A passenger strand variant in miR-196a2 contributes to asthma severity in children and adolescents: A preliminary study
Bibliographic record
Abstract
There is emerging evidence to support the role of microRNAs in allergic airway diseases and inflammation. Genetic variants in microRNA genes might affect microRNA-mediated cell regulation. This preliminary study was designed to investigate the association of the microRNA-196a2 rs11614913 (C/T) polymorphism with susceptibility to asthma and clinical outcomes in children and adolescents. Genotyping of rs11614913 polymorphism was determined in 96 patients with bronchial asthma (6-18 years of age) and 96 unrelated controls, using real-time polymerase chain reaction technology. In-silico target prediction and network core analyses were performed. The asthmatics did not show significant differences in genotype distribution (p = 0.609) and allele frequencies (p = 0.428) compared with the controls. There were also no associations with disease duration, age at onset, asthma phenotype, asthma control, therapeutic level, airway hyper-responsiveness, or biochemical parameters in the blood. However, the CC genotype was associated with a more severe degree of asthma (p = 0. 023) and higher frequency of nocturnal asthma (p = 0.002). Carriers for CC were 17 times more likely to develop nocturnal asthma, and had a more than 2.5-fold increased risk for poor disease outcome compared with CT and TT individuals. In conclusion, microRNA-196a2 rs11614913 polymorphism might be associated with asthma severity in our sample of the Egyptian population. Further investigations in studies with a larger sample size and functional tests are needed to validate our findings and to explore the detailed biological mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".