Chronic ethanol exposure impairs alveolar leukocyte infiltration during pneumococcal pneumonia, leading to an increased bacterial burden despite increased CXCL1 and nitric oxide levels
Bibliographic record
Abstract
Ethanol abuse is a risk factor for the development of pneumonia caused by Streptococcus pneumoniae, a critical pathogen for public health. The aim of this article was to investigate the inflammatory mechanisms involved in pneumococcal pneumonia that may be associated with chronic ethanol exposure. Male C57BL6/J-Unib mice were exposed to 20% (v/v) ethanol for twelve weeks and intranasally infected with 5x104 CFU of S. pneumoniae. Twenty-four hours after infection, lungs, bronchoalveolar lavage and blood samples were obtained to assess the consequences of chronic ethanol exposure during infection. Alcohol-fed mice showed increased production of nitric oxide and CXCL1 in alveoli and plasma during pneumococcal pneumonia. Beside this, ethanol-treated mice exhibited a decrease in leukocyte infiltration into the alveoli and reduced frequency of severe lung inflammation, which was associated with an increase in bacterial load. Curiously, no changes were observed in survival after infection. Taken together, these results demonstrate that chronic ethanol exposure alters the inflammatory response during S. pneumoniae lung infection in mice with a reduction in the inflammatory infiltrate even in the presence of higher levels of the chemoattractant CXCL1.
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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) | 0.001 | 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".