MétaCan
Menu
Back to cohort
Record W4381887708 · doi:10.1101/2023.06.19.545614

Liquid Transmission Electron Microscopy for Probing Collagen Biomineralization

2023· preprint· en· W4381887708 on OpenAlexafffund
Liza‐Anastasia DiCecco, Ruixin Gao, Jennifer L. Gray, Deborah F. Kelly, Eli D. Sone, Kathryn Grandfield

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsUniversity of TorontoMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaHuck Institutes of the Life SciencesDivision of Materials ResearchCanada Research ChairsCanadian Institutes of Health ResearchPennsylvania State University
KeywordsBiomineralizationMineralization (soil science)Transmission electron microscopyFibrilCollagen fibrilChemistryMineralized tissuesBiophysicsElectron microscopeChemical engineeringMaterials scienceNanotechnologyComposite materialBiochemistryOpticsDentinOrganic chemistryBiologyPhysicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Collagen biomineralization is foundational to hard tissue assembly. While studied extensively, collagen mineralization processes are not fully understood as the majority of theories are derived from electron microscopy (EM) in static, dehydrated, or frozen conditions, unlike the liquid phase environment where mineralization occurs dynamically. Herein, novel liquid transmission EM (TEM) strategies are presented, where collagen mineralization was explored in liquid conditions for the first time. 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 this 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)0.001
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 score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicCollagen: Extraction and CharacterizationFrench-language works237,207