Rapid screening and analysis of alcohol dehydrogenase binders from <i>Glycyrrhiza uralensis</i> root extract using functionalized magnetic nanoparticles coupled with HPLC−MS/MS
Bibliographic record
Abstract
Alcohol dehydrogenase (ADH) is a key enzyme that converts ethanol into acetaldehyde. Its binders could be used to efficiently treat human toxic alcohol poisoning and suppress the acetaldehyde accumulation in alcohol hypersensitive alcoholics. In the present study, a new assay based on ADH-functionalized magnetic nanoparticles coupled with high-performance liquid chromatography − tandem mass spectrometry (HPLC−MS/MS) was developed for the rapid screening and identification of ADH binders. ADH was immobilized on silica-coated Fe 3 O 4 magnetic nanoparticles via covalent bonds and the synthesized nanoparticles were characterized by TEM, XRD, FT-IR, and VSM. The amount of bound ADH onto magnetic nanoparticles was 80.6 μg/mg. The optimum temperature and pH for the enzymatic activity of immobilized ADH were 25 °C and 7.0, respectively. The relative activity of immobilized ADH remained 68.14% after 10 times of recycle, which exhibited good durability. Nine compounds with ADH-binding activity were screened and identified in Glycyrrhiza uralensis root extracts, among which seven compounds were first screened and identified. Experimental results proved that the proposed method could rapidly screen ADH binders from complex mixtures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".