Microparticles: biomarkers and beyond
Bibliographic record
Abstract
Membrane microparticles are submicron fragments of membrane shed into extracellular space from cells under conditions of stress/injury. They may be distinguished from other classes of extracellular vesicles (i.e. exosomes) on the basis of size, content and mechanism of formation. Microparticles are found in plasma and other biological fluids from healthy individuals and their levels are altered in various diseases, including diabetes, chronic kidney disease, pre-eclampsia and hypertension among others. Accordingly, they have been considered biomarkers of vascular injury and pro-thrombotic or pro-inflammatory conditions. In addition to this, emerging evidence suggests that microparticles are not simply a consequence of disease, but that they themselves may contribute to pathological processes. Thus microparticles appear to serve as both markers and mediators of pathology. The present review examines the evidence for microparticles as both biomarkers of, and contributors to, the progression of disease. Approaches for the detection of microparticles are summarized and novel concepts relating to the formation of microparticles and their biological effects are examined.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".