MétaCan
Menu
Back to cohort
Record W4415947713 · doi:10.1002/admi.202500609

3D‐Printed Functional Biphasic Scaffolds with Nanocomposites for Osteochondral Regeneration: A Step Toward Bioengineered Cartilage and Bone Integration

2025· article· en· W4415947713 on OpenAlexaff
Negin Khoshnood, Mostafa Shahrezaee, John P. Frampton, Mohammad Hossein Shahrezaee, Ali Zamanian

Bibliographic record

VenueAdvanced Materials Interfaces · 2025
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsScaffoldCartilageExtracellular matrixMesenchymal stem cellMatrix (chemical analysis)Layer (electronics)Tissue engineeringNanocomposite

Abstract

fetched live from OpenAlex

Abstract Repair of osteochondral defects remains a great challenge because of the complex interplay between cartilage and subchondral bone, each of which has distinct structural, biological, and mechanical properties. Here, the fabrication and of a novel 3D printed biphasic osteochondral scaffold composed of polycaprolactone/laponite (PL) is demonstrated for the bone layer and methylsulfonylmethane (MSM)‐loaded polycaprolactone/chitosan (PC) for the cartilage layer. Comprehensive characterization of the scaffold revealed gradient mechanical properties, high biocompatibility, and hydrophilicity, replicating the structural requirements of native osteochondral tissue. In vitro biological assays demonstrated enhanced cell adhesion, proliferation, and differentiation of bone marrow‐derived mesenchymal stem cells for both cartilage and bone layers. The PL layer exhibited osteogenic capacity, while the MSM‐loaded PC layer facilitated chondrogenesis. Additionally, the scaffold displayed controlled degradation and sustained release of MSM, further promoting extracellular matrix production. Altogether, the results suggest that the designed biphasic scaffold represents a promising platform for osteochondral tissue regeneration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Materials InterfacesSame topicBone Tissue Engineering MaterialsFrench-language works237,207