Effects of everolimus and HLA-G on cellular proliferation and neutrophil adhesion in an in vitro model of cardiac allograft vasculopathy
Bibliographic record
Abstract
Human leukocyte antigen-G (HLA-G) expression is modulated by immunosuppressant use and is associated with lower incidence of graft rejection and cardiac allograft vasculopathy (CAV). We examined whether everolimus induces HLA-G expression and inhibits human coronary artery smooth muscle cell (HCASMC) proliferation, a critical event in CAV. Also, we examined whether TNFα-stimulated neutrophil adhesion is inhibited by HLA-G on human coronary artery endothelial cells (HCAECs). HLA-G expression in HCASMCs following everolimus treatment was determined by western-blot densitometric analysis. HCASMCs proliferation following incubation with recombinant HLA-G was determined by automated cell counter detecting 2-10 µm particles. Assessment of recombinant HLA-G on neutrophil adhesion to HCAECs in response to TNF-α induced-injury was determined by nonstatic adhesion assays. HLA-G expression was upregulated in HCASMCs following everolimus exposure (1000 ng/ml; P < .05). HLA-G (500, 1000 ng/ml; both P < .05) reduced HCASMC proliferation and inhibited TNFα-stimulated neutrophil adhesion to endothelial cells at all concentrations (0.1-1 ng/ml; all P < .001). Our study reveals novel regulation of HLA-G by everolimus, by demonstrating HLA-G upregulation and subsequent inhibition of HCASMC proliferation. HLA-G is a potent inhibitor of neutrophil adhesion to HCAECs. Findings support HLA-G's importance and potential use in heart transplantation for preventative therapy or as a marker to identify patients at high risk for developing CAV.
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".