99mTc-Labeled Bevacizumab via HYNIC for Imaging of Melanoma
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is one of the classic factors to tumour-induced angiogenesis in several types, including melanoma. Bevacizumab, a monoclonal antibody anti-VEGF, could be used as an imaging tool in clinical studies. The aim of this study was to radiolabeled Bevacizumab with 99mTc and evaluate it in vivoimaging properties. Bevacizumab was derivatized with the activated ester succinimidyl-hydrazinonicotinamide hydrochloride (Suc-HYNIC) as a bifunctional coupling agent. A mixture of Tricine/SnCl2.2H2O was added to Bevacizumab-HYNIC and radiolabeled with 99mTcO4-. The radiochemical stability of the radiolabeled sntibody was assessed. Biodistribution studies and SPECT-CT imaging were evaluated in healthy and tumor-bearing C57BL/6J mice at 1, 4 and 24 h (n =5). We demonstrated that 99mTc-HYNIC-Bevacizumab was stable over 24 h in solution and serum. In vivo biodistribution studies revealed tumor-to-muscle ratios of 99mTc-HYNIC-Bevacizumab was 9.28, 17.19 and 8.51 at 1, 4 and 24 h p.i. SPECT/CT imaging of tumor-bearing C57BL/6J mice showed tumor selective uptake of 99mTc-HYNIC-Bevacizumab. 99mTc-HYNIC-Bevacizumab could become a potential radiopharmaceutical to evaluate the expression of vascular endothelial growth factor (VEGF) in solid tumors and could be seen as a clinic tool for the screening of solid tumors that might respond to the Bevacizumab chemotherapy.
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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 | 0.000 |
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".