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Construcción de matrices como sustitutos dérmicos: aplicación potencial en la regeneración de piel (MATRIX CONSTRUCTION AS DERMAL SUBSTITUTES: POTENTIAL APPLICATION IN SKIN REGENERATION)

2014· article· es· W1848852161 on OpenAlexaff
Juliana Valencia Serna, Catalina Pineda Molina

Bibliographic record

VenueRevista Ingeniería Biomédica · 2014
Typearticle
Languagees
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryMolecular biologyBiology

Abstract

fetched live from OpenAlex

La pérdida de grandes porciones de piel limita su capacidad de regeneración, haciendo difícil su tratamiento. Mediante la ingeniería de tejidos se han desarrollado sustitutos dérmicos que promueven la regeneración; sin embargo, es necesario hallar un material para el crecimiento de fibroblastos que sea un sustituto dérmico adecuado. En este trabajo se construyeron matrices tridimensionales porosas de colágeno y colágeno-ácido hialurónico evaluando su potencial como sustituto dérmico. Las matrices se entrecruzaron con 1-etil-3-(3-dimetilaminopropil) carbodiimida hidrocloruro. Se evaluó la porosidad, se realizaron pruebas de degradación, evaluación de adherencia celular con fibroblastos humanos y, ensayos de viabilidad y proliferación. Las matrices presentaron poros de 50 µm de diámetro uniformemente distribuidos. Las matrices de colágeno y colágeno-ácido hialurónico (2:8 y 4:6) presentaron una degradación progresiva con pesos similares. Luego de 24 horas en cultivo, los resultados de la prueba de MTT mostraron que los fibroblastos en colágeno proliferaron; mientras que los fibroblastos en colágeno-ácido hialurónico no evidenciaron proliferación. Aunque la estructura porosa y la tasa de degradación de las matrices son similares, las de colágeno brindan mejores condiciones de adhesión y proliferación a los fibroblastos cultivados en comparación con las que tienen ácido hialurónico como adición, convirtiéndose en el mejor andamio para ser empleado como sustituto dérmico entre las matrices evaluadas.Abstract: Wounds can cause loss of skin that limits the skin regeneration, making the treatment more difficult to address. The tissue engineering has developed skin substitutes that promote skin regeneration; however, it is imperative to find materials that allow fibroblast growth in order to find an appropriate skin substitute. The construction of tridimensional porous collagen and collagen hyaluronic acid matrixes crosslinked with 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide hydrochloride is presented in order to evaluate their potential use in skin regeneration therapies. Matrix porosity was evaluated. Degradation tests, human fibroblast adherence, viability and proliferation tests were performed. Matrixes had a uniform distributed porosity with mean diameters of 50 µm. Both collagen and collagen-hyaluronic acid (2:8 and 4:6) matrixes presented a progressive degradation rate with similar weight. After 24 hours under culture, the number of fibroblasts seeded on collagen matrixes were doubled, while the number of fibroblast seeded on collagen-hyaluronic acid matrixes remained similar. Even though porous structure and degradation rate of different types of constructed matrixes in this study are similar, collagen matrixes offer better adhesion and proliferation conditions for seeded fibroblasts in comparison with hyaluronic acid added matrixes, making them the best scaffold to be employed as a dermic substitute among the matrixes compared here.

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: 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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.257
Teacher spread0.254 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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