Targeting the immune system with subtype-selective GABAA receptor modulator to alleviate asthma symptoms.
Bibliographic record
Abstract
Abstract Inflammation is one of the main characteristics of asthma in addition to mucus metaplasia and airway hyperresponsiveness due to muscle constriction. Therefore, current asthma treatments include corticosteroids, which are associated with many negative side effects1. Recently, small molecule modulators of the γ-aminobutyric acid receptor (GABAAR) have been shown to reduce inflammation and induce airway smooth muscle relaxation2,3. GABAARs are expressed in the neurons to moderate neuronal firing. However other cell types have distinct arrangements of GABAAR subtypes as well, which can be targeted with subtype-selective GABAAR modulators. Our study hypothesizes that compounds targeting GABAARs bearing the α4 subunit will predominantly target airway smooth muscle cells and immune cells. We have developed two subtype-selective GABAAR modulators, Xhe-III-74EE and Xhe-III-74A. Investigations with T cells showed reduced IL-2 levels for treated cells. In addition, both compounds reduced cellular rapid cytoplasmic calcium release upon activation. In vivo studies showed that XHE-III-74A was able to reduce eosinophilia in an ovalbumin sensitized and challenged (Ova S/C) model.
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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 | 0.000 | 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".