KIF2A decreases IL-33 production and attenuates allergic asthmatic inflammation
Bibliographic record
Abstract
BACKGROUND: The microtubule-dependent molecular motor protein Kinesin Family Member 2A (KIF2A) is down-regulated in asthmatic human airway epithelium. However, little is known about the roles of KIF2A as well as the possible underlying mechanisms in asthma. METHODS: House dust mite (HDM) extract was administered to establish a murine model of asthma. The expression of KIF2A, IL-33 and the autophagy pathways were detected. The plasmid pCMV-KIF2A was used to overexpress KIF2A in the airway epithelial cells in vitro and in vivo. IL-4, IL-5, IL-33 and other cytokines in bronchoalveolar lavage fluid (BALF) and lung tissues homogenates were measured. RESULTS: In response to the challenge of house dust mite (HDM) in vitro and in vivo, airway epithelial cells displayed decreased production of KIF2A. Meanwhile, autophagy and IL-33 were increased in HMD-treated epithelial cells. Mechanistically, KIF2A decreased autophagy via suppressing mTORC1 pathway in HDM-treated epithelial cells, which contributed to the reduced production of IL-33. Moreover, in vivo KIF2A transfection reduced IL-33 and autophagy in the lung, leading to the attenuation of allergic asthma. CONCLUSION: KIF2A suppressed mTORC1-mediated autophagy and decreased the production of epithelial-derived cytokine IL-33 in allergic airway inflammation. These data indicate that KIF2A may be a novel target in allergic 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) | 0.001 | 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 | 0.001 |
| 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".