Small Quantum Dots Conjugated to Nanobodies as Immunofluorescence Probes for Nanometric Microscopy
Bibliographic record
Abstract
Immunofluorescence, a powerful technique to detect specific targets using fluorescently labeled antibodies, has been widely used in both scientific research and clinical diagnostics. The probes should be made with small antibodies and high brightness. We conjugated GFP binding protein (GBP) nanobodies, small single-chain antibodies from llamas, with new ∼7 nm quantum dots. These provide simple and versatile immunofluorescence nanoprobes with nanometer accuracy and resolution. Using the new probes we tracked the walking of individual kinesin motors and measured their 8 nm step sizes; we tracked Piezo1 channels, which are eukaryotic mechanosensitive channels; we also tracked AMPA receptors on living neurons. Finally, we used a new super-resolution algorithm based on blinking of (small) quantum dots that allowed ∼2 nm precision.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".