Analysis of DNA in Phosphate Buffered Saline Using Kinetic Capillary Electrophoresis
Bibliographic record
Abstract
Kinetic capillary electrophoresis (KCE) methods are useful in the study of kinetics and equilibrium properties of interactions between DNA and its binding partners (ligands). KCE experiments are typically performed in a narrow set of "conventional" low-conductivity run buffers while DNA-ligand interactions in biological systems occur in physiological fluids, characterized by high ionic strengths. The nature and ionic strength of the buffer, in which DNA-ligand interaction occurs, can significantly influence the binding. Therefore, KCE experiments meant to study such interactions would greatly benefit if they could be performed in physiological buffers, such as phosphate buffered saline (PBS). No previous KCE studies of DNA used PBS as the run buffer. Here, we test the feasibility of using PBS as a KCE run buffer for analysis of DNA and show that its usage under standard KCE conditions renders DNA undetectable. We uncover the causes of this previously unreported detrimental effect and come up with a modification of KCE which allows one to overcome it. We apply the modified KCE method to an experimental model of a platelet-derived growth factor (PDGF) protein and its DNA aptamer, which was selected in PBS, and show that the results obtained in PBS run buffer are much closer to previously reported values than those which were obtained with a conventional low-conductivity capillary electrophoresis (CE) buffer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".