R-roscovitine Reduces Lung Inflammation Induced by Lipoteichoic Acid and Streptococcus pneumoniae
Bibliographic record
Abstract
Bacterial pneumonia remains associated with high morbidity and mortality. The gram-positive pathogen Streptococcus pneumoniae is the most common cause of community-acquired pneumonia. Lipoteichoic acid (LTA) is an important proinflammatory component of the gram-positive bacterial cell wall. R-roscovitine, a purine analog, is a potent cyclin-dependent kinase (CDK)-1, -2, -5 and -7 inhibitor that has the ability to inhibit the cell cycle and to induce polymorphonuclear cell (PMN) apoptosis. We sought to investigate the effect of R-roscovitine on LTA-induced activation of cell lines with relevance for lung inflammation in vitro and on lung inflammation elicited by either LTA or viable S. pneumoniae in vivo. In vitro R-roscovitine enhanced apoptosis in PMNs and reduced tumor necrosis factor (TNF)-α and keratinocyte chemoattractant (KC) production in MH-S (alveolar macrophage) and MLE-12/MLE-15 (respiratory epithelial) cell lines. In vivo R-roscovitine treatment reduced PMN numbers in bronchoalveolar lavage fluid during LTA-induced lung inflammation; this effect was reversed by inhibiting apoptosis. Postponed treatment with R-roscovitine (24 and 72 h) diminished PMN numbers in lung tissue during gram-positive pneumonia; this step was associated with a transient increase in pulmonary bacterial loads. R-roscovitine inhibits proinflammatory responses induced by the gram-positive stimuli LTA and S. pneumoniae. R-roscovitine reduces PMN numbers in lungs upon LTA administration by enhancing apoptosis. The reduction in PMN numbers caused by R-roscovitine during S. pneumoniae pneumonia may hamper antibacterial defense.
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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".