Positron emission tomography detection of human endothelial cell and fibroblast monolayers: effect of pretreament and cell density on 18FDG uptake
Bibliographic record
Abstract
BACKGROUND: The non-destructive assessment and characterization of tridimensional (3D) cell and tissue constructs in bioreactors represents a challenge in tissue engineering. Medical imaging modalities, which can provide information on the structure and function of internal organs and tissues in living organisms, have the potential of allowing repetitive monitoring of these 3D cultures in vitro. Positron emission tomography (PET) is the most sensitive non-invasive imaging modality, capable of measuring picomolar amounts of radiolabeled molecules. However, since PET imaging protocols have been designed almost exclusively for in vivo investigations, suitable methods must be devised to enable imaging of cells or tissue substitutes. As a prior step to imaging 3D cultures, cell radiotracer uptake conditions must first be optimized. METHODS: In this study, human umbilical vein endothelial cells (HUVEC) and human fibroblasts were cultured at different densities and PET was used to non-destructively monitor their glycolytic activity by measuring 18F-fluorodeoxyglucose (18FDG) uptake. Various cell preconditioning protocols were investigated by adjusting the following parameters to optimize 18FDG uptake: glucose starvation, insulin stimulation, glucose concentration, 18FDG incubation time, cell density and radiotracer efflux prevention. RESULTS: The conditions yielding optimal 18FDG uptake, and hence best detection sensitivity by PET, were as follows: 2-hour cell preconditioning by glucose deprivation with 1-hour insulin stimulation, followed by 1-hour 18FDG incubation and 15-minute stabilization in standard culture medium, prior to rinsing and PET scanning. CONCLUSIONS: A step-wise dependence of 18FDG uptake on glucose concentration was found, but a linear correlation between PET signal and cell density was observed. Detection thresholds of 36 ± 7 and 21 ± 4 cells were estimated for endothelial cells and fibroblasts, respectively.
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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".