CEMIP regulates the proliferation and migration of vascular smooth muscle cells in atherosclerosis through the WNT–beta-catenin signaling pathway
Bibliographic record
Abstract
In this study we investigated the regulatory role of cell-migration-inducing and hyaluronan-binding protein (CEMIP) in the proliferation and migration of vascular smooth muscle cells (VSMCs). The mRNA and protein levels of CEMIP were upregulated in the plasma samples from patients with atherosclerosis, and in VSMCs stimulated with platelet-derived growth factor-BB (PDGF-BB), compared with plasma from healthy subjects and untreated VSMCs. Silencing CEMIP suppressed PDGF-BB-induced cell migration and proliferation in VSMCs, as determined using a Cell Counting Kit-8 assays, 5-ethynyl-2′-deocyuridine (EDU) assays, flow cytometry, wound healing assays, and Transwell assays. Overexpression of CEMIP promoted the proliferation and migration of VSMCs via activation of the Wnt–β-catenin signaling pathway and the upregulation of its target genes, including matrix metalloproteinase-2, matrix metalloproteinase-7, cyclin D1, and c-myc, whereas CEMIP deficiency showed the opposite effects. The knockdown of CEMIP in ApoE−/− mice by intravenous injection of lentiviral vector expressing si-CEMIP protected against high-fat-diet-induced atherosclerosis, as shown by the reduced aortic lesion areas, aortic sinus lesion areas, and the concentration of blood lipids compared with mice normally expressing CEMIP. These results demonstrated that CEMIP regulates the proliferation and migration of VSMCs in atherosclerosis by activating the WNT–β-catenin signaling pathway, which suggests the therapeutic potential of CEMIP for the management of atherosclerosis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".