High Resolution for Single-Strand Conformation Polymorphism Analysis by Capillary Electrophoresis
Bibliographic record
Abstract
Since the successful completion of the Human Genome Project, increasing concern is being directed toward the polymorphic aspect of the genome and its clinical relevance. A form of single-strand DNA-conformation polymorphism analysis (SSCP) employing nondenaturing slab-gel electrophoresis (SGE) is applicable to the genetic diagnosis of bladder cancer from urine samples. To bring this technique into routine clinical practice, the use of capillary electrophoresis (CE) is naturally favorable in terms of speed and automation. However, the resolving power of SSCP, a prerequisite basis for reliability required in diagnostics, remains as a challenge for CE systems. We thus focused on this topic and conducted studies on CE instruments equipped with a single capillary or an array of multiple capillaries, using the resolution (Rs) as a quantitative scale for the resolving power. Polymer concentration and buffer are shown to be the decisive parameters. High Rs values of >2.5 are achieved for representative SNPs markers under the optimized conditions, without sacrificing such intrinsic advantages of CE over SGE as the 10-fold quicker migration time and operation that is reproducible, continuous, and automatic. The strategies presented broaden the limits of CE in both the current and related applications.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".