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Record W4383092825 · doi:10.1016/j.heliyon.2023.e17050

3D hydrogel/ bioactive glass scaffolds in bone tissue engineering: Status and future opportunities

2023· review· en· W4383092825 on OpenAlex

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

VenueHeliyon · 2023
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcMaster University
FundersUniversiti Malaysia Pahang
KeywordsTissue engineeringBioactive glassBiomedical engineeringSelf-healing hydrogelsMaterials scienceEngineeringChemical engineering

Abstract

fetched live from OpenAlex

Repairing significant bone defects remains a critical challenge, raising the clinical demand to design novel bone biomaterials that incorporate osteogenic and angiogenic properties to support the regeneration of vascularized bone. Bioactive glass scaffolds can stimulate angiogenesis and osteogenesis. In addition, natural or synthetic polymers exhibit structural similarity with extracellular matrix (ECM) components and have superior biocompatibility and biodegradability. Thus, there is a need to prepare composite scaffolds of hydrogels for vascularized bone, which incorporate to improve the mechanical properties and bioactivity of natural polymers. In addition, those composites' 3-dimensional (3D) form offer regenerative benefits such as direct doping of the scaffold with ions. This review presents a comprehensive discussion of composite scaffolds incorporated with BaG, focusing on their effects on osteo-inductivity and angiogenic properties. Moreover, the adaptation of the ion-doped hydrogel composite scaffold into a 3D scaffold for the generation of vascularized bone tissue is exposed. Finally, we highlight the challenges and future of manufacturing such biomaterials.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.040
GPT teacher head0.269
Teacher spread0.229 · 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