Human Aortic Endothelial Cell Labeling with Positive Contrast Gadolinium Oxide Nanoparticles for Cellular Magnetic Resonance Imaging at 7 Tesla
Bibliographic record
Abstract
Positive T₁ contrast using gadolinium (Gd) contrast agents can potentially improve detection of labeled cells on magnetic resonance imaging (MRI). Recently, gadolinium oxide (Gd₂O₃) nanoparticles have shown promise as a sensitive T₁ agent for cell labeling at clinical field strengths compared to conventional Gd chelates. The objective of this study was to investigate Gado CELLTrack, a commercially available Gd₂O₃ nanoparticle, for cell labeling and MRI at 7 T. Relaxivity measurements yielded r1 = 4.7 s⁻¹ mM⁻¹ and r₂/r₁ = 6.2. Human aortic endothelial cells were labeled with Gd₂O₃ at various concentrations and underwent MRI from 1 to 7 days postlabeling. The magnetic resonance relaxation times T₁ and T₂ of labeled cell pellets were measured. Cellular contrast agent uptake was quantified by inductively coupled plasma-atomic emission spectroscopy, which showed very high uptake compared to conventional Gd compounds. MRI demonstrated significant positive T₁ contrast and stable labeling on cells. Enhancement was optimal at low Gd concentrations, attained in the 0.02 to 0.1 mM incubation concentration range (corresponding cell uptake was 7.26 to 34.1 pg Gd/cell). Cell viability and proliferation were unaffected at the concentrations tested and up to at least 3 days postlabeling. Gd₂O₃ is a promising sensitive and stable positive contrast agent for cellular MRI at 7 T.
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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 | 0.000 |
| 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 | 0.001 |
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".