Biologically Active Synthetic Anionophores
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".