Recent advances in asthma mucus biology and emerging treatment strategies
Bibliographic record
Abstract
PURPOSE OF REVIEW: To describe the recent advances in the pathobiology and treatment of mucus hypersecretion in asthma, a critical factor contributing to airway obstruction, inflammation, and impaired lung function. RECENT FINDINGS: Significant progress has been made in understanding how mucin protein regulation, mucus viscosity, and adhesion are affected by cytokine-driven inflammation, especially interleukin-13, and defects in ion transport mechanisms. Advances in imaging techniques, such as multidetector computed tomography (MDCT) and hyperpolarized gas MRI, allow for a more precise assessment of mucus plugging and associated ventilation defects. Emerging therapies, including biologicals targeting type-2 (T2) inflammation, and novel mucolytics aimed at modifying mucus properties and secretion, offer promising effects in reducing mucus in severe asthmatics. SUMMARY: The growing understanding of mucus biology and the development of advanced imaging and therapeutic strategies could significantly improve the management of mucus-related complications in asthma. By targeting mucus characteristics, these findings support future approaches to reduce airway obstruction, enhance lung function, and improve clinical outcomes in patients with severe asthma. A deeper understanding of the glycobiology of mucus is critical to develop new therapies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".