CETP inhibition enhances monocyte activation and bacterial clearance and reduces streptococcus pneumonia–associated mortality in mice
Bibliographic record
Abstract
Sepsis is a leading cause of mortality worldwide, and pneumonia is the most common cause of sepsis in humans. Low levels of high-density lipoprotein cholesterol (HDL-C) levels are associated with an increased risk of death from sepsis, and increasing levels of HDL-C by inhibition of cholesteryl ester transfer protein (CETP) decreases mortality from intraabdominal polymicrobial sepsis in APOE*3-Leiden.CETP mice. Here, we show that treatment with the CETP inhibitor (CETPi) anacetrapib reduced mortality from Streptococcus pneumoniae-induced sepsis in APOE*3-Leiden.CETP and APOA1.CETP mice. Mechanistically, CETP inhibition reduced the host proinflammatory response via attenuation of proinflammatory cytokine transcription and release. This effect was dependent on the presence of HDL, leading to attenuation of immune-mediated organ damage. In addition, CETP inhibition promoted monocyte activation in the blood prior to the onset of sepsis, resulting in accelerated macrophage recruitment to the lung and liver. In vitro experiments demonstrated that CETP inhibition significantly promoted the activation of proinflammatory signaling in peripheral blood mononuclear cells and THP1 cells in the absence of HDL; this may represent a mechanism responsible for improved bacterial clearance during sepsis. These findings provide evidence that CETP inhibition represents a potential approach to reduce mortality from 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".