Do Statins have a Role in Reduction/Prevention of Post‐PCI Restenosis?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The pathophysiology of post-PCI restenosis involves neointimal formation that consists of three phases: thrombosis (within 24 h), recruitment (3-8 days), and proliferation, which starts on day 8 of PCI. Various factors suggested to be predictors/risks for restenosis include C-reactive protein (CRP), inflammatory mediators (cytokines and adhesion molecules), oxygen radicals, advanced glycation end products (AGEs) and their receptors (RAGE), and soluble RAGE (sRAGE). The earlier noted factors produce thrombogenesis, vascular smooth muscle cell proliferation, and extracellular matrix formation. Statins have pleiotropic effects. Besides lowering serum cholesterol, they have various other biological effects including antiinflammatory, antithrombotic, CRP-lowering, antioxidant, antimitotic, and inhibition of smooth muscle cell proliferation. They inhibit matrix metalloproteinase and cyclooxygenase-2, lower AGEs, decrease expression of RAGE and increase levels of serum sRAGE. They also increase the synthesis of nitric oxide (NO) by increasing endothelial NO synthase expression and activity. Preprocedural statin therapy is known to reduce peri- and post-PCI myonecrosis and reduce the need for repeat revascularization. There is evidence that statin-eluting stents inhibit in-stent restenosis in animal models. It is concluded that because of the above attributes of statins, they are suitable candidates for reduction of post-PCI restenosis and post-PCI myonecrosis. The future directions for the use of statins in reduction of post-PCI restenosis and myonecrosis have been discussed.
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.
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) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it