Small-Molecule Fluorescent Probes for Binding- and Activity-Based Sensing of Redox-Active Biological Metals
Bibliographic record
Abstract
Although transition metals constitute less than 0.1% of the total mass within a human body, they have a substantial impact on fundamental biological processes across all kingdoms of life. Indeed, these nutrients play crucial roles in the physiological functions of enzymes, with the redox properties of many of these metals being essential to their activity. At the same time, imbalances in transition metal pools can be detrimental to health. Modern analytical techniques are helping to illuminate the workings of metal homeostasis at a molecular and atomic level, their spatial localization in real time, and the implications of metal dysregulation in disease pathogenesis. Fluorescence microscopy has proven to be one of the most promising non-invasive methods for studying metal pools in biological samples. The accuracy and sensitivity of bioimaging experiments are predominantly determined by the fluorescent metal-responsive sensor, highlighting the importance of rational probe design for such measurements. This review covers activity- and binding-based fluorescent metal sensors that have been applied to cellular studies. We focus on the essential redox-active metals: iron, copper, manganese, cobalt, chromium, and nickel. We aim to encourage further targeted efforts in developing innovative approaches to understanding the biological chemistry of redox-active metals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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.
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".