MétaCan
Menu
Back to cohort
Record W4400292676 · doi:10.1021/acsnano.4c03839

In Operando Imaging Electrostatic-Driven Disassembly and Reassembly of Collagen Nanostructures

2024· article· en· W4400292676 on OpenAlexfundno aff
Clara Garcia-Sacristán, Victor G. Gisbert, K. Klein, Anđela Šarić, Ricardo Garcı́a

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsnot available
FundersMinisterio de Ciencia e InnovaciónH2020 European Research CouncilMinisterio de Ciencia, Innovación y UniversidadesSimon Fraser UniversityEuropean CommissionRoyal Society
KeywordsNanostructureElectrostaticsNanoscopic scaleMoleculeMolecular dynamicsStatic electricityNanotechnologyAtomic force microscopyBiophysicsChemistryElectrostatic interactionMaterials scienceChemical physicsComputational chemistryOrganic chemistryBiologyPhysics

Abstract

fetched live from OpenAlex

Collagen is the most abundant protein in tissue scaffolds in live organisms. Collagen can self-assemble in vitro, which has led to a number of biotechnological and biomedical applications. To understand the dominant factors that participate in the formation of collagen nanostructures, here we study in real time and with nanoscale resolution the disassembly and reassembly of collagens. We implement a high-speed force microscope, which provides in situ high spatiotemporal resolution images of collagen nanostructures under changing pH conditions. The disassembly and reassembly are dominated by the electrostatic interactions among amino-acid residues of different molecules. Acidic conditions favor disassembly by neutralizing negatively charged residues. The process sets a net repulsive force between collagen molecules. A neutral pH favors the presence of negative and positively charged residues along the collagen molecules, which promotes their electrostatic attraction. Molecular dynamics simulations reproduce the experimental behavior and validate the electrostatic-based model of the disassembly and reassembly processes.

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 score0.042
Threshold uncertainty score0.339

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 head0.003
GPT teacher head0.270
Teacher spread0.267 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueACS NanoSame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207