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Record W2024283409 · doi:10.1002/cbic.201200081

Cardiotoxin‐I: An Unexpectedly Potent Insulinotropic Agent

2012· article· en· W2024283409 on OpenAlexafffund
Thi Tuyet Nhung Nguyen, Benjamin Folch, Myriam Létourneau, David Vaudry, Nam Hai Truong, Nicolas Doucet, David Chatenet, Alain Fournier

Bibliographic record

VenueChemBioChem · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsCardiotoxinSecretionInsulinHemolysisViability assaySnake venomBiologyPharmacologyChemistryVenomBiochemistryCellEndocrinologyImmunology

Abstract

fetched live from OpenAlex

Insulin secretion from pancreatic β-cells is a complex process, involving the integration and interaction of multiple external and internal stimuli, in which glucose plays a major role. Understanding the physiology leading to insulin release is a crucial step toward the identification of new targets. In this study, we evaluated the presence of insulinotropic metabolites in Naja kaouthia snake venom. Only one fraction, identified as cardiotoxin-I (CTX-I) was able to induce insulin secretion from INS-1E cells without affecting cell viability and integrity, as assessed by MTT and LDH assays. Interestingly, CTX-I was also able to stimulate insulin secretion from INS-1E cells even in the absence of glucose. Although cardiotoxins have been characterized as potent hemolytic agents and vasoconstrictors, CTX-I was unable to induce direct hemolysis of human erythrocytes or to induce potent vasoconstriction. As such, this newly identified insulin-releasing toxin will surely enrich the pool of existing tools to study β-cell physiology or even open a new therapeutic avenue.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 designBench or experimental
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

Citations25
Published2012
Admission routes2
Has abstractyes

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