Impacts of peroxisome proliferator-activated receptor-γ activation on cigarette smoke-induced exacerbated response to bacteria
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is characterised by a state of chronic pulmonary inflammation punctuated by microbial exacerbations. Despite advances in treatment options, COPD remains difficult to manage. In this study, we investigated the potential of peroxisome proliferator-activated receptor (PPAR)γ activation as a new therapy against cigarette smoke-induced inflammation and its associated bacterial exacerbation. C57BL/6 mice were exposed to room air or cigarette smoke for either 4 days or 4 weeks and treated either prophylactically or therapeutically with rosiglitazone. The impact of rosiglitazone on cigarette smoke-induced exacerbated response to the bacterial pathogen nontypeable Haemophilus influenzae (NTHi) was studied using the therapeutic treatment protocol. We found that rosiglitazone was able to reduce cigarette smoke-induced neutrophilia both when administered prophylactically or therapeutically. Therapeutic intervention with rosiglitazone was also effective in preventing cigarette smoke-induced neutrophilia exacerbation following NTHi infection. Moreover, the anti-inflammatory effects of rosiglitazone did not lead to an increase in the pulmonary bacterial burden, unlike dexamethasone. Altogether, our data suggest that pharmacological activation of PPARγ may be an effective therapeutic approach to improve COPD management, as it is able to reduce cigarette smoke-induced inflammation and decrease the magnitude of bacterial exacerbations, without compromising the ability of the immune system to control the infection.
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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".