Plasmalogens and Chronic Inflammatory Diseases
Bibliographic record
Abstract
It is becoming widely acknowledged that lipids play key roles in cellular function, regulating a variety of biological processes. Lately, a subclass of glycerophospholipids, namely plasmalogens, has received increased attention due to their association with several degenerative and metabolic disorders as well as aging. All these pathophysiological conditions involve chronic inflammatory processes, which have been linked with decreased levels of plasmalogens. Currently, there is a lack of full understanding of the molecular mechanisms governing the association of plasmalogens with inflammation. However, it has been shown that in inflammatory processes, plasmalogens could trigger either an anti- or pro-inflammation response. While the anti-inflammatory response seems to be linked to the entire plasmalogen molecule, its pro-inflammatory response seems to be associated with plasmalogen hydrolysis, i.e., the release of arachidonic acid, which, in turn, serves as a precursor to produce pro-inflammatory lipid mediators. Moreover, as plasmalogens comprise a large fraction of the total lipids in humans, changes in their levels have been shown to change membrane properties and, therefore, signaling pathways involved in the inflammatory cascade. Restoring plasmalogen levels by use of plasmalogen replacement therapy has been shown to be a successful anti-inflammatory strategy as well as ameliorating several pathological hallmarks of these diseases. The purpose of this review is to highlight the emerging role of plasmalogens in chronic inflammatory disorders as well as the promising role of plasmalogen replacement therapy in the treatment of these pathologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".