Radiolabeling RGD peptide and preliminary biodistribution evaluation in mice bearing S180 tumors
Bibliographic record
Abstract
OBJECTIVE: To prepare the rhenium-188 (188Re)-arginine-glycine-aspartic acid (RGD) peptide in a convenient manner and to evaluate its potential as an agent for alphavbeta3 integrin receptor-positive tumors. METHODS: Radiolabeled RGD was obtained by conjugating the His group at the end of peptide with fac-[188Re(H2O)3(CO)3]+. Chelating efficiency of fac-[188Re(H2O)3(CO)3]+ and radiolabeling efficiency of radiolabeled peptide were measured by thin-layer chromatography and high-performance liquid chromatography. In-vitro stability of the radio-complex was determined in phosphate-buffered saline (0.05 mol/l, pH 7.4), new-born calf serum, His or Cys solution at 37 degrees C or room temperature and analyzed by thin-layer chromatography. A biodistribution study was carried out in mice bearing S180 tumors. RESULTS: 188Re-RGD was obtained with a more than 95% of radiolabeling efficiency, and showed high stability in phosphate-buffered saline, new-born calf serum, His and Cys solution. Furthermore, this radio-complex was cleared rapidly from the blood and showed specific tumor uptake in mice bearing S180 tumors. CONCLUSION: 188Re-RGD was prepared by a simple method. Preliminary biodistribution results showed its potential as an agent for cancer therapy and encouraged further investigation.
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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 | 0.000 |
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".