The Acrolein–Lipopolysaccharide Mouse Model for Frequent Exacerbations in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a severe progressive lung disease, often caused by prolonged exposure to cigarette smoke and environmental factors. Preclinical COPD research predominately relies on chronic smoke or elastase animal models, each with their own advantages and limitations, such as limited pathophysiological insights or long treatment times. Here we describe a novel and time-efficient mouse model of COPD based on bacterial LPS and the reactive aldehyde acrolein (Acro). Mice were treated once per week for 4 weeks with a combination of both LPS and Acro. Histological, inflammatory, and metabolomic alterations were analyzed by histological quantification, multicolor flow cytometry, and nuclear magnetic resonance. Acro/LPS treatment induced moderate airspace enlargement and bronchial remodeling. These structural changes were associated with a distinct inflammatory profile marked by an increase in macrophages and T-helper cells, as well as increased cytokines, including CXCL11, IL-17a, and TNF-α. Strong inflammation, consisting of T-helper and B cells, was detected in the perivascular and peribronchial spaces and increased macrophages in the alveolar regions. In addition, intervention with the steroid dexamethasone induced a strong reduction in T cells and macrophages and partially ameliorated histological alterations. Furthermore, we could detect alterations in the metabolome of serum and tissue, including an increase in COPD-associated metabolites like trimethylamine N-oxide, as well as a misbalance in energy-related metabolites and several amino acids. In summary, we can describe a practical, representative, and time-efficient mouse model of COPD, with the potential to study the immunological and pathophysiological development of the disease.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".