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Record W2004933369 · doi:10.1021/la802452v

Immuno-Atomic Force Microscopy Characterization of Adsorbed Fibronectin

2008· article· en· W2004933369 on OpenAlexaff
Jane W.C. Cheung, Gilbert C. Walker

Bibliographic record

VenueLangmuir · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAtomic force microscopyCharacterization (materials science)AdsorptionMicroscopyNanotechnologyChemistryMaterials scienceBiophysicsPhysicsPhysical chemistryOpticsBiology

Abstract

fetched live from OpenAlex

The fibronectin (Fn) binding conformation on mica and ultraflat poly(D,L-lactide-co-glycolide) (UPLGA) was characterized using atomic force microscopy (AFM). AFM topographic images showed that Fn was in an extended form on mica and in a compact structure on UPLGA. With immuno-AFM, an antibody (Ab(hep)) was used to characterize the Fn binding conformation. When Fn opens its binding site for an antibody upon adsorption to a surface, the resulting Fn-antibody complex creates an additional peak in the sample's height distribution. Immuno-AFM uses this change to detect antigen-antibody binding. In this letter, height histograms (distributions) were generated using the mean true height of molecules, which was measured by examining the histogram for each individual molecule and subtracting the mica background. Mean true height values were obtained from the histograms and showed that Fn and Ab(hep) formed complexes on mica, signifying that one of the heparin binding sites on Fn was open when Fn was adsorbed to mica. The mean true height of the Fn-antibody complex from the histogram is greater than expected, suggesting that the antibody had pulled the extended "arms" of Fn together and caused an Fn conformation change upon binding. The height histograms can illustrate the Fn binding conformation and other antigen-antibody binding.

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 score0.028
Threshold uncertainty score0.365

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 head0.007
GPT teacher head0.249
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.

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

Citations18
Published2008
Admission routes1
Has abstractyes

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