Molecular regulation of plasma lipid levels during systemic inflammation and sepsis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sepsis is a common syndrome of multiorgan system dysfunction caused by a dysregulated inflammatory response to an infection and is associated with high rates of mortality. Plasma lipid and lipoprotein levels and composition change profoundly during sepsis and have emerged as both biomarkers and potential therapeutic targets for this condition. The purpose of this article is to review recent progress in the understanding of the molecular regulation of lipid metabolism during sepsis. RECENT FINDINGS: Patients who experience greater declines in high-density lipoprotein during sepsis are at much greater risk of succumbing to organ failure and death. Although the causality of these findings remains unclear, all lipoprotein classes can sequester and prevent the excessive inflammation caused by pathogen-associated lipids during severe infections such as sepsis. This primordial innate immune function has been best characterized for high-density lipoproteins. Most importantly, results from human genetics and preclinical animal studies have suggested that several lipid treatment strategies, initially designed for atherosclerosis, may hold promise as therapies for sepsis. SUMMARY: Lipid and lipoprotein metabolism undergoes significant changes during sepsis. An improved understanding of the molecular regulation of these changes may lead to new opportunities for the treatment of sepsis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 0.003 |
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".