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Record W4413107531 · doi:10.1002/adhm.202502465

Biomimetic Mineralization of Keratin Scaffolds for Enamel Regeneration

2025· article· en· W4413107531 on OpenAlexaff
Sara Gamea, Elham Radvar, Dimitra Athanasiadou, Ryan Lee Chan, Giacomo De Sero, Ecaterina Ware, Sunie Kundi, Shwan Horamee, Shuaib Hadadi, Mads Carlsen, Leanne Allison, Roland A. Fleck, K. L. Andrew Chan, Avijit Banerjee, Nicola M. Pugno, Marianne Liebi, Paul T. Sharpe, Karina M. M. Carneiro, Sherif Elsharkawy

Bibliographic record

VenueAdvanced Healthcare Materials · 2025
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversity of Toronto
FundersPaul Scherrer InstitutKing's College LondonMinistry of Scientific Research, EgyptAcademy of Medical SciencesWellcome TrustNational Institute of Health Sciences
KeywordsRegeneration (biology)Mineralization (soil science)Enamel paintKeratinMaterials scienceBiomineralizationDentistryChemistryComposite materialGeologyChemical engineeringEngineeringBiologyCell biologyMedicine

Abstract

fetched live from OpenAlex

Biomimetic protein-based platforms, with their hierarchical networks and optimal mechanical properties, show promising potential for hard tissue regeneration, including dental enamel. However, achieving aligned enamel-like apatite nanocrystals from organic matrices remains challenging. A simple organic-based approach to re-create the hierarchical enamel structure using water-based keratin films is reported. These films assemble via disulfide bridging into a fibrous organic network and birefringent spherulitic construction of predominant ordered β-sheet conformation. The flexible structure of keratin templates facilitates rearrangement of the secondary structures into α-helices upon mineralization, guiding the ordered growth of apatite nanocrystals. This system has shown potential in repairing early defective dental enamel lesions, restoring both optical appearance and mechanical properties. This study offers a promising, simple, and clinically-friendly method for developing novel protein-based matrices for hard tissue regeneration from naturally abundant sources.

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 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 score0.016
Threshold uncertainty score0.368

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 head0.018
GPT teacher head0.351
Teacher spread0.333 · 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.

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

Citations9
Published2025
Admission routes1
Has abstractyes

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