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Hybrid GelMA-HAMA hydrogels incorporating dexamethasone-loaded PLGA nanoparticles for controlled release and osteogenic differentiation for bone tissue regeneration

2025· article· en· W4414462638 on OpenAlexaff
Brenda Velasco, Luis Díaz‐Gómez, Luis Carlos Rosales‐Rivera, Alberto Pardo, Sílvia Barbosa, J. F. A. Soltero, Pablo Taboada

Bibliographic record

VenueEuropean Polymer Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsInstitute of Particle Physics
FundersAgencia Estatal de InvestigaciónXunta de GaliciaEuropean Regional Development FundConsellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia
KeywordsSelf-healing hydrogelsGelatinControlled releaseHyaluronic acidMesenchymal stem cellPLGAAlkaline phosphataseRegeneration (biology)

Abstract

fetched live from OpenAlex

• Characterization of methacrylated gelatin (GelMA)/ hyaluronic acid (HAMA) hydrogels. • A single emulsion synthesized dexamethasone-loaded poly(lacticco-glycolic acid) nanoparticles (DEX-PLGA NPs). • 2. The DEX-PLGA NPs were incorporated into the polymeric hydrogels to achieve a controlled and sustained drug release. • The physicochemical properties of the NP-loaded hydrogels were not affected by the incorporation of DEX- PLGA NPs. • In vitro studies showed that these hybrid hydrogels are biocompatible and present excellent cell adhesion, proliferation, and differentiation properties. • The sustained release of dexamethasone ensures the progressive osteogenic differentiation of adipose-derived mesenchymal stem cells (adMSC) within the scaffold The development of novel approaches to bone regeneration remains a challenge in medicine. For such, the control release of biochemical factors appears key to successfully regulate the regeneration process. In this work, the characterization of methacrylated gelatin (GelMA)-hyaluronic acid (HAMA) hydrogels that incorporated dexamethasone-loaded poly(lactic-co-glycolic acid) nanoparticles (DEX-PLGA NPs) was explored as potential scaffolds for bone tissue regeneration. The DEX-PLGA NPs were synthesized and incorporated into the polymeric hydrogels to achieve a controlled and sustained release of the drug in order to ensure the progressive osteogenic differentiation of adipose-derived mesenchymal stem cells (adMSC) within the scaffold. The physicochemical properties of the NP-loaded hydrogels were not affected by the incorporation of DEX-PLGA NPs. In vitro studies demonstrated that these hybrid hydrogels are biocompatible and presented excellent cell adhesion, proliferation, and differentiation properties promoted by the sustained release of dexamethasone as observed, for example, by the alkaline phosphatase (ALP) assay, which confirmed large concentrations of phosphate ions after the first 14 days of incubation. Furthermore, Alizarin Red S staining corroborated a good mineralization, indicative of effective bone matrix formation.

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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