Bibliographic record
Abstract
A asthma is a chronic inflammatory disease characterized by an airway hyper-responsiveness and a deregulated inflammation in response to allergens. Available treatments are mainly symptoms-driven and do not interfere with the natural history of the diseases. Severe asthma constitutes a challenging problem for the healthcare system. Its heterogeneity complicates the management of the disease. There is a significant need to understand the pathogenesis of severe asthma. Bronchial epithelium is considered a key player in coordinating airway wall remodeling. While in mild asthma, the epithelium is damaged and fails to proliferate and to repair in severe asthma the epithelium was reported to be highly proliferative and thicker. This may be due to different regulatory mechanisms. We studied microRNAs profile and evaluated their role in regulating proliferation of bronchial epithelial cells obtained from severe asthmatic subjects in comparison to cells obtained from mild asthmatics and healthy controls. We found that in mild asthma epithelial cells produce high amount of TGFβ1 and express high level of TGFβ-RI and phosphorylated-Smad3 indicating that TGFβ1signalling is up-regulated. In severe asthma, this pathway was down-regulated. Thus, in epithelial cells from severe asthmatics compared to mild asthma and controls, miR19a, a member of the miR-17~92 cluster is up-regulated and increases proliferation. Knockdown of miR-19a in epithelial cells reduces significantly their proliferation through targeting TGF-β downstream signaling. Our study uncovers a new regulatory pathway involving miR-19a that is critical to the severe phenotype of asthma and indicates that down-regulating miR-19a expression could be explored as a potential new therapy to modulate the epithelium repair in asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".