LncRNAs ENST00000499459 and TCONS_00004989 enhance asthma progression in children with house dust mite-induced allergic asthma
Bibliographic record
Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) have been extensively reported to play critical roles in the pathogenesis of various disease, especially in cancer. However, little is known about the role of lncRNAs in the pathogenesis of pediatric allergic asthma. METHODS: High-throughput sequencing analysis was performed to identify differentially expressed mRNAs and lncRNAs in peripheral blood mononuclear cells (PBMCs) from 3 children with allergic asthma and 3 matched healthy controls. Bioinformatics analysis was used to select candidate lncRNAs and mRNAs that may be involved in the pathogenesis of asthma. Candidate lncRNAs were validated in a larger size of asthma patients and healthy controls. Finally, lncRNAs and molecular pathways associated with the pathogenesis of allergic asthma were identified by competing endogenous RNA (ceRNA) analysis. RESULTS: Five differentially expressed lncRNAs were identified after high-throughput sequencing and verified by real-time PCR. LncRNAs ENST0000631797, TCONS_00004989 and ENST00000499459 were verified to be differentially expressed in allergic asthma. Besides, ENST00000499459/DIXDC1 axis was identified to play a crucial role in allergic asthma after comprehensive ceRNA network analysis. CONCLUSION: ENST00000499459 and TCONS_00004989 are potential biomarkers for house dust mite-induced allergic asthma.
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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 | 0.001 |
| 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".