Enhanced Relaxometric Properties of MRI “Positive” Contrast Agents Confined in Three‐Dimensional Cubic Mesoporous Silica Nanoparticles
Bibliographic record
Abstract
Abstract Mesoporous silica nanoparticles (MSNs) are of growing interest for the development of novel probes enabling efficient tracking of cells in vivo using magnetic resonance imaging (MRI). The incorporation of Gd 3+ paramagnetic ions into highly porous MSNs is a powerful strategy to synthesize “positive” MRI contrast agents for more quantitative T 1 ‐weighted MR imaging. Within this context, different strategies have been reported to integrate Gd chelates to 2D pore network MSNs. As an alternative, we report on the modulation of the pore network topology through the preparation of a 3D pore network hybrid GdSi x O y MSN system. In this study, 2D GdSi x O y ‐MSNs with similar porosity and particle size were also prepared and the relaxometric performances of both materials, directly compared. Both syntheses lead to water‐dispersible MSNs suspensions (particle size < 200 nm), which were stable for at least 48h. 3D GdSi x O y ‐MSNs provided a significant increase in 1 H longitudinal relaxivity (18.5 s −1 mM −1 ; 4.6 times higher than Gd‐DTPA) and low r 2 /r 1 ratios (1.56) compatible with the requirements of “positive” contrast agents for MRI. These results demonstrate the superiority of a 3D pore network to host paramagnetic atoms for MRI signal enhancement using T 1 ‐weighted imaging. Such an approach minimizes the total amount of paramagnetic element per particle.
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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".