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Record W4386447393 · doi:10.1021/acs.nanolett.3c02344

Liquid Transmission Electron Microscopy for Probing Collagen Biomineralization

2023· article· en· W4386447393 on OpenAlexafffund
Liza‐Anastasia DiCecco, Ruixin Gao, Jennifer L. Gray, Deborah F. Kelly, Eli D. Sone, Kathryn Grandfield

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiomineralizationMineralization (soil science)Transmission electron microscopyFibrilCollagen fibrilChemistryMineralized tissuesMaterials scienceElectron microscopeBiophysicsChemical engineeringNanotechnologyComposite materialBiochemistryDentinOpticsOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Collagen biomineralization is fundamental to hard tissue assembly. While studied extensively, collagen mineralization processes are not fully understood, with the majority of theories derived from electron microscopy (EM) under static, dehydrated, or frozen conditions, unlike the liquid phase environment where mineralization occurs. Herein, novel liquid transmission EM (TEM) strategies are presented, in which collagen mineralization was explored in liquid for the first time via TEM. Custom thin-film enclosures were employed to visualize the mineralization of reconstituted collagen fibrils in a calcium phosphate and polyaspartic acid solution to promote intrafibrillar mineralization. TEM highlighted that at early time points precursor mineral particles attached to collagen and progressed to crystalline mineral platelets aligned with fibrils at later time points. This aligns with observations from other techniques and validates the liquid TEM approach. This work provides a new liquid imaging approach for exploring collagen biomineralization, advancing toward understanding disease pathogenesis and remineralization strategies for hard tissues.

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 score0.058
Threshold uncertainty score0.457

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

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