Visualizing Lung Cellular Adaptations during Combined Ozone and LPS Induced Murine Acute Lung Injury
Bibliographic record
Abstract
Lungs are continually faced with direct and indirect insults in the form of sterile (particles or reactive toxins) and infectious (bacterial, viral or fungal) inflammatory conditions. An overwhelming host response may result in compromised respiration and acute lung injury, which is characterized by lung neutrophil recruitment as a result of the patho-logical host immune, coagulative and tissue remodeling response. Sensitive microscopic methods to visualize and quantify murine lung cellular adaptations, in response to low-dose (0.05 ppm) ozone, a potent environmental pollutant in combination with bacterial lipopolysaccharide, a TLR4 agonist, are crucial in order to understand the host inflammatory and repair mechanisms. We describe a comprehensive fluorescent microscopic analysis of various lung and systemic body compartments, namely the broncho-alveolar lavage fluid, lung vascular perfusate, left lung cryosections, and sternal bone marrow perfusate. We show damage of alveolar macrophages, neutrophils, lung parenchymal tissue, as well as bone marrow cells in correlation with a delayed (up to 36-72 h) immune response that is marked by discrete chemokine gradients in the analyzed compartments. In addition, we present lung extracellular matrix and cellular cytoskeletal interactions (actin, tubulin), mitochondrial and reactive oxygen species, anti-coagulative plasminogen, its anti-angiogenic peptide fragment angiostatin, the mitochondrial ATP synthase complex V subunits, α and β. These surrogate markers, when supplemented with adequate in vitro cell-based assays and in vivo animal imaging techniques such as intravital microscopy, can provide vital information towards understanding the lung response to novel immunomodulatory agents.
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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".