Kit-Like <sup>18</sup>F-Labeling of Proteins: Synthesis of 4-(Di-<i>tert</i>-butyl[<sup>18</sup>F]fluorosilyl)benzenethiol (Si[<sup>18</sup>F]FA-SH) Labeled Rat Serum Albumin for Blood Pool Imaging with PET
Bibliographic record
Abstract
Radiosyntheses of 18F-radiopharmaceuticals for positron emission tomography (PET) normally require an extraordinarily high effort of technical equipment and specially trained personnel. We recently reported a novel method for the introduction of fluorine-18 into peptides for PET-imaging based on silicon-18F-chemistry (SiFA technique). We herewith introduce the first SiFA-based Kit-like radio-fluorination of a protein (rat serum albumin,RSA) and demonstrate its usefulness for in vivo imaging with microPET in normal rats as well as in a rat heterotropic transplanted heart model. As a labeling agent, we prepared 4-(di-tert-butyl[18F]fluorosilyl)benzenethiol (Si[18F]FASH)by simple isotopic exchange in 40-60% radiochemical yield (RCY) and coupled it directly to a Sulfo-SMCC derivatized RSA in an overall RCY of 12% within 20-30 min. The technically simple labeling procedure does not require any elaborated purification procedures and is a straightforward example of a successful application of Si-18F chemistry for in vivo imaging with PET.
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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 | 0.001 |
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".