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Record W4220895126

Exosomes and the cardiovascular system : role in cardiovascular health and disease

2022· article· en· W4220895126 on OpenAlexaff
Karla B Neves, Francisco J. Rios, Augusto C. Montezano, Rhian M. Touyz

Bibliographic record

VenueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMicrovesiclesEndosomemicroRNADiseaseBiologyCell biologyExosomeTransfectionOxidative stressBioinformaticsIntracellularMedicinePathologyGeneBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Exosomes, which are membrane-bound extracellular vesicles (EVs), are generated in the endosomal compartment of almost all eukaryotic cells. They are formed upon the fusion of multivesicular bodies and the plasma membrane and carry proteins, nucleic acids, lipids and other cellular constituents from their parent cells. Multiple factors influence their production including cell stress and injury, humoral factors, circulating toxins, and oxidative stress. They play an important role in intercellular communication, through their ability to transfer their cargo (proteins, lipids, RNAs) from one cell to another. Exosomes have been implicated in the pathophysiology of various diseases including cardiovascular disease (CVD), cancer, kidney disease, and inflammatory conditions. In addition, circulating exosomes may act as biomarkers for diagnostic and prognostic strategies for several pathological processes. In particular exosome-containing miRNAs have been suggested as biomarkers for the diagnosis and prognosis of myocardial injury, stroke and endothelial dysfunction. They may also have therapeutic potential, acting as vectors to deliver therapies in a targeted manner, such as the delivery of protective miRNAs. Transfection techniques are in development to load exosomes with desired cargo, such as proteins or miRNAs, to achieve up-regulation in the host cell or tissue. These advances in the field have the potential to assist in the detection and monitoring progress of a disease in patients during its early clinical stages, as well as targeted drug delivery.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde)Same topicExtracellular vesicles in diseaseFrench-language works237,207