Emerging monoclonal antibodies as targeted innovative therapeutic approaches to asthma
Bibliographic record
Abstract
Asthma is characterized by discordant responses among cells of the adaptive and innate immune systems. This interplay involves a complex pattern of cytokine-driven processes resulting in cell migration and recruitment, inflammation, and proliferative states. The significant majority of asthmatic patients respond well to conventional inhaled treatments. However, about 5% of asthmatics have severe refractory asthma and account for 50% of the health expenditure on asthma. Human(ized) monoclonal antibodies (hMabs) targeting inflammatory pathways are promising therapeutic agents in asthma management. The anti-IgE hMab omalizumab was the first biologic treatment approved for the treatment of allergic asthma. Potential future strategies and targets include interleukin (IL)-5, IL-4, and IL-13, anti-TSLP, IL-25, and IL-33. hMabs targeting IL-5 have shown great promise in severe refractory asthma with a persisting eosinophilia, and clinical trials with hMabs against IL-13 and IL4Rα have also shown clinical benefit. Studies of hMabs against other cytokines in severe asthma are under way.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".