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Extracellular Vesicles and Insulin Resistance: A Potential Interaction in Vascular Dysfunction

2018· review· en· W2895057155 on OpenAlex
Tamara Sáez, Fernando Toledo, Luis Sobrevía

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

VenueCurrent Vascular Pharmacology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Alberta
FundersFondo Nacional de Desarrollo Científico y Tecnológico
KeywordsInsulin resistanceMicrovesiclesExtracellularExtracellular vesiclesInsulinMedicineDiabetes mellitusVesicleCell biologyExosomeInternal medicineEndocrinologyBiologyBiochemistrymicroRNA

Abstract

fetched live from OpenAlex

Insulin resistance plays a key role in cardiovascular complications associated with diabetes mellitus and hypertensive disorders. In states of insulin resistance several circulating factors may contribute to a defective insulin sensitivity in different tissues, including the vasculature. One of these factors influencing the vascular insulin resistance are the extracellular vesicles. The extracellular vesicles include exosomes, microvesicles, and apoptotic bodies which are released to the circulation by different vascular cells. Since the cargo of extracellular vesicles seems to be altered in metabolic complications associated with insulin resistance, these vesicles may be candidates contributing to vascular insulin resistance. Despite the studies linking insulin resistance signalling pathways with the vascular effect of extracellular vesicles, the involvement of these structures in vascular insulin resistance is a phenomenon that remains unclear.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.335
Teacher spread0.309 · 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