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Record W2589979933 · doi:10.3934/matersci.2017.2.370

Perspectives on nanofiber dressings for the localized delivery of botanical remedies in wound healing

2017· article· en· W2589979933 on OpenAlexaff
Sukhwinder K. Bhullar, Harpal S. Buttar

Bibliographic record

VenueAIMS Materials Science · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of OttawaUniversity of Victoria
Fundersnot available
KeywordsNanofiberAntisepticBiocompatible materialWound healingMaterials scienceMedicineBiomedical engineeringNanotechnologySurgery

Abstract

fetched live from OpenAlex

Based on their antiseptic and anti-inflammatory properties, plant-derived remedies and herbal products have been used since ancient times for wound and burn cure as well as for treating chronic skin diseases like dermatitis and eczema. Biocompatible and biodegradable polymer nanofiber devices are currently fabricated using sophisticated engineering techniques. Such nanofiber structures have proven efficacious for the localized delivery of therapeutic agents for the treatment of wounds due to their unique physical-chemical properties such as large surface-area-to-volume ratio, high porosity, improved cell adherence, cellular proliferation and migration, as well as controlled in vivo biodegradation rates. The remit of this communication is to highlight the methodology used for the fabrication of nanofiber mats and dressings for the localised delivery of herbal products and plant-derived ingredients for wound 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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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