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Topical application of a film‐forming emulgel dressing that controls the release of stratifin and acetylsalicylic acid and improves/prevents hypertrophic scarring

2012· article· en· W1538632824 on OpenAlexafffund
Elham Rahmani‐Neishaboor, Reza Jallili, Ryan Hartwell, Victor C. M. Leung, Nicholas Carr, Aziz Ghahary

Bibliographic record

VenueWound Repair and Regeneration · 2012
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineHypertrophic scarHyaluronic acidWound healingCarboxymethyl celluloseSurgeryChemistrySodiumAnatomy

Abstract

fetched live from OpenAlex

Here, we evaluate the efficacy of an emulgel dressing to control the release of an antifibrogenic factor, stratifin (SFN), along with an anti-inflammatory drug, acetylsalicylic acid (ASA), to be used as a wound dressing with hypertrophic scar reducing features. Emulgel dressings were prepared by dispersing positively charged submicron vesicles in carboxymethyl cellulose gel. Release kinetics of SFN/ASA and toxicity for primary skin cells were assessed in vitro. Antifibrogenic efficacy of medicated emulgel dressings was tested on a rabbit ear fibrotic model. Following topical application on the wounds, emulgels formed an occlusive film and controlled the release of SFN and ASA for 7 and 24 hours, respectively. Wounds treated with SFN/ASA-containing emulgel dressings showed an 80% reduction in scar elevation compared with untreated controls. Topical formulations were nontoxic for cultured human keratinocytes and fibroblasts. Inflammation was significantly controlled in treated wounds, as shown by a reduced number of infiltrated CD3(+) T cells (p < 0.001) and macrophages. SFN/ASA-treated wounds showed a significantly higher (p < 0.001) expression of matrix metalloproteinase-1, resulting in reduced collagen deposition and less scarring. Film-forming emulgel dressings that control the release of antifibrogenic and anti-inflammatory factors provide an excellent treatment option for postburn hypertrophic scar management.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.240

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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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