Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it