Differential expression of MMP-2, MMP-9 and TIMP proteins in thoracic aortic aneurysm – comparison with and without bicuspid aortic valve: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is believed that the balance of matrix metalloproteinases (MMPs) and the tissue inhibitors of metalloproteinases (TIMPs), in the aorta, play a critical role in aneurysm formation. The objective of this study was to perform a meta-analysis of studies reporting protein expression of MMPs and TIMPs in the ascending aorta of thoracic aortic aneurysms (TAA) cases and to examine this expression in persons with TAA and bicuspid aortic valves (BAV). METHODS: OvidSP Medline and EMbase were systematically searched for studies that were: human ascending TAA cases with measurement of MMP or TIMP protein expression in the aorta and a control group. A similar search was conducted for BAV compared to those with a normal or trileaflet aortic valve (TAV). RESULTS: Eight studies fulfilled the inclusion criteria. There was a significant increase in MMP-9 and no change in MMP-2, in the aorta from persons with TAA (N = 106) compared to control (N = 30). There was also a highly significant reduction in TIMP-1 and TIMP-2 in TAA (N = 93) compared to control (N = 24) resulting in a MMP-9 to TIMP-1 or TIMP-2 ratio over 3.5 fold greater than controls. There was a highly significant increase in MMP-2 but not MMP-9 in TAA with BAV (N = 112) compared to TAV (N = 53). There was a significant reduction for TIMP-1 in BAV compared to TAV but no change in TIMP-2, TIMP-3 or TIMP-4. CONCLUSIONS: These data suggest that MMP may be implicated in the pathogenesis of TAA and there is a differential expression with MMP-9 increased and TIMP-1 and -2 reduced in the most common forms of TAA. MMP-2 is increased and only TIMP-1 decreased in TAA with BAV compared to TAV.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".