Biodistribution studies of ultrasmall silicon nanoparticles and carbon dots in experimental rats and tumor mice
Bibliographic record
Abstract
Ultrasmall clearable nanoparticles possess enormous potential as cancer imaging agents. In particular, biocompatible silicon nanoparticles (Si NPs) and carbon quantum dots (CQDs) hold great potential in this regard. Their facile surface functionalization easily allows the introduction of different labels for in vivo imaging. However, to date, a thorough biodistribution study by in vivo positron emission tomography (PET) and a comparative study of Si vs. C particles of similar size are missing. In this contribution, ultrasmall (size <5 nm) Si NPs and CQDs were synthesized and characterized by high-resolution transmission electron microscopy (HR-TEM), Fourier-transform infrared (FTIR), absorption and steady-state emission spectroscopy. Subsequent functionalization of NPs with a near-infrared dye (Kodak-XS-670) or a radiolabel (64Cu) enabled a detailed in vitro and in vivo study of the particles. For radiolabeling experiments, the bifunctional chelating agent S-2-(4-isothiocyanatobenzyl)-1,4,7-triazacyclononane-1,4,7-triacetic acid (p-SCN-Bn-NOTA) was conjugated to the amino surface groups of the respective NPs. Efficient radiolabeling of NOTA-functionalized NPs with the positron emitter 64Cu was found. The biodistribution and PET studies showed a rapid renal clearance from the in vivo systems for both variants of the nanoparticles. Interestingly, the different derivatives investigated exhibited significant differences in the biodistribution and pharmacokinetic properties. This can mostly be attributed to different surface charge and hydrophilicity of the NPs, arising from the synthetic strategy used to prepare the particles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".