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Record W3081302228 · doi:10.18433/jpps31254

RECENT ADVANCES IN ELASTIN-BASED BIOMATERIALS

2020· review· en· W3081302228 on OpenAlexvenueno aff
Marí­a Luisa Del Prado-Audelo, Néstor Mendoza‐Muñoz, Lidia Escutia-Guadarrama, David M. Giraldo‐Gómez, Maykel González‐Torres, Benjamí­n Florán, Hernán Cortés, Gerardo Leyva‐Gómez

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoUniversidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y Tecnología
KeywordsElastinSelf-healing hydrogelsTissue engineeringDrug deliveryExtracellular matrixBiomaterialChemistryNanotechnologyBiomedical engineeringMaterials scienceBiochemistryPolymer chemistryEngineeringBiology

Abstract

fetched live from OpenAlex

Elastin is one of the main components of the extracellular matrix; it provides resistance and elasticity to a variety of tissues and organs of the human body, besides participating in cellular signaling. On the other hand, elastin-derived peptides are synthetic biopolymers with a similar conformation and structure to elastin, but these possess the advantage of solubility in aqueous mediums. Due to their biological activities and physicochemical properties, elastin and related peptides may be applied as biomaterials to develop diverse biomedical devices, including scaffolds, hydrogels, and drug delivery systems for tissue engineering. Likewise, the combination of elastin with natural or synthetic polymers has demonstrated to improve the mechanical properties of biomedical products and drug delivery systems. Here we comprehensively describe the physicochemical properties and physiological functions of elastin. Moreover, we offer an overview of the use of elastin and its derivative polymers as biomaterials to develop scaffolds and hydrogels for tissue engineering. Finally, we discuss some perspectives on the employment of these biopolymers to fabricate new biomedical products.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.153
GPT teacher head0.511
Teacher spread0.358 · 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.

Study designOther design
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

Citations48
Published2020
Admission routes1
Has abstractyes

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