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Record W3008444530 · doi:10.1016/j.mtbio.2020.100046

Role of active nanoliposomes in the surface and bulk mechanical properties of hybrid hydrogels

2020· article· en· W3008444530 on OpenAlexfundno aff
Rana Kadri, J. Bacharouch, Kamil Elkhoury, Ghazi Ben Messaoud, Cyril J.F. Kahn, Stéphane Desobry, Michel Linder, Ali Tamayol, Grégory Francius, João F. Mano, Laura Sánchez‐González, Elmira Arab‐Tehrany

Bibliographic record

VenueMaterials Today Bio · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheAssociation Nationale de la Recherche et de la TechnologieEuropean CommissionMinistério da Educação e CiênciaCentro de Investigação em Materiais Cerâmicos e CompósitosMountain Equipment Co-operative
KeywordsSelf-healing hydrogelsGelatinMaterials scienceDrug deliveryChemical engineeringPorosityPolymerNanotechnologyPolymer chemistryChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Nanoliposomes are widely used as delivery vehicles for active compounds. Nanoliposomes from rapeseed phospholipids were incorporated into interpenetrating polymer network hydrogels of gelatin methacryloyl and alginate. The multiscale physicochemical properties of the hydrogels are studied both on the surface and through the thickness of the 3D network. The obtained composite hydrogels exhibited strong mechanical properties and a highly porous surface. The blend ratio, as well as the concentration of nanoliposomes, affects the properties of the hydrogels. Nanofunctionalized hydrogels induced keratinocyte ​growth. These advantageous characteristics may open up many applications of the developed hydrogels in drug delivery and tissue engineering.

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 score0.003

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

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