Nanoscale Imaging of Epidermal Growth Factor Receptor Clustering
Bibliographic record
Abstract
The development of some solid tumors is associated with overexpression of the epidermal growth factor receptor (EGFR) and often correlates with poor prognosis. Near field scanning optical microscopy, a technique with subdiffraction-limited optical resolution, was used to examine the influence of two inhibitors (the chimeric 225 antibody and tyrosine phosphorylation inhibitor AG1478) on the nanoscale clustering of EGFR in HeLa cells. The EGFR is organized in small clusters, average diameter of 150 nm, on the plasma membrane for both control and EGF-treated cells. The numbers of receptors in individual clusters vary from as few as one or two proteins to greater than 100. Both inhibitors yield an increased cluster density and an increase in the fraction of clusters with smaller diameters and fewer receptors. Exposure to AG1478 also decreases the fraction of EGFR that colocalizes with both rafts and caveolae. EGF stimulation results in a significant loss of the full-length EGFR from the plasma membrane with the concomitant appearance of low molecular mass proteolytic products. By contrast, AG1478 reduces the level of EGFR degradation. Changes in receptor clustering provide one mechanism for regulating EGFR signaling and are relevant to the design of strategies for therapeutic interventions based on modulating EGFR signaling.
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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".