Bacteria Challenge in Smoke-exposed Mice Exacerbates Inflammation and Skews the Inflammatory Profile
Bibliographic record
Abstract
RATIONALE: The pathogenesis of chronic obstructive pulmonary disease is associated with acute episodes of bacterial exacerbations. The most commonly isolated bacteria during episodes of exacerbation is nontypeable Haemophilus influenzae (NTHI). OBJECTIVES: In this study, we investigated the in vivo consequences of cigarette smoke exposure on the inflammatory response to an NTHI challenge. METHODS: C57BL/6 and BALB/c mice were exposed to cigarette smoke for 8 weeks and subsequently challenged intranasally with NTHI. MEASUREMENTS AND MAIN RESULTS: We observed increased pulmonary inflammation and lung damage in cigarette smoke-exposed NTHI-challenged mice as compared with control NTHI-challenged mice. Furthermore, although NTHI challenge in control mice was marked by increases in tumor necrosis factor-alpha, IL-6, MIP-2, and KC/GROalpha, NTHI challenge in cigarette smoke-exposed mice led to a prominent up-regulation of a different subset of inflammatory mediators, most notably MCP-1, -3, and -5, IP-10, and MIP-1gamma. This skewed inflammatory mediator expression was also observed after ex vivo NTHI stimulation of alveolar macrophages, signifying their importance to this altered response. Importantly, corticosteroids attenuated inflammation after NTHI challenge in both cigarette smoke-exposed and control mice; however, this was associated with significantly increased bacterial burden. CONCLUSIONS: Collectively, these data suggest that cigarette smoke exacerbates the inflammatory response to a bacterial challenge via skewed inflammatory mediator expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".