A population analysis of VEGF transgene expression and secretion
Bibliographic record
Abstract
The induction of therapeutic angiogenesis with gene therapy approaches has received considerable interest and some limited clinical success. A major drawback to this approach is a lack of understanding of the pharmacokinetics of therapeutic protein delivery. This has become increasingly more relevant as recent studies have illustrated a defined therapeutic window for angiogenic protein secretion into the local microenvironment. For cell based gene therapies, with cells widely distributed throughout the tissue, this implies that any individual cell must attain a specific secretion rate to produce a local angiogenic response. Here we report a reproducible technique enabling the study of growth factor secretion from individual cells following transient plasmid transfection. We demonstrate significant variability in single cell vascular endothelial growth factor (VEGF) secretion with the majority of total protein secretion arising from a small subpopulation of transfected cells. We demonstrate that VEGF secretion is linearly correlated to intracellular plasmid copy number and protein secretion does not appear to reach saturation within the cell population. The selection of gene therapy approaches that optimize individual cell secretion profiles may be essential for the development of effective gene therapies.
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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 | 0.001 | 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 | 0.001 |
| 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".