Modulation of low-dose ozone and LPS exposed acute mouse lung inflammation by IF1 mediated ATP hydrolysis inhibitor, BTB06584
Bibliographic record
Abstract
Ozone and bacterial lipopolysaccharide (LPS) are common air pollutants that are related to high hospital admissions due to airway hyperreactivity and increased susceptibility to infections, especially in children, older population and individuals with underlying conditions. We modeled acute lung inflammation (ALI) by exposing 6-8 week old male mice to 0.005 ppm ozone for 2 h followed by 50 μg of intranasal LPS. We compared the immunomodulatory effects of single dose pre-treatment with CD61 blocking antibody (clone 2C9.G2), ATPase inhibitor BTB06584 against propranolol as the immune-stimulant and dexamethasone as the immune-suppressant in the ALI model. Ozone and LPS exposure induced lung neutrophil and eosinophil recruitment as measured by respective peroxidase (MPO and EPX) assays, systemic leukopenia, increased levels of lung vascular neutrophil regulatory chemokines such as CXCL5, SDF-1, CXCL13 and a decrease in immune-regulatory chemokines such as BAL IL-10 and CCL27. While CD61 blocking antibody and BTB06584 produced maximum increase in BAL leukocyte counts, protein content and BAL chemokines, these treatments induced moderate increase in lung MPO and EPX content. CD61 blocking antibody induced maximal BAL cell death, a markedly punctate distribution of NK1.1, CX3CR1, CD61. BTB06584 preserved BAL cell viability with cytosolic and membrane distribution of Gr1 and CX3CR1. Propranolol attenuated BAL protein, protected against BAL cell death, induced polarized distribution of NK1.1, CX3CR1 and CD61 but presented with high lung EPX. Dexamethasone induced sparse cell membrane distribution of CX3CR1 and CD61 on BAL cells and displayed very low lung MPO and EPX levels despite highest levels of BAL chemokines. Our study unravels ATPase inhibitor IF1 as a novel drug target for lung injury.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".