An Efficient <i>T</i><sub>1</sub> Contrast Agent for Labeling and Tracking Human Embryonic Stem Cells on MRI
Bibliographic record
Abstract
Noninvasive cell tracking in vivo has the potential to advance stem cell-based therapies into the clinic. Magnetic resonance imaging (MRI) provides an excellent image-guidance platform; however, existing MR cell labeling agents are fraught with limited specificity. To address this unmet need, we developed a highly efficient manganese porphyrin contrast agent, MnEtP, using a two-step synthesis. In vitro MRI at 3 Tesla on human embryonic stem cells (hESCs) demonstrated high labeling efficiency at a very low dose of 10 µM MnEtP, resulting in a four-fold lower T1 relaxation time. This extraordinarily low dose is ideal for labeling large cell numbers required for large animals and humans. Cell viability and differentiation capacity were unaffected. Cellular manganese quantification corroborated MRI findings, and the agent localized primarily on the cell membrane. In vivo MRI of transplanted hESCs in a rat demonstrated excellent sensitivity and specificity of MnEtP for noninvasive stem cell tracking.
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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 | 0.001 |
| 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".