LncRNA-AK007111 affects airway inflammation in asthma via the regulation of mast cell function
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) are involved in gene transcription and pathophysiological processes of human diseases. Multiple lncRNAs have been shown to play important roles in the occurrence and development of asthma. This study aimed to explore the role of a novel lncRNA, lncRNA-AK007111, in asthma. Overexpression of lncRNA-AK007111 was induced in a mouse model of asthma via viral transfection, followed by the collection of alveolar lavage fluid and lung tissue for the detection of relevant inflammatory factors and pathological analysis of lung sections. Pulmonary resistance and respiratory dynamic compliance were measured using an animal pulmonary function analyzer. The number of mast cells sensitized by immunofluorescence was detected at the cellular level. The degree of degranulation of lncRNA-AK007111 after its knockdown was determined by detecting the level of β-hexosaminidase that was released and quantifying IL-6 and TNF-α using ELISA in a model of RBL-2H3 cells activated by immunoglobulin E plus antigen. Finally, we observed the migration ability of mast cells under a microscope. The results showed that in ovalbumin-sensitized mice, the upregulation of lncRNA-AK007111 promoted the infiltration of inflammatory cells in lung tissue, increased the number of total cells, eosinophils, and mast cells, upregulated IL-5 and IL-6 levels, and increased airway hyper-reactivity. Downregulation of lncRNA-AK007111 decreased the degranulation ability of IgE/Ag-activated mast cells and inhibited the expression of IL-6 and TNF-α; moreover, the migration ability of mast cells was significantly weakened. In conclusion, our study revealed that lncRNA-AK007111 plays an important role in asthma by modulating mast cell-related functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".