Kinetic methods in capillary electrophoresis and their applications
Bibliographic record
Abstract
In recent years, capillary electrophoresis (CE) has been one of rapidly growing analytical techniques to study affinity interactions. Quick analysis, high efficiency, high resolving power, low sample consumption, and wide range of possible analytes make CE an indispensable tool for studies of biomolecules and, in particular, studies of their interactions. In the article, we discuss kinetic methods in CE. The spectrum of proven applications of kinetic CE methods includes: (i) measuring equilibrium and rate constants of protein-ligand interaction from a single experiment, (ii) quantitative affinity analyses of proteins, (iii) measuring temperature in CE, (iv) studying thermochemistry of affinity interactions, and (v) kinetic selection of ligands from combinatorial libraries. We demonstrate that new kinetic CE method can serve as a "Swiss army knife" in the development and utilization of oligonucleotide aptamers. Uniquely, they can facilitate selection of smart aptamers - aptamers with pre-defined binding parameters. We believe that further development of kinetic CE methods will provide a variety of methodological schemes for high-throughput screening of combinatorial libraries for affinity probes and drug candidates using CE as a universal instrumental platform.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".