Isolation of a Natural Inhibitor of Human Malignant Glial Cell Invasion: Inter α-Trypsin Inhibitor Heavy Chain 2
Bibliographic record
Abstract
Malignant central nervous system (CNS) tumors, such as glioblastoma multiforme, invade the brain and disrupt normal tissue architecture, making complete surgical removal virtually impossible. Here, we have developed and optimized a purification strategy to isolate and identify natural inhibitors of glioma cell invasion in a three-dimensional collagen type I matrix. Inter alpha-trypsin inhibitor heavy chain 2 (ITI H2) was identified from the most inhibitory fractions and its presence was confirmed both as a single protein and in a bikunin-bound form. Stable overexpression in U251 glioma cells validated ITI H2's strong inhibition of human glioma cell invasion together with significant inhibition of cell proliferation and promotion of cell-cell adhesion. Analysis of primary human brain tumors showed significantly higher levels of ITI H2 in normal brain and low-grade tumors compared with high-grade gliomas, indicating an inverse correlation with malignancy. The phosphatidylinositol 3-kinase/Akt signaling cascade seemed to be one of the pathways involved in the effect of ITI H2 on U251 cells. These findings suggest that reduction of ITI H2 expression correlates with brain tumor progression and that targeting factors responsible for its loss or restoring the ITI supply exogenously may serve as potential therapeutic strategies for a variety of CNS tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".