Binding Interaction Between Boronic Acid Derivatives with Monosaccharaides: Effect of Structural Change of Monosaccharaides Upon Binding Using S–V Plots
Bibliographic record
Abstract
Abstract Sugar sensing and continuous monitoring of glucose (CGM) play an important and vital role in controlling diabetes. The present enzyme‐based sugar sensors have their own drawbacks. Problems associated with them have encouraged alternate approaches to design new sensors. Among many, fluorescent intensity change based sensors are drawing more attention. Fluorescence sensors based on boronic acid derivatives are more popular because of their ability to reversibly bind diol‐containing compounds. Here, the binding ability of two boronic acid derivatives, namely 2‐methylphenyl boronic acid (B1) and 3‐methoxyphenyl boronic acid (B2) with mono saccharides (sugars), is under investigation. The sugar concentration is kept nearly 1000 times more than that of boronic acid. The interactions of B1 and B2 with three saccharides ( d ‐sorbitol, dextrose, and fructose) are studied by absorbance and steady‐state fluorescence. Both B1 and B2 fluorescence is quenched by formation of esters with saccharides. The results of absorbance and fluorescence measurements indicate that the studied sugars can be ordered by their affinity to B1 as: d ‐sorbitol > xylose > dextrose and for B2 as: dextrose > xylose > d ‐sorbitol. In each case, slope of modified S–V plots is nearly one indicating only a single binding site in boronic acids for sugars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".