Observations on the Use of the Avian Chorioallantoic Membrane (CAM) Model in Investigations into Angiogenesis
Bibliographic record
Abstract
The chorioallantoic membrane (CAM) is widely used as a model to examine angiogenesis, and anti-angiogenesis. Its advantages over mammalian systems include low cost, and ease of preparation, as well as the absence of a mature immune system. Although the use of this model presents a major opportunity to compare data generated in different laboratories, thereby expediting the evaluation of new drugs, angiogenic potential of cells and tissues, and body fluids, as well as to provide meaningful information concerning the molecular mechanisms involved, the wide range of methodologies used, especially in the quantification of the response, make any comparison essentially invalid. In this review, the major methodologies for all aspects of the use of the CAM in angiogenesis-related studies have been described. These include the source of the CAM, the methods for culture, and methods for evaluation of normal growth and of the response to an intervention. Methods for applying an intervention, the age for intervention and the duration of an intervention are documented. The structure and growth characteristics, the nature of possible responses to stimulation and inhibition of angiogenesis, and the complications of non-specific reactions have been examined. The need for a standardized approach to the use of the CAM model is obvious. One set of possible parameters is suggested.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".