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Fabrication and properties of alginate-hydroxyapatite biocomposites as efficient biomaterials for bone regeneration

2021· article· en· W3140435341 on OpenAlexfundno aff
Connie Ocando, Sorina Dinescu, Iuliana Samoilă, Cristina Ghiţulică, Andreia Cucuruz, Marieta Costache, Luc Avérous

Bibliographic record

VenueEuropean Polymer Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersConseil régional du Grand EstAgence Universitaire de la Francophonie
KeywordsInterconnectivityRegeneration (biology)Materials scienceScaffoldBiocompatible materialBone tissueNanotechnologyTissue engineeringSelf-healing hydrogelsBiomedical engineeringChemical engineeringPolymer chemistryCell biology

Abstract

fetched live from OpenAlex

Restricted self-recovery that pursues a bone injury and the need of inexpensive and painless clinical treatments for tissue regeneration require new pathways developments to fabricate biocompatible and biodegradable scaffolding materials. For these requirements, covalent crosslinked alginate and alginate/Mg-doped hydroxyapatite (MgHAp) hydrogels are fabricated by “click” chemistry to mimic highly porous structures with the dimensional hierarchy of bone tissue. Cells viability, proliferation and cytotoxicity are reported. These scaffolds show high pore volume with adequate size and interconnectivity for osseous tissue regeneration. An uniform dispersion of low content of bioactive MgHAp nanoparticles on the surface of pore walls allows a good preosteoblast cell attachment and proliferation.

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)0.001
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
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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

Citations37
Published2021
Admission routes1
Has abstractno

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