Differences in vasculature between pilocytic and anaplastic astrocytomas of childhood
Bibliographic record
Abstract
BACKGROUND: The clinical manifestations of childhood pilocytic astrocytoma (PA) and anaplastic astrocytoma (AA) markedly differ, especially in the time to progression and prognosis. Because of the aggressive course and poor survival rate of AA, one would expect it to be associated with a high angiogenic index. Counterintuitively, we often find higher microvessel density counts in PA than in AA. PROCEDURE: We examined the differences in type or density of microvasculature between the two neoplasms. To differentiate established, mature vessels from immature growing ones, we used antibodies to Factor VIII (FVIII) to stain endothelial cells (ECs) of blood vessels and alpha-smooth muscle actin (alpha-SMA) antibodies to stain vessels supported by adventitia. RESULTS: We found that large, mature, alpha-SMA-positive vessels predominated in PA, and small, immature, alpha-SMA-negative vessels in AA. The vessel maturation index was 54.5% for PA, and 6.1% for AA. Immunostaining with vascular endothelial growth factor (VEGF) and anti-flt-1/VEGF receptor-1 antibodies showed distinct tissue patterns. VEGF immunoreactivity occurred mainly in the processes of the tumor astrocytes in PA; the opposite was observed in AA. flt-1/VEGFR-1 was detected in the tumor cells of AA but not in those of PA. CONCLUSIONS: We propose that the predominance of small, alpha-SMA-negative vessels in AA represents immature, unstable vasculature with a potentially greater susceptibility to anti-angiogenic therapy. The expression of both flt-1 and VEGF by AA tumor cells also suggests a possible autocrine growth-promoting function for VEGF in addition to its role as paracrine pro-angiogenic growth factor for activated ECs, thus making anti-angiogenesis an attractive therapeutic target in the treatment of AA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".