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Record W135387682 · doi:10.2340/0001555577264267

Terpene-enhanced transdermal permeation of water and ethanol in human epidermis

2021· article· en· W135387682 on OpenAlexaff
Manel Bm, Arturo Acero P., K LO.

Bibliographic record

VenueActa Dermato Venereologica · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsDepartment of National Defence
Fundersnot available
KeywordsTransdermalPenetration (warfare)PermeationEthanolChemistryStratum corneumTerpeneChromatographyMembraneNuclear chemistryOrganic chemistryBiochemistryPharmacology

Abstract

fetched live from OpenAlex

The study was performed to investigate the effect of penetration enhancers on the stratum corneum barrier. Epidermal membranes were prepared from freeze-stored (-70 degrees C) Caucasian breast skin and mounted in a flow-through diffusion cell. The validity of the freeze storage procedure was verified by measurement of [3H]-water penetration. The effect of the cyclic terpene, carveol, on the transdermal penetration of water and ethanol was studied in vitro. Control ethanol and water penetration measured with a donor solution of 50% ethanol/PBS (w/w) was 1.9+/-0.2 and 3.6+/-0.5 x 10(-3) cm/h. The addition of 3% carveol to the donor solution increased the permeation of ethanol and water after 4 h to 8.3+/-1.1 and 12.5+/-1.9 x 10(-3) cm/h, respectively. In a separate experiment, terpinen-4-ol and alpha-terpineol were also tested, in addition to carveol, for effect on tritium flux. No significant difference in maximum tritium flux was obtained between the three terpenes studied. The maximum increase in permeability coefficients of carveol, terpinen-4-ol and alpha-terpineol was 10.6, 8.7 and 10.9, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.077
GPT teacher head0.403
Teacher spread0.326 · 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 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

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

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