Growth Regulation of Nervous System Tumours: Models for Assessment of Angiogenesis in Brain Tumours
Bibliographic record
Abstract
The metabolic demand of rapidly proliferating tumour cells is reliant on an adequate blood supply that allows the continual delivery of oxygen, nutrients and growth factors. The growth and progression of tumours is significantly reduced in the absence of neovascularization and often increased abnormal neoangiogenesis correlates with the increased malignancy and poor prognosis in many tumours. By far, the most studied and understood mechanism of blood vessel formation is via angiogenesis, a process that initiates the sprouting and elongation of existing vessels into the tumour. However, more recent concepts suggest that in large tumours, the process of vasculogenesis, whereby bone marrow derived progenitor cells (BMDPCs) are recruited to the tumour and differentiate into ECs and other vascular cell types, is a more important mechanism of generating de novo vessels.The mechanisms underlying both processes are poorly understood and the redundancy between signalling pathways involved leads to complications in elucidating the control mechanisms involved. Using various experimental techniques to investigate the processes of tumour neovascularization is an evolving field, one which in this chapter we try to summarize and provide an overview of both the traditional and more novel experimental techniques used to study angiogenesis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".