Visual monitoring of biocatalytic processes using small molecular fluorescent probes: strategies-mechanisms-applications
Bibliographic record
Abstract
the transfer of the γ-glutamine group and the release of the free amino group, which results in restoration of the "Donor-π-Acceptor" (D-π-A) system and fluorescence recovery. (2) Probes for monitoring biocatalytic oxidation: a propylamine is connected to the fluorophore as a recognition group, which cages the hydroxyl group, leading to the inhibition of ICT; propylamine is oxidized and subsequently β-elimination occurs, resulting in exposure of the hydroxyl group and fluorescence recovery. (3) Probes for monitoring biocatalytic reduction: a nitro group attached to a fluorophore as a fluorescence quenching group, this is converted to an amino group by catalytic reduction, resulting in fluorescence recovery. (4) Probes for monitoring biocatalytic hydrolysis: β-D-galactopyranoside or phosphate acts as a recognition group attached to hydroxyl groups of the fluorophore; the subsequent biocatalytic hydrolysis reaction releases the hydroxyl group resulting in fluorescence recovery. Following these 4 mechanisms, fluorophores including cyanine, coumarin, rhodamine, and Nile-red, have been used to develop systems for monitoring biocatalytic reactions. We anticipate that these strategies will result in systems able to rapidly diagnose and facilitate the treatment of serious diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".