Regulation of MMP-9 gene expression for the development of novel molecular targets against cancer and inflammatory diseases
Bibliographic record
Abstract
The need to pharmacologically control the proteolytic activity of matrix metalloproteinases (MMPs) has been commonly acknowledged, despite its limited efficacy in clinical trials. Among the reasons that explain this failure is our limited understanding of the signals that control the expression of MMPs in different cell types during different pathological conditions. Thus, future therapies must rely on more selective approaches. With the continually increasing body of proof implicating MMPs in a large number of diseases, it has become a priority to establish the pertinence of molecules involved in the signalling pathways leading to the expression of these enzymes. MMP-9 is a case in point: its dramatic overexpression in cancer and various inflammatory conditions clearly points to the molecular mechanisms controlling its expression as a potential target for eventual rational therapeutic intervention. In this article, recent progress in the signalling pathways that regulate MMP-9 expression is reviewed, and the latest strategies to be considered in the search for a specific inhibitor of its expression are presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".