Involvement of lymphocytes in asthma and allergic diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: The activation and regulation of lymphocytes play a central role in asthmatic inflammation. It is increasingly recognized that diverse panels of lymphocyte lineages and cytokine profiles are involved in the asthmatic phenotypes. In this review, we discuss the advances in the gene variants associated with the regulation of lymphocytes and relevant cytokines underlying asthma and allergic diseases. We also discuss the current evidence about the epigenetic regulation of lymphocyte differentiation and the interaction with environment. RECENT FINDINGS: Many genetic variants in asthma are functionally associated with lymphocytes and relevant cytokines. Interleukin (IL)-2RB is important in the homeostasis of T regulatory cells (Tregs) through effects from IL-2. IL-18R1 and ST2/IL-1RL1 drive the T helper 1 and 2 inflammation via the ligands of their encoding receptors. Novel genes, like orosomucoid 1-like 3/gasdermin-like gene and taste receptor type 2 members are being explored for their roles in T-cell activation. T-cell lineages are epigenetically regulated by de novo methyltransferases, histone methylase, CD44 and microRNA. Environmental factors such as second-hand smoke and ambient air pollution modify Tregs differentiation significantly. SUMMARY: Plenty of genetic loci of lymphocyte regulation provide us a deeper insight into the asthma pathogenesis. Future challenge is to define genetic drivers in asthma phenotypes to provide therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".