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Record W4409800439 · doi:10.1177/08853282251336552

<i>In vitro</i> characterization of 3D printed polycaprolactone/graphene oxide scaffolds impregnated with alginate and gelatin hydrogels for bone tissue engineering

2025· article· en· W4409800439 on OpenAlex
Shaghayegh Amini-Mosleh-Abadi, Zahra Yazdanpanah, Farinaz Ketabat, Mahya Saadatifar, Mohammad Mohammadi, Ali Sadeghianmaryan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Biomaterials Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials sciencePolycaprolactoneGelatinSelf-healing hydrogelsScaffoldSwellingComposite numberElastic modulusComposite materialCompressive strengthGrapheneTissue engineeringModulusBiomedical engineeringPolymerNanotechnologyChemistryPolymer chemistry

Abstract

fetched live from OpenAlex

To achieve successful bone tissue engineering (BTE), it is necessary to fabricate a biomedical scaffold with appropriate structure as well as favorable composition. Despite a broad range of studies, this remains a challenge, highlighting the need for a better understanding of how structural features (e.g., pore size) and scaffold composition influence mechanical and physical properties, as well as cellular behavior. Therefore, the objective of this study was to characterize physical properties (swelling, degradation), mechanical properties (compressive modulus), and cellular behavior in relation to varying compositions (referred to composite and hybrid scaffolds) as well as varying pore sizes in three-dimensional (3D) printed scaffolds. Composite scaffolds were fabricated from polycaprolactone (PCL) and two different graphene oxide (GO) (3% and 9% (w/v)) concentrations. Additionally, hybrid scaffolds were fabricated by impregnating 3D printed scaffolds in a hydrogel blend of alginate/gelatin. Pore sizes of 400, 1000, and 1500 μm were investigated in this study to assess their effect on the scaffold properties. Our findings showed that swelling and degradation properties were enhanced by (I) the addition of GO as well as introduction of both hydrogel and highest concentration of GO (9% (w/v) GO) into the polymeric matrix of PCL, and (II) increasing the pore size within the scaffolds. Mechanical testing revealed that compressive elastic modulus increased with decreasing pore size, incorporation of GO, and increasing GO content into the matrix of PCL. Although our investigated scaffolds with various pore sizes did not show comparable elastic moduli to that of cortical bone, they exhibited an elastic modulus range (∼31-48 MPa) matching that of trabecular bone. The highest compressive modulus (∼48 MPa) was observed in scaffolds of PCL/9% (w/v) GO (composite scaffolds) with the pore size of 400 μm. Cell viability assay demonstrated high MG-63 cell survival (greater than 70%) in all composite and hybrid scaffolds (indicating scaffold biocompatibility) except PCL/3% (w/v) GO scaffolds. The findings of this study contribute to the field of BTE by providing scaffold design insights in terms of pore size and composition.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.106
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.209
Teacher spread0.206 · 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