PCSK9 is a critical regulator of the innate immune response and septic shock outcome
Bibliographic record
Abstract
A decrease in the activity of proprotein convertase subtilisin/kexin type 9 (PCSK9) increases the amount of low-density lipoprotein (LDL) receptors on liver cells and, therefore, LDL clearance. The clearance of lipids from pathogens is related to endogenous lipid clearance; thus, PCSK9 may also regulate removal of pathogen lipids such as lipopolysaccharide (LPS). Compared to controls, Pcsk9 knockout mice displayed decreases in inflammatory cytokine production and in other physiological responses to LPS. In human liver cells, PCSK9 inhibited LPS uptake, a necessary step in systemic clearance and detoxification. Pharmacological inhibition of PCSK9 improved survival and inflammation in murine polymicrobial peritonitis. Human PCSK9 loss-of-function genetic variants were associated with improved survival in septic shock patients and a decrease in inflammatory cytokine response both in septic shock patients and in healthy volunteers after LPS administration. The PCSK9 effect was abrogated in LDL receptor (LDLR) knockout mice and in humans who are homozygous for an LDLR variant that is resistant to PCSK9. Together, our results show that reduced PCSK9 function is associated with increased pathogen lipid clearance via the LDLR, a decreased inflammatory response, and improved septic shock outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".