Methodological Advancements in the CAM Model:Yolk-Delivered Tracers, Dual-Tracer Autoradiography, Dynamic PET, in ovo Proton Irradiation and OCT Vascular Assessment
Bibliographic record
Abstract
Aim The chorioallantoic membrane (CAM) model is a scalable preclinical model capable of high-throughput tumor-bearing in vivo growth of tumor material for oncological research. Our purpose has been to establish the necessary methods for the CAM model to become an attractive platform for advanced nuclear medicine and proton radiation experiments on CAM tumors. The model is based on an extra-embryonal membrane in the eggs of avian and reptilian species. Methods & Results Commercial Dekalb White eggs (gallus gallus domesticus) were opened in a square window and grafted with C3H mammary carcinoma or MOC2 mouse oral carcinoma tumor pieces. We established feasibility of in ovo proton irradiation and optical coherence tomography (OCT) of tumor vasculature. Radioactive tracer uptake via alternative administration routes was proved and quitified with PET and autoradiography. Conclusions Autoradiography was performed on 53 tumors and PET scans of 11 tumors were obtained after using alternative administration routes of various tracers to evaluate intratumoral distribution and pharmacokinetics. Furthermore, we have investigated in ovo proton irradiation and following growth on 16 tumors. To evaluate vessel morphology, we have developed a tool using OCT angiography data to investigate key vessel characteristics. The tumor-bearing CAM model is highly relevant as a preclinical model in nuclear medicine and radiation biology. In ovo proton irradiation with OCT angiography follow-up allows for high radiation doses and functional, non-invasive post-irradiation vascular evaluation. Autoradiography and PET scans after yolk injection of tracers can be used to evaluate physiological changes of vascularization and tumor micromilleu post-irradiation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".