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Improvement of hypertrophic scarring by using topical anti‐fibrogenic/anti‐inflammatory factors in a rabbit ear model

2010· article· en· W1849339882 on OpenAlexafffund
Elham Rahmani‐Neishaboor, Farrah Meng‐kay Yau, Reza B. Jalili, Ruhangiz T. Kilani, Aziz Ghahary

Bibliographic record

VenueWound Repair and Regeneration · 2010
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineRabbit (cipher)PathologyDermatologyComputer science

Abstract

fetched live from OpenAlex

This study investigates the scar-reducing efficacy of topical application of stratifin and acetylsalicylic acid (ASA) in a rabbit ear model. A total of five New Zealand white rabbits with four wounds per ear were examined. Either recombinant stratifin (0.002%) or ASA (0.5%) incorporated in carboxymethyl cellulose gel was topically applied on each wound at postwounding Day 5. Scars were harvested at postwounding Day 28 for histological analysis. The wounds treated with stratifin and ASA showed 82 and 73% reduction in scar volume, respectively, compared with that of untreated controls. A reduction of 57 and 41% in total tissue cellularity along with 79 and 91% reduction in infiltrated CD3+ T cells were observed in response to treatment with stratifin and ASA, respectively, compared with those of untreated controls. Wounds treated with stratifin showed a 2.8-fold increase in matrix metalloproteinase-1 expression, which resulted in a 48% decrease in collagen density compared with those of untreated controls. Qualitative wound assessment showed a reduced hypertrophic scarring in stratifin and ASA-treated wounds when compared with the controls. This study showed that topical application of either stratifin or ASA-impregnated carboxymethyl cellulose gel reduced hypertrophic scar formation following dermal injuries in a rabbit ear fibrotic model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.380

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 head0.017
GPT teacher head0.265
Teacher spread0.247 · 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

Citations48
Published2010
Admission routes2
Has abstractyes

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