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Record W3124166787 · doi:10.3390/molecules26030618

From Angiotensin II to Cyclic Peptides and Angiotensin Receptor Blockers (ARBs): Perspectives of ARBs in COVID-19 Therapy

2021· review· en· W3124166787 on OpenAlexaff
John Matsoukas, Vasso Apostolopoulos, Anthony Zulli, Graham J. Moore, Konstantinos Kelaidonis, Kalliopi Moschovou, Thomas Mavromoustakos

Bibliographic record

VenueMolecules · 2021
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Calgary
FundersGeneral Secretariat for Research and TechnologyVictoria University
KeywordsLosartanAngiotensin IIRenin–angiotensin systemPharmacologyChemistryAngiotensin II receptor type 1PeptideAngiotensin II receptor antagonistAngiotensin receptorAntagonistAngiotensin-converting enzymeReceptorInternal medicineMedicineBiochemistryBlood pressure

Abstract

fetched live from OpenAlex

The octapeptide hormone angiotensin II is one of the most studied peptides with the aim of designing and synthesizing non-peptide mimetics for oral administration. To achieve this, cyclizations at different positions within the peptide molecule has been a useful strategy to define the active conformation. These studies on angiotensin II led to the discovery of Sarmesin, a type II angiotensin II antagonist, and the breakthrough non-peptide mimetic Losartan, the first in a series of sartans marketed as a new generation of anti-hypertensive drugs in the 1990s. Angiotensin II receptor blockers (ARBS) and angiotensin I converting enzyme inhibitors (ACEI) were recently reported to protect hypertensive patients infected with SARS-CoV-2. The renin-angiotensin system (RAS) inhibitors reduce excess angiotensin II and increase antagonist heptapeptides alamandine and aspamandine which counterbalance angiotensin II and maintain homeostasis and vasodilation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.049
GPT teacher head0.367
Teacher spread0.318 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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