Role of lipoproteins and proprotein convertase subtilisin/kexin type 9 in endotoxin clearance in sepsis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition is a recent high-impact cardiovascular intervention aimed at reducing low-density lipoprotein (LDL) cholesterol levels. Notably, pathogen lipids are also carried in lipoprotein particles and are cleared by hepatocyte LDL receptors. Therefore, the role of PCSK9 in sepsis is reviewed. RECENT FINDINGS: Endogenous PCSK9 decreases clearance of LDL cholesterol by decreasing the number of LDL receptors on hepatocytes. Similarly, PCSK9 decreases clearance of pathogen lipids, such as endotoxin, carried in LDL. Pathogen lipids, such as lipopolysaccharide (LPS) from gram-negative organisms or lipoteichoic acid from gram-positive organisms, are carried in high-density lipoprotein, LDL, and very low-density lipoprotein particles. Transfer proteins that handle pathogen lipids (e.g., LPS binding protein) are homologous to transfer proteins that handle cholesterol (e.g., phospholipid transfer protein, cholesterol ester transfer protein). Reduction in PCSK9 function results in increased LPS clearance, a decreased inflammatory response, and improved clinical outcomes in mice. PCSK9 inhibition improves survival in septic mice. Similarly, humans who carry loss-of-function variants of the PCSK9 gene have increased survival in sepsis. SUMMARY: PCSK9 inhibition may be a useful strategy to increase pathogen lipid clearance in the treatment of patients with sepsis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".