Plasmalogens and platelet‐activating factor roles in chronic inflammatory diseases
Bibliographic record
Abstract
Fatty acids and phospholipid molecules are essential for determining the structure and function of cell membranes, and they hence participate in many biological processes. Platelet activating factor (PAF) and its precursor plasmalogen, which represent two subclasses of ether phospholipids, have attracted increasing research attention recently due to their association with multiple chronic inflammatory, neurodegenerative, and metabolic disorders. These pathophysiological conditions commonly involve inflammatory processes linked to an excess presence of PAF and/or decreased levels of plasmalogens. However, the molecular mechanisms underlying the roles of plasmalogens in inflammation have remained largely elusive. While anti-inflammatory responses most likely involve the plasmalogen signal pathway; pro-inflammatory responses recruit arachidonic acid, a precursor of pro-inflammatory lipid mediators which is released from membrane phospholipids, notably derived from the hydrolysis of plasmalogens. Plasmalogens per se are vital membrane phospholipids in humans. Changes in their homeostatic levels may alter cell membrane properties, thus affecting key signaling pathways that mediate inflammatory cascades and immune responses. The plasmalogen analogs of PAF are also potentially important, considering that anti-PAF activity has strong anti-inflammatory effects. Plasmalogen replacement therapy was further identified as a promising anti-inflammatory strategy allowing for the relief of pathological hallmarks in patients affected by chronic diseases with an inflammatory component. The aim of this Short Review is to highlight the emerging roles and implications of plasmalogens in chronic inflammatory disorders, along with the promising outcomes of plasmalogen replacement therapy for the treatment of various PAF-related chronic inflammatory 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 | 0.001 |
| 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) | 0.003 | 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".