Hypercholesterolemia Induces a Mast Cell–CD4+ T Cell Interaction in Atherosclerosis
Bibliographic record
Abstract
Abstract Mast cells (MCs) are potent innate immune cells that aggravate atherosclerosis through the release of proinflammatory mediators inside atherosclerotic plaques. Similarly, CD4+ T cells are constituents of the adaptive immune response and accumulate within the plaques following lipid-specific activation by APCs. Recently it has been proposed that these two cell types can interact in a direct manner. However, no indication of such an interaction has been investigated in the context of atherosclerosis. In our study, we aimed to examine whether MCs can act as APCs in atherosclerosis, thereby modulating CD4+ T cell responses. We observed that MCs increased their MHC class II expression under hyperlipidemic conditions both in vivo and in vitro. Furthermore, we showed that MCs can present Ags in vivo via MHC class II molecules. Serum from high-fat diet–fed mice also enhanced the expression of the costimulatory molecule CD86 on cultured MCs, whereas OVA peptide–loaded MCs increased OT-II CD4+ T cell proliferation in vitro. The aortic CD4+ and TH1 cell content of atherosclerotic mice that lack MCs was reduced as compared with their wild-type counterparts. Importantly, we identified MCs that express HLA-DR in advanced human atheromata, indicating that these cells are capable of Ag presentation within human atherosclerotic plaques. Therefore, in this artice, we show that MCs may directly modulate adaptive immunity by acting as APCs in 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) | 0.002 | 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".