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Record W4307426413 · doi:10.1088/1748-605x/ac9d7b

Bacterial collagen-templated synthesis and assembly of inorganic particles

2022· article· en· W4307426413 on OpenAlexafffund
Zahra Abdali, Masoud Aminzare, Amy Chow, Noémie‐Manuelle Dorval Courchesne

Bibliographic record

VenueBiomedical Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFaculty of Engineering, McGill University
KeywordsMineralization (soil science)BiomineralizationNucleationMaterials scienceNanoparticleChemical engineeringType I collagenBiophysicsFibrilNanotechnologyChemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Collagen has been used as a common template for mineralization and assembly of inorganic particles, because of the special arrangement of its fibrils and the presence of charged residues. Streptococcal bacterial collagen, which is inherently secreted on the surface of Streptococcus pyogenes , has been progressively used as an alternative for type I animal collagen. Bacterial collagen is rich in charged amino acids, which can act as a substrate for the nucleation and growth of inorganic particles. Here, we show that bacterial collagen can be used to nucleate three different inorganic materials: hydroxyapatite crystals, silver nanoparticles, and silica nanoparticles. Collagen/mineral composites show an even distribution of inorganic particles along the collagen fibers, and the particles have a more homogenous size compared with minerals that are formed in the absence of the collagen scaffold. Furthermore, the gelation of silica occurring during mineralization represents a means to produce processable self-standing collagen composites, which is challenging to achieve with bacterial collagen alone. Overall, we highlight the advantage of simply combining bacterial collagen with minerals to expand their applications in the fields of biomaterials and tissue engineering, especially for bone regenerative scaffolds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.010
GPT teacher head0.204
Teacher spread0.193 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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