Circulating <scp>PCSK</scp>9 is lowered acutely following surgery
Bibliographic record
Abstract
BACKGROUND: A decrease in serum low-density lipoprotein cholesterol (LDL-C) is well documented after acute stress. Plasma proprotein convertase subtilisin kexin 9 (PCSK9), which promotes degradation of low-density lipoprotein receptor (LDL-R) resulting in reduced plasma clearance of low-density lipoproteins (LDL) and an increase in serum LDL-C, would be predicted to decrease. Yet, a few studies have demonstrated an increase 1-8 days after acute stress. Our objective was to assess the earlier status of plasma PCSK9, within the first 24 hours of onset of stress. METHODS: We measured serum lipids and plasma PCSK9 in 39 patients before and soon after an elective surgical procedure (abdominal aortic aneurysm (AAA) repair). RESULTS: We observed an early decrease in PCSK9 following surgery, as well as a decrease in total cholesterol (TC), LDL-C, high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C) and triglycerides (TG). CONCLUSION: Unlike other studies which showed an increase in PCSK9 after the onset of stress, our study detected a fall in PCSK9 following acute surgical stress. The observed difference is likely due to the earlier timing of PCSK9 measurement in our study. Further studies involving serial poststress measurements for several days are needed to determine whether PCSK9 behaves as an acute-phase reactant, whether it displays a biphasic response to acute stress, and whether changes in circulating PCSK9 are responsible for lipoprotein changes observed after surgical stress. (Clinical Trial Registration: ClinicalTrials.gov study ID NCT00493389).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".