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Record W2462416979 · doi:10.1515/hsz-2015-0240

Kinins and peptide receptors

2015· review· en· W2462416979 on OpenAlexaff
D. Regoli, Fernand Gobeil

Bibliographic record

VenueBiological Chemistry · 2015
Typereview
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsReceptorChemistryPeptideCell biologyBiochemistryBiology

Abstract

fetched live from OpenAlex

This paper is divided into two sections: the first contains the essential elements of the opening lecture presented by Pr. Regoli to the 2015 International Kinin Symposium in S. Paulo, Brazil on June 28th and the second is the celebration of Dr. Regoli's 60 years of research on vasoactive peptides. The cardiovascular homeostasis derives from a balance of two systems, the renin-angiotensin system (RAS) and the kallikrein-kinin system (KKS). The biologically active effector entity of RAS is angiotensin receptor-1 (AT-1R), and that of KKS is bradykinin B2 receptor (B2R). The first mediates vasoconstriction, the second is the most potent and efficient vasodilator. Thanks to its complex and multi-functional mechanism of action, involving nitric oxide (NO), prostacyclin and endothelial hyperpolarizing factor (EDHF). B2R is instrumental for the supply of blood, oxygen and nutrition to tissues. KKS is present on the vascular endothelium and functions as an autacoid playing major roles in cardiovascular diseases (CVDs) and diabetes. KKS exerts a paramount role in the prevention of thrombosis and atherosclerosis. Such knowledge emphasizes the already prominent value of the ACE-inhibitors (ACEIs) for the treatment of CVDs and diabetes. Indeed, the ACEIs, thanks to their double action (block of the RAS and potentiation of the KKS) are the ideal agents for a rational treatment of these diseases.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.179
GPT teacher head0.375
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2015
Admission routes1
Has abstractyes

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