Nanoscale Aggregation of Cellular β<sub>2</sub>-Adrenergic Receptors Measured by Plasmonic Interactions of Functionalized Nanoparticles
Bibliographic record
Abstract
Adrenergic signaling that controls the contraction of cardiac myocyte cells and the beating of the mammalian heart is initiated by ligand binding to adrenergic receptors contained in nanoscale multiprotein complexes at the cellular membrane. Here we demonstrate that the surface-enhanced Raman scattering (SERS) of functionalized silver nanoparticles can be used to report on the receptor aggregation state of specifically label beta(2)-adrenergic receptors on mouse cardiac myocyte cells. Furthermore, multimodal imaging including Raman, Rayleigh scattering, scanning electron microscopy, and luminescence imaging was combined to fully characterize the beta(2)-adrenergic receptor-mediated aggregation of silver nanoparticles on the membrane of cardiac myocytes. Scanning electron microscopy analysis reveals distinct SERS active clusters of between 10 and 70 nanoparticles per signaling domain from ultra-high-resolution images of beta(2)-adrenergic receptor clusters on the cellular membrane. These techniques can be generally applied to study the aggregation of other cell surface receptors and explore their distribution on cell surfaces.
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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".