MnO-Labeled Cells: Positive Contrast Enhancement in MRI
Bibliographic record
Abstract
Manganese oxide (MnO) nanoparticles have been suggested as a promising "positive" MRI contrast agent for cellular and molecular studies. Mn-based contrast agents could enable T(1)-weighted quantitative cell tracking procedures in vivo based on signal enhancement. In this study, ultrasmall MnO particles were synthesized and coated with thiolated molecules (DMSA) and polyethylene glycol (PEG) to allow enhanced cell labeling properties and colloidal stability. This coating allowed the fabrication of individual ultrasmall nanoparticles of MnO (USPMnO) as well as of nanoaggregates of the same material (SPMnO). Particle size was measured by TEM and DLS. Physico-chemical properties were characterized by XPS and FTIR. The relaxometric properties of these aqueous suspensions were measured at various magnetic fields. The suspensions provided strong positive contrast enhancement in T(1)-weighted imaging due to high longitudinal relaxivities (r(1)) and low r(2)/r(1) ratios (USPMnO: r(1) = 3.4 ± 0.1 mM(-1)s(-1), r(2)/r(1) = 3.2; SPMnO: r(1) = 17.0 ± 0.5 mM(-1)s(-1), r(2)/r(1) = 4.0, at 1.41T). HT-1080 cancer cells incubated with the contrast agents were clearly visualized in MRI for Mn contents >1.1 pg Mn/cell. The viability of cells was not affected, contrarily to cells labeled with an equivalent concentration of Mn(2+) ions. A higher signal per cell was found for SPMnO-labeled compared with USPMnO-labeled cells, due to the higher relaxometric properties of the agglomerates. As a result, the "positive" signal enhancement effect is not significantly affected upon agglomeration of MnO particles in endosomes. This is a major requirement in the development of reliable cell tracking procedures using T(1)-weighted imaging sequences. This study confirms the potential of SPMnO and USPMnO to establish more quantitative cell tracking procedures with MRI.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".