skin healing.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.321
Teacher spread0.271 · 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

Citations56
Published2018
Admission routes2
Has abstractyes

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