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Record W2275366049 · doi:10.18314/idcd.v1i1.53

Role of Vascular Endothelium in Hypertension, Atherosclerosis and Peripheral Arterial Disease

2016· article· en· W2275366049 on OpenAlexaff
Dhalla Ns

Bibliographic record

VenueIntegrative Diabetes and Cardiovascular Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersSlovenská Akadémia Vied
KeywordsMedicineThrombosisEndothelial dysfunctionEndotheliumVascular diseaseHemostasisInflammationEndothelin receptorPathophysiology of hypertensionCardiologyPeripheralVasoconstrictionDiseaseCirculatory systemInternal medicineBlood pressureReceptor

Abstract

fetched live from OpenAlex

The vascular endothelium is a dynamic structure which lines the entire circulatory and lymphatic systems and interacts with local and systemic stimuli. It plays an important role in such processes as vasoconstriction/vasorelaxation, inflammation, cell proliferation, and hemostasis. Dysfunction of endothelial cells contributes to the development of different diseases including hypertension, atherosclerosis, and peripheral arterial disease, which are commonly seen in patients with chronic diabetes. Various risk factors including low density lipoprotein oxidation, inflammation, thrombosis as well as imbalance between NO and endothelin production are considered to induce the VE dysfunction and associated cardiovascular diseases. Although several interventions such as thrombolytic agents, anti-inflammatory agents, antioxidants, NO donors, endothelin inhibitors and stem cell therapy are used for the treatment of hypertension, atherosclerosis and peripheral artery disease, none of these have been found to exert satisfactory beneficial effects. Thus a great deal of research work needs to be carried out to define the exact molecular targets and develop newer therapies for the treatment of hypertension, atherosclerosis and peripheral vascular disease.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2016
Admission routes1
Has abstractyes

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