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Biologically Active Synthetic Anionophores

2014· article· en· W2768721195 on OpenAlexaff
Andreea R. Schmitzer, Claude-Rosny Elie, Marc Vidal, Mathieu Charbonneau, Audrey Hébert

Bibliographic record

VenueCurrent Organic Chemistry · 2014
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsChemistryBiological activityIon channelTransmembrane proteinTransmembrane channelsCancer cellBiophysicsComputational biologyCombinatorial chemistryBiochemistryNanotechnologyCancerIn vitroBiologyReceptor

Abstract

fetched live from OpenAlex

The physiological importance of proteins that can regulate ion balance and transmembrane transport is highlighted by different diseases where ion channel dysfunction is observed. During the past two decades, considerable effort has been devoted to develop synthetic ionophores that can insert or cross cell membranes and restore the dysfunction of highly complex protein channels. Not withstanding the remarkable structural advances made, only a few classes of synthetic ionophores were studied in complex with proand eukaryote cells in order to obtain information about their biological activity and potential application in ion channel replacement therapy, anti-cancer therapy or antimicrobial treatments. However, only a few synthetic ionophores showed promising biological activity in cellular assays. This review aims to show the utility of synthetic ionophores for different biological applications, including: restoring ion concentration, inducing cell death in different cancer cells, and protecting against a variety of pathogenic microbes. Because the activities of these ionophores depend primarily on their overall physicochemical properties and structure, we discuss here specific functional units and scaffolds that are important for obtaining selective, non-toxic transporters for specific biological applications. Keywords: Anionophores, anion transporters, biological applications.

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 categoriesInsufficient payload (model declined to judge)
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 score0.097
Threshold uncertainty score0.987

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)0.0140.000

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 head0.009
GPT teacher head0.221
Teacher spread0.211 · 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.

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

Citations6
Published2014
Admission routes1
Has abstractyes

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