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Record W2497885527 · doi:10.1186/s40824-016-0073-3

Effect of molecular weight of hyaluronic acid (HA) on viscoelasticity and particle texturing feel of HA dermal biphasic fillers

2016· article· en· W2497885527 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomaterials Research · 2016
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMaterials scienceHyaluronic acidComposite materialViscoelasticityDermisParticle (ecology)Elastic modulusMicrosphereParticle sizeChemical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: Hyaluronic acid (HA) dermal biphasic fillers are synthesized for their efficacy in correcting aesthetic defects such as wrinkles, scars and facial contouring defects. The fillers consist of crosslinked HA microspheres suspended in a noncrosslinked HA. To extend the duration of HAs within the dermis and obtain the particle texturing feel, HAs are crosslinked to obtain the suitable mechanical properties. RESULTS: Hyaluronic acid (HA) dermal biphasic fillers are prepared by mixing the crosslinked HA microspheres and the noncrosslinked HAs. The elastic modulus of the fillers increased with raising the volume fraction of the microspheres. The mechanical properties and the particle texturing feel of the fillers made from crosslinked HA (1058 kDa) microspheres suspended in noncrosslinked HA (1368 kDa) are successfully achieved, which are adequate for the fillers. CONCLUSIONS: Dermal biphasic HA fillers made from 1058 kDa exhibit suitable elastic moduli (211 to 420 Pa) and particle texturing feel (scale 7 ~ 9).

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.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.032
GPT teacher head0.376
Teacher spread0.345 · 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