Differential expression of CCR2 and CX3CR1 on CD16+ monocyte subsets is associated with asthma severity
Bibliographic record
Abstract
Abstract Background Monocytes play an important role in immune and inflammatory diseases and monocyte subsets are predictors of disease in certain conditions. Expression of the chemokine receptors, CCR2 and CX3CR1 on monocyte subsets relates to their function and can be used in their characterization. Our objective was to determine whether CD14, CD16, CCR2 and CX3CR1 on monocyte subsets are potential indicators of asthma severity. Methods Blood samples were collected from Saudi Arabian patients with asthma and normal healthy individuals. Six-color flow-cytometry phenotypic analysis was used to identify human blood monocyte subsets, based on their expression of CD14 and CD16 following CD45 gating. Expression of CCR2 and CX3CR1 was analysed on classical (CD14++CD16−), intermediate (CD14++CD16+) and non-classical (CD14+CD16++) subsets and correlated with disease severity. Results We demonstrated a significant increase in percentage of total CD45-positive monocytes in the blood of patients with severe asthma, but the proportion of the individual monocyte subsets was not significantly changed when patients with mild, moderate and severe asthma were compared with healthy individuals. CD16 expression (mean fluorescence intensity, MFI) was decreased on intermediate and non-classical subsets in patients with severe asthma compared to healthy controls. CX3CR1 expression was also lower, with a lower percentage of cells expressing CX3CR1 in the non-classical CD14+CD16++ subset in all patients with asthma and this was inversely related to the percentage of cells expressing CCR2. Conclusions CCR2 expression on monocytes indicated a tendency toward more phagocytic monocytes in patients with asthma. The differential expression of CD16, CX3CR1 and CCR2 on monocyte subsets in peripheral blood indicates modulation of the inflammatory response and suggests a role for monocytes in asthma pathogenesis.
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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 | 0.000 |
| 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".