Differential expression and localization of TIMP-1 and TIMP-4 in human gliomas
Bibliographic record
Abstract
Studies have suggested that an imbalance of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) may contribute to the malignant phenotype of gliomas. In this study, we have undertaken a detailed analysis of expression of the TIMP family in normal human brain and malignant gliomas at both the mRNA and protein level. Reverse transcription-PCR (RT-PCR) analyses of total RNA from surgical tumour specimens revealed unique expression patterns for the 4 members of the TIMP family, with TIMP-1 and -4 showing positive and negative correlations, respectively, with glioma malignancy. By RT-PCR, TIMP-2 and TIMP-3 expression did not change with tumour grade. In situ hybridization localized TIMP-1 to glial tumour cells and also to the surrounding tumour vasculature. TIMP-4 transcripts were predominantly localized to tumour cells, though minor expression was found in vessels. Recombinant TIMP-4 reduced invasion of U251 glioma cells through Matrigel, and U87 clones overexpressing TIMP-4 showed reduced invasive capacity in vitro. TIMP-4, but not TIMP-1, blocked Membrane Type-1-MMP-mediated progelatinase-A (MMP-2) activation in human umbilical vein endothelial cells. The differential expression and localization of individual TIMPs may contribute to the pathophysiology of human malignant gliomas, particularly with regard to tumour vascularization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".