Effects of Quantum Dot Labeling on Endothelial Progenitor Cell Function and Viability
Bibliographic record
Abstract
Endothelial progenitor cells (EPC) play an important role in repairing damaged endothelium. An effective imaging method for in vivo tracking of EPCs is essential for understanding EPC-based cell therapy. Fluorescent quantum dots (QDs) have attractive optical characteristics such as extreme brightness and photostability. QDs are currently being investigated as probes for stem cell labeling; however, there is concern about whether QDs can be used safely. We investigated whether quantum dot (QD) labeling would influence EPC viability and function. Rat bone marrow-derived EPCs were cultured and characterized. The cells were labeled with near-infrared-emitting, carboxyl-coated QDs (8 nM) for 24 h. QD labeling efficiency was higher than 97%. Using WST-1 assay, we showed that the viability of the QD-labeled EPCs was not different from that of the control EPCs. Moreover, QD labeling did not influence the ability of EPCs to form capillary tubes on Matrigel and to migrate. The percentage of QD-positive cells decreased with time, probably due to the rapid division of EPCs. These data suggest that the carboxyl-coated QD705 can be useful for labeling EPCs without interrupting their viability and functions.
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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 | 0.001 |
| 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 | 0.000 |
| 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".