Bibliographic record
Abstract
Radiolabeled peptides have been the subject of intense research efforts for targeted diagnostic imaging and radiotherapy over the last 20 years. Peptides offer several advantages for receptor imaging and targeted radiotherapy. The low molecular weight of peptides allows for rapid clearance from the blood and non-target tissue, which results in favorable target-to-non-target ratios. Moreover, peptides usually display good tissue penetration and they are generally non-immunogenic. A major drawback is their potential low metabolic stability. The majority of currently used radiolabeled peptides for targeted molecular imaging and therapy of cancer is labeled with various radiometals like 99mTc, 68Ga, and 177Lu. However, over the last decade an increasing number of 18F-labeled peptides have been reported. Despite of obvious advantages of 18F like its ease of production in large quantities at high specific activity, the low β+ energy (0.64 MeV) and the favorable half-life (109.8 min), 18F-labeling of peptides remains a special challenge. The first part of this review will provide a brief overview on chemical strategies for peptide labeling with 18F. A second part will discuss recent technological advances for 18F-labeling of peptides with special focus on microfluidic technology, automation, and kit-like preparation of 18F-labeled peptides.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".