Circulating CD4 <sup>+</sup> CD25 <sup>hi</sup> CD127 <sup>lo</sup> Regulatory T-Cell Levels Do Not Reflect the Extent or Severity of Carotid and Coronary Atherosclerosis
Bibliographic record
Abstract
OBJECTIVE: Regulatory T (Treg) cells play a protective role in experimental atherosclerosis. In the present study, we investigated whether the levels of circulating Treg cells relate to the degree of atherosclerosis in carotid and coronary arteries. METHODS AND RESULTS: We studied 2 distinct populations: (1) 113 subjects, selected from a free-living population (carotid study), in which we measured the intima-media thickness of the common carotid artery, as a surrogate marker of initial atherosclerosis; and (2) 75 controls and 125 patients with coronary artery disease (coronary study): 36 with chronic stable angina, 50 with non-ST-elevation acute coronary syndrome, 39 with ST-elevation acute myocardial infarction. Treg-cell levels were evaluated by flow cytometry (Treg cells identified as CD3(+)CD4(+)CD25(high)CD127(low)) and by mRNA expression of forkhead box P3 or of Treg-associated cytokine interleukin 10. In the carotid study, no correlation was observed between Treg-cell levels and intima-media thickness. No differences in Treg-cell levels were observed comparing rapid versus slow intima-media thickness progressors from a subgroup of patients (n=65), in which prospective data on 6-year intima-media thickness progression were available. In the coronary group, Treg-cell levels were not altered in chronic stable angina patients. In contrast, nonunivocal variations were observed in patients suffering an acute coronary syndrome (with a Treg-cell increase in ST-elevation acute myocardial infarction and a Treg-cell decrease in non-ST-elevation acute coronary syndrome patients). CONCLUSIONS: The results suggest that determination of circulating Treg-cell levels based on flow cytometry or mRNA assessment is not a useful indicator of the extent or severity 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 | 0.001 |
| 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) | 0.003 | 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".