Manganese-impregnated mesoporous silica nanoparticles for signal enhancement in MRI cell labelling studies
Bibliographic record
Abstract
Mesoporous silica nanoparticles (MSNs) are used in drug delivery and cell tracking applications. As Mn(2+) is already implemented as a "positive" cell contrast agent in preclinical imaging procedures (in the form of MnCl2 for neurological studies), the introduction of Mn in the porous network of MSNs would allow labelling cells and tracking them using MRI. These particles are in general internalized in endosomes, an acidic environment with high saline concentration. In addition, the available MSN porosity could also serve as a carrier to deliver medical/therapeutic substances through the labelled cells. In the present study, manganese oxide was introduced in the porous network of MCM-48 silica nanoparticles (Mn-M48SNs). The particles exhibit a narrow size distribution (~140 nm diam.) and high porosity (~60% vol.), which was validated after insertion of Mn. The resulting Mn-M48SNs were characterized by TEM, N2 physisorption, and XRD. Evidence was found with H2-TPR, and XPS characterization, that Mn(II) is the main oxidation state of the paramagnetic species after suspension in water, most probably in the form of Mn-OOH. The colloidal stability as a function of time was confirmed by DLS in water, acetate buffer and cell culture medium. In NMR data, no significant evidence of Mn(2+) leaching was found in Mn-M48SNs in acidic water (pH 6), up to 96 hours after suspension. High longitudinal relaxivity values of r1 = 8.4 mM(-1) s(-1) were measured at 60 MHz and 37 °C, with the lowest relaxometric ratios (r2/r1 = 2) reported to date for a Mn-MSN system. Leukaemia cells (P388) were labelled with Mn-M48SNs and nanoparticle cell internalization was confirmed by TEM. Finally, MRI contrast enhancement provided by cell labelling with escalated incubation concentrations of Mn-M48SNs was quantified at 1 T. This study confirmed the possibility of efficiently confining Mn into M48SNs using incipient wetness, while maintaining an open porosity and relatively high pore volume. Because these Mn-labelled M48SNs express strong "positive" contrast media properties at low concentrations, they are potentially applicable for cell tracking and drug delivery methodologies.
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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) | 0.001 | 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".