Bifunctional Pyrrolidin-2-one Terminated Manganese Oxide Nanoparticles for Combined Magnetic Resonance and Fluorescence Imaging
Bibliographic record
Abstract
Multimodal probes are an asset for simplified, improved medical imaging. In particular, fluorescence and magnetic resonance imaging (MRI) are sought-after combined capabilities. Here, we show that pyrrolidin-2-one-capped manganese oxide nanoparticles ( MnO pyrr NPs) combine MRI with fluorescence microscopy to function as efficient bifunctional bio-nanoprobes. We employ a one-pot synthesis for ca. 10 nm MnO NPs, wherein manganese(II) 2,4-pentadionate is thermally decomposed using pyrrolidin-2-one as a solvent and capping ligand. The MnO pyrr NPs are soluble in water without any further postsynthetic modifications. The r 1 relaxivity and r 2 /r 1 ratio indicate that these NPs are potential T 1 MRI contrast agents at clinical (3 T) and ultrahigh (9.4 T) magnetic fields. Serendipitously, the as-prepared NPs are photoluminescent. The unexpected luminescence is ascribed to the modification of the pyrrolidin-2-one during the thermal treatment. MnO pyrr NPs are successfully used to enable fluorescence microscopy of HeLa cells, demonstrating bifunctional imaging capabilities. A low cytotoxic response in two distinct cell types (HeLa, HepG2) supports the suitability of MnO pyrr NPs for biological imaging applications.
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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".