The Chimeric Human/Mouse Model of Angiogenesis
Bibliographic record
Abstract
Angiogenesis, the formation of new blood vessels from preexisting vessels, is an essential component of many normal biological processes such as embryonic development, wound healing, and endometrial maturation in premenopausal women ( 1 – 3 ). This process is similar to, but not identical with vasculogenesis, which is associated with the development of blood vessels from precursor cells termed angioblasts ( 4 , 5 ). Under normal physiological conditions the complex cellular events controlling vascular development are tightly regulated. However, when the molecular and biochemical mechanisms controlling angiogenesis are disrupted, uncontrolled neovascularization can contribute to a number of pathologies. In fact, several clinically important human diseases are characterized by abnormal vascular development including solid tumor growth, rheumatoid arthritis, diabetic retinopathy, and psoriasis ( 1 – 3 , 6 – 8 ). Thus, the pathological consequences of abnormal neovascularization impacts a large segment of the population and clearly demonstrates the need for an in depth understanding of the molecular mediators involved in the regulation of angiogenesis. To this end, an expanding body of work has identified a wide variety of molecules as potential targets for antiangiogenic strategies including a complex network of cytokines, cell adhesion receptors, proteolytic enzymes, and extracellular matrix components ( 9 – 11 ). Interestingly, many of these important discoveries were first identified by the use of in vitro and in vivo angiogenesis models. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".