Identification of "antigen-specific" neutrophils in atherosclerosis patients that compromise vascular endothelial barrier function.
Bibliographic record
Abstract
The vascular endothelial barrier dysfunction is associated with the pathogenesis of many cardiovascular diseases, such as atherosclerosis (AS). This study aims to identify specific antigen (Ag, in short)-specific polymorphonuclear neutrophils (PMN) in AS patients and to investigate the role of "Ag-specific" PMN activation in causing vascular endothelial barrier dysfunction. In this study, PMNs were isolated from blood samples collected from patients with AS and analyzed with immunological approaches. Human umbilical vein endothelial cells (HUVEC) monolayers were used as a vascular endothelial barrier model. The results showed that "Ag-specific" PMNs were identified in the blood of 50 AS patients. This subset of PMN was featured as the FcγRI and specific IgG (sIgG) complexes on the cell surface; exposure to specific Ags triggered the "Ag-specific" PMNs to release proinflammatory cytokines. PMN-derived cytokine levels in the serum were positively correlated with the serum levels of sIgG in AS patients. Exposure of naive PMNs to sIgG formed FcγRI and sIgG complexes on the surface; this conferred PMNs the property to be recognized and activated by specific Ag. Stimulation of "Ag-specific" PMN activated the mitogen-activated protein kinase and the activities of nuclear factor activated T cells and promoted the gene transcription of tumor necrosis factor-α. Coculture of "Ag-specific" PMNs and HUVEC monolayers in the presence of specific Ag resulted in the HUVEC monolayer barrier dysfunction. In conclusion, "Ag-specific" PMNs were identified in AS patients. Activation of the PMNs compromised vascular endothelial barrier function. Therefore, to regulate the "Ag-specific" PMN's activities may have translational potential in the treatment of AS.
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 | 0.000 |
| 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".