GFI1 KO can develop allergic reaction independently from IL5 and ILC2 cells
Bibliographic record
Abstract
Abstract The number of people living with a chronic inflammatory lung disease, such as asthma, is constantly increasing. The development of resistance to existing therapies and the fact that exposure to air pollution and particular matter are continuously increasing have made asthma a major health problem worldwide. We have observed that in mice lacking the transcriptional factor GFI1 (short for “Growth factor independent 1”) where ILC2 are not functional and IL5 is almost absent, a severe asthmatic inflammatory reaction can still be provoked with increased numbers of eosinophils in the airways. This finding strongly suggests that pathways other than those initiated by IL5/IL5R and ILC2 cells exist that play a major role in severe allergic asthma. We hypothesize that the situation in GFI1 KO mice reflects what it is observed in asthmatic patients that are resistant to the recent therapies. We further posit that GFI1 KO mice is a tool to discover new pathways involved in asthmatic reactions different from the known and established mechanisms involving IL5. We are using this unique mouse model (Gfi1 deficient mice) and high throughput genomic sequencing technologies with a specific bioinformatics approach to identify new molecules pathways mediating the inflammatory reaction in asthma. We expect to reveal new molecular signaling pathways that cause lung inflammation during an allergic asthma reaction, specifically those regulating eosinophil recruitment and activities. Our first results show that GFI1 KO seem to be able to support an allergic reaction though IL17 pathway and Th17 cells. We anticipate to identify of molecular targets, which could be used to develop new, more effective therapies for patients with severe 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".