Development of a xylitol biosensor composed of xylitol dehydrogenase and diaphorase
Bibliographic record
Abstract
In preparation for the development of a xylitol biosensor, the xylitol dehydrogenase of Candida tropicalis IFO 0618 was partially purified and characterized. The optimal pH and temperature of the xylitol dehydrogenase were pH 8.0 and 50 degrees C, respectively. Of the various alcohols tested, xylitol was the most rapidly oxidized, with sorbitol and ribitol being reduced at 65% and 58% of the xylitol rate. The enzyme was completely inactive on arabitol, xylose, glucose, glycerol, and ethanol. The enzyme's xylitol oxidation favored the use of NAD+ (7.9 U/mg) over NADP+ (0.2 U/mg) as electron acceptor, while the reverse reaction, D-xylulose reduction, favored NADPH (7.7 U/mg) over NADH (0.2 U/mg) as electron donor. The K(m) values for xylitol and NAD+ were 49.8 mM and 38.2 microM, respectively. For the generation of the xylitol biosensor, the above xylitol dehydrogenase and a diaphorase were immobilized on bromocyan-activated sephallose. The gel was then attached on a dissolved oxygen electrode. In the presence of vitamin K3, NAD+ and phosphate buffer, the biosensor recorded a linear response to xylitol concentration up to 3 mM. The reaction was stable after 15 min. When the biosensor was applied to a flow injection system, optimal operation pH and temperature were 8.0 and 30 degrees C, respectively. The strengths and limitations of the xylitol biosensor are its high affinity for NAD+, slow reaction time, narrow linear range of detection, and moderate affinity for xylitol.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".