MétaCan
Menu
Back to cohort
Record W2075983567 · doi:10.1042/cs20120309

Microparticles: biomarkers and beyond

2012· review· en· W2075983567 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Science · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMicrovesiclesExtracellular vesiclesBiological fluidsExtracellularDiseasePathologicalKidney diseaseExtracellular vesicleMedicineCell biologyChemistryImmunologyPathologyBiologyBiochemistryInternal medicinemicroRNA

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.999
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.423
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it