A highly sensitive CE‐UV method with dynamic coating of silica‐fused capillaries for monitoring of nucleotide pyrophosphatase/phosphodiesterase reactions
Bibliographic record
Abstract
A new highly sensitive capillary electrophoresis (CE) method applying dynamic coating and on-line stacking for the monitoring of nucleotide pyrophosphatases/phosphodiesterases (NPPs) and the screening of inhibitors was developed. NPP1 and NPP3 are membrane glycoproteins that catalyze the hydrolysis nucleotides, e.g. convert adenosine 5'-triphosphate to adenosine 5'-monophosphate (AMP) and pyrophosphate. Enzymatic reactions were performed and directly subjected to CE analysis. Since the enzymatic activity was low, standard methods were insufficient. The detection of nanomolar AMP and other nucleotides could be achieved by field-enhanced sample injection and the addition of polybrene to the running buffer. The polycationic polymer caused a dynamic coating of the silica-fused capillary, resulting in a reversed electroosmotic flow. The nucleotides migrated in the direction of the electroosmotic flow, whereas the positively charged polybrene molecules moved in the opposite direction, resulting in a narrow sample zone over a long injection time. Using this on-line sensitivity enhancement technique, a more than 70-fold enrichment was achieved for AMP (limit of detection, 46 nM) along with a short migration time (5 min) without compromising separation efficiency and peak shape. The optimized CE conditions were as follows: fused-silica capillary (30 cm effective lengthx75 mum), electrokinetic injection for 60 s, 50 mM phosphate buffer pH 6.5, 0.002% polybrene, constant current of -60 microA, UV detection at 210 nm, uridine 5'-monophosphate as the internal standard. The new method was used to study enzyme kinetics and inhibitors. It opens an easy way to determine the activities of slowly metabolizing enzymes such as NPPs, which are of considerable interest as novel drug targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".