Increased Neovascularization in Mice Lacking Tissue Inhibitor of Metalloproteinases-3
Bibliographic record
Abstract
PURPOSE: Tissue inhibitor of metalloproteinases-3 (TIMP-3) is a matrix-bound inhibitor of matrix metalloproteinases (MMPs). The authors have previously determined a novel function of TIMP-3 to inhibit vascular endothelial growth factor (VEGF)-mediated angiogenesis. Here, the authors examined the in vivo angiogenic phenotype of ocular vessels in mice deficient in TIMP-3. METHODS: VEGF-mediated corneal neovascularization and laser-induced choroidal neovascularization (CNV) were examined in TIMP-3-null mice. The effects of the absence of TIMP-3 on the phosphorylation status of the VEGF-receptor-2 (VEGFR-2) and the downstream signaling pathways were evaluated biochemically. In addition, the activation state of MMPs in the retina of TIMP-3-deficient mice was examined by in situ zymography. RESULTS: The results of these studies determine an accentuation of pathologic VEGF-mediated angiogenesis in the cornea and laser-induced CNV in mice lacking TIMP-3. In the absence of the MMP inhibitor, pathophysiological changes were observed in the choroidal vasculature concomitantly with an increase in gelatinolytic activity. These results suggest that an imbalance of extracellular matrix homeostasis, together with a loss of an angiogenesis inhibitor, can prime vascular beds to be more responsive to an angiogenic stimulus. CONCLUSIONS: In light of the recent studies suggesting that genetic variants near TIMP-3 influence susceptibility to age-related macular degeneration, these results imply that TIMP-3 may regulate the development of the choroidal vasculature and is a likely contributor to increased susceptibility to choroidal neovascularization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".