Clinical implications of CD4+ T cell subsets in adult atopic asthma patients
Bibliographic record
Abstract
T cells play a central role in chronic inflammation in asthma. However, the roles of individual subsets of T cells in the pathology of asthma in patients remain to be better understood. We investigated the potential signatures of T cell subset phenotypes in asthma using fresh whole blood from adult atopic asthma patients (n = 43) and non-asthmatic control subjects (n = 22). We further assessed their potential clinical implications by correlating asthma severity. We report four major features of CD4 + T cells in the blood of atopic asthma patients. First, patients had a profound increase of CCR7 + memory CD4 + T cells, but not CCR7 − memory CD4 + T cells. Second, an increase in CCR4 + CD4 + T cells in patients was mainly attributed to the increase of CCR7 + memory CD4 + T cells. Accordingly, the frequency of CCR4 + CCR7 + memory CD4 + T cells correlated with asthma severity. Current common asthma therapeutics (including corticosteroids) were not able to affect the frequency of CCR4 + CCR7 + memory CD4 + T cell subsets. Third, patients had an increase of Tregs, as assessed by measuring CD25, Foxp3, IL-10 and CTLA-4 expression. However, asthma severity was inversely correlated only with the frequency of CTLA-4 + CD4 + T cells. Lastly, patients and control subjects have similar frequencies of CD4 + T cells that express CCR5, CCR6, CXCR3, CXCR5, CD11a, or α4 integrin. However, the frequency of α4 + CD4 + T cells in patients correlated with asthma severity. CCR4 + CCR7 + memory, but not CCR4 + CCR7 − memory, α4 + , and CTLA4 + CD4 + T cells in patients show significant clinical implications in atopic asthma. Current common therapeutics cannot alter the frequency of such CD4 + T cell subsets in adult atopic asthma patients.
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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) | 0.002 | 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".