Immuno-Atomic Force Microscopy Characterization of Adsorbed Fibronectin
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".