The Central Role of Proprotein Convertase Subtilisin/Kexin Type 9 in Septic Pathogen Lipid Transport and Clearance
Bibliographic record
Abstract
Microbial cell walls contain pathogenic lipids, including LPS in gram-negative bacteria, lipoteichoic acid in gram-positive bacteria, and phospholipomannan in fungi. These pathogen lipids are major ligands for innate immune receptors and figure prominently in triggering the septic inflammatory response. Alternatively, pathogen lipids can be cleared and inactivated, thus limiting the inflammatory response. Accordingly, biological mechanisms for sequestering and clearing pathogen lipids from the circulation have evolved. Pathogen lipids released into the circulation are initially bound by transfer proteins, notably LPS binding protein and phospholipid transfer protein, and incorporated into high-density lipoprotein particles. Next, LPS binding protein, phospholipid transfer protein, and other transfer proteins transfer these lipids to ApoB-containing lipoproteins, including low-density (LDL) and very-low-density lipoproteins and chylomicrons. Pathogen lipids within these lipoproteins and their remnants are then cleared from the circulation by the liver. Hepatic clearance involves the LDL receptor (LDLR) and possibly other receptors. Once absorbed by the liver, these lipids are then excreted in the bile. Recent evidence suggests pathogen lipid clearance can be modulated. Importantly, reduced proprotein convertase subtilisin/kexin type 9 activity increases recycling of the LDLR and thereby increases LDLR on the surface of hepatocytes, which increases clearance by the liver of pathogen lipids transported in LDL. Increased pathogen lipid clearance, which can be achieved by inhibiting proprotein convertase subtilisin/kexin type 9, may decrease the systemic inflammatory response to sepsis and improve clinical outcomes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".