Rapid <sup>18</sup>F-Labeling and Loading of PEGylated Gold Nanoparticles for in Vivo Applications
Bibliographic record
Abstract
Water-soluble 3 nm maleimide-terminated PEGylated gold nanoparticles (maleimide-AuNP) were synthesized in both partially hydrolyzed and nonhydrolyzed forms. Both of these maleimide-AuNPs, when reacted with the silicon-fluorine prosthetic group [(18)F]SiFA-SH, resulted in radiolabeled AuNPs. These NPs were readily purified with high radiochemical yields (RCY) of 60-80% via size exclusion chromatography. Preliminary small animal positron emission tomography (PET) measurements in healthy rats gives information about the pathway of excretion and the stability of the radioactive label in vivo. The partially hydrolyzed [(18)F]SiFA-maleimide-AuNPs shows uptake in the brain region of interest (ROI) (> 0.13%ID/g) which was confirmed by ex vivo examination of the thoroughly perfused rat brain. The multiple maleimide end groups on the AuNP surface also allows for the simultaneous incorporation of [(18)F]SiFA-SH and a bioactive peptide (cysteine-modified octreotate, cys-TATE, which can bind to somatostatin receptor subtypes 2 and 5) in a proof-of-concept study. The well-defined Michael addition reaction between various thiol containing molecules and the multifunctionalized maleimide-AuNPs thus offers an opportunity to develop a new bioconjugation platform for new diagnostics as well as therapeutics.
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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 | 0.000 | 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 | 0.001 |
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".