<scp>NOD1</scp> promotes leukocyte clearance and limits inflammation in female mice during obesity‐associated acute lung injury
Bibliographic record
Abstract
Abstract Obesity is associated with metabolic inflammation, which includes changes to innate immune responses relevant to acute lung injury. NOD1 is a cytosolic pattern recognition receptor involved in sensing bacterial peptidoglycan and has been linked to metabolic inflammation. However, its role in obesity‐associated acute lung injury, especially in females, remains unclear. Here, we investigated the impact of NOD1 deficiency on pulmonary inflammation in female mice subjected to a high‐fat diet and lipopolysaccharide‐induced acute lung injury. Compared to wild‐type controls, obese Nod1−/− mice showed reduced leukocyte and neutrophil numbers in the bronchoalveolar lavage (BAL), but increased BAL levels of TNF‐α, IL‐1β, IL‐6, IL‐17A, and IL‐22, suggesting impaired neutrophil clearance. In the lung tissue, NOD1 deficiency during obesity led to elevated neutrophil accumulation, increased myeloperoxidase activity, reduced CD163+ macrophages, and enhanced β‐galactosidase activity. Gene expression analysis revealed upregulation of chemokines, adhesion molecules, and inflammasome components, alongside downregulation of M2 polarization markers. Additionally, obese Nod1−/− mice showed higher NF‐κB and ERK1/2 activation and lower p38 phosphorylation. These findings indicate that NOD1 regulates leukocyte dynamics, inflammation, and macrophage function in the obese lung. We identify NOD1 as a key protective modulator of pulmonary immune responses during acute lung injury under metabolic stress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".