The Sirt1 Activator SRT1720 Mitigates Human Monocyte Activation and Improves Outcome During Gram-Negative Pneumosepsis in Mice
Bibliographic record
Abstract
Community-acquired pneumonia (CAP) is a leading cause of death, with mortality linked to an unbalanced host response. Sirtuin (Sirt)1, a histone deacetylase, regulating metabolism and epigenetics, may be fundamental in activating the innate immune response. Sirt1 mRNA expression was significantly reduced in monocytes from CAP patients (n = 76) upon admission compared to healthy controls (n = 42), with levels returning to normal after 30 days. Pharmacological activation of Sirt1 with SRT1720 decreased LPS- and K. pneumoniae-induced IL-6 release in primary human monocytes and decreased NF-κB activation in THP1 cells. In a mouse K. pneumoniae pneumosepsis model, SRT1720 strongly reduced neutrophil influx and degranulation markers in bronchoalveolar lavage fluid, lowered pulmonary concentrations of IL-6 and TNF-α, and reduced lung pathology scores. Simultaneously, it reduced neutrophil content in liver tissue and plasma transaminase levels, alongside a trend toward reduced liver necrosis. Plasma IL-6 and TNF-α were significantly lower in SRT1720-treated mice at 42 h. Finally, while SRT1720 did not impact bacterial loads in the lungs, it reduced bacterial burden in blood, with a similar trend observed in liver homogenates. In conclusion, the Sirt1 activator SRT1720 exerts anti-inflammatory effects on human monocytes, reduces local and systemic inflammation and organ injury, and diminishes bacterial dissemination in murine pneumosepsis.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".