CD27 co-stimulation increases regulatory T cell survival and limits progression of atherosclerosis.
Bibliographic record
Abstract
Abstract Co-stimulatory signals are important shaping adaptive immunity which is pivotal to all stages of atherosclerosis. The interaction of the co-stimulatory molecules CD27 and CD70 modulates Treg development. Furthermore, T cell activation, proliferation, and also differentiation are affected by CD27-CD70 interactions. Thus, we hypothesized that the deficiency of CD27 will lead to exacerbated atherosclerosis. Cd27−/− mice were crossed with Apoe−/− mice. Cd27−/−Apoe−/− and littermate controls (Cd27+/+Apoe−/−) were sacrificed at the age of 18 and 28 weeks. Bone marrow of Cd27−/−Apoe−/− and littermate controls was transplanted into Apoe−/− recipient mice. Lesion size, histology and cellular composition were analyzed in the ascending aorta. Human and murine T cells co-localize with CD27 in atherosclerotic lesions. At early stages of atherosclerosis, Cd27−/−Apoe−/− mice displayed bigger and more complex atherosclerotic lesions. Flow cytometry revealed a significant decrease in abundance of splenic and aortic Tregs and increased apoptosis of Treg in the thymus of Cd27−/−Apoe−/− mice. In contrast, at later stages of atherosclerosis (28 weeks), splenic and lesional Treg content and atherosclerotic burden were similar. Transplantation of Cd27−/−Apoe−/− bone marrow, in contrast to control, induced increased atherosclerotic plaque size and a profound pro-inflammatory plaque phenotype accompanied by reduced frequency of aortic and splenic Tregs. Furthermore, the number of circulating leukocytes was increased. Taken together, CD27 deficiency impairs thymic Treg development which exacerbates early atherogenesis. However, the role of CD27–CD70 interactions does not affect later stages of atherosclerosis.
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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) | 0.003 | 0.001 |
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".