Microparticles and acute lung injury
Bibliographic record
Abstract
The pathophysiology of acute lung injury (ALI) and its most severe form, acute respiratory distress syndrome (ARDS), is characterized by increased vascular and epithelial permeability, hypercoagulation and hypofibrinolysis, inflammation, and immune modulation. These detrimental changes are orchestrated by cross talk between a complex network of cells, mediators, and signaling pathways. A rapidly growing number of studies have reported the appearance of distinct populations of microparticles (MPs) in both the vascular and alveolar compartments in animal models of ALI/ARDS or respective patient populations, where they may serve as diagnostic and prognostic biomarkers. MPs are small cytosolic vesicles with an intact lipid bilayer that can be released by a variety of vascular, parenchymal, or blood cells and that contain membrane and cytosolic proteins, organelles, lipids, and RNA supplied from and characteristic for their respective parental cells. Owing to this endowment, MPs can effectively interact with other cell types via fusion, receptor-mediated interaction, uptake, or mediator release, thereby acting as intrinsic stimulators, modulators, or even attenuators in a variety of disease processes. This review summarizes current knowledge on the formation and potential functional role of different MPs in inflammatory diseases with a specific focus on ALI/ARDS. ALI has been associated with the formation of MPs from such diverse cellular origins as platelets, neutrophils, monocytes, lymphocytes, red blood cells, and endothelial and epithelial cells. Because of their considerable heterogeneity in terms of origin and functional properties, MPs may contribute via both harmful and beneficial effects to the characteristic pathological features of ALI/ARDS. A better understanding of the formation, function, and relevance of MPs may give rise to new promising therapeutic strategies to modulate coagulation, inflammation, endothelial function, and permeability either through removal or inhibition of "detrimental" MPs or through administration or stimulation of "favorable" MPs.
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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) | 0.002 | 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".