Nod1 and Nod2 regulation of pulmonary immunity to Legionella pneumophila (135.77)
Bibliographic record
Abstract
Abstract The role of Nod1 and Nod2 in pulmonary innate immune responses is poorly understood. We hypothesized that Nod1 and Nod2 regulate non-redundant and specific immune responses during murine in vivo infection with Legionella pneumophila (Lp). We first examined whether Nod1 and Nod2 regulate detection of Lp in HEK293 cells transfected with luciferase reporter constructs and either Nod1 or Nod2. Heat-killed Lp stimulated NF-κB and interferon-β-dependent promoter activity in cells transfected with either Nod1 or Nod2. To examine the role of Nod1 and Nod2 during in vivo infection, we exposed Nod1-/- and Nod2-/- and C57Bl/6 control mice to aerosolized Lp. Bronchoalveolar lavage fluid from Nod1-/- animals had decreased neutrophils compared to wild type animals at 4 and 24 hours post-infection. Furthermore, monocyte recruitment was also impaired at 24 hours. In addition Nod1-/- mice had increased lung bacterial loads at 72 hours in comparison to wild type and Nod2-/- mice. In contrast, there was no reproducible difference in cytokines in Nod1-/- lung homogenates at 4 hours and 24 hours. Conversely, Nod2-/- mice had impaired neutrophil cell recruitment at 24 hours, and only a trend to increased organism burden at 72 hours (p=0.07). Nod2-/- animals, however, showed reproducible impairment in cytokine production from lung homogenates. Lung histologic sections at later time points (3-7 days) had no detectable differences in inflammation in Nod1-/- or Nod2-/- mice in comparison to wild type mice. Overall these data indicate that Nod1 regulates early inflammatory cell recruitment of both neutrophils and monocytes to the alveolar space without detectable differences in inflammatory cytokine productions or changes in lung histology at later time points.
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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".