Integration of a PDMS/SU-8/Quartz Microfluidic Chip With a Novel Macroporous Poly Dimethylsiloxane (PDMS) Membrane for Isoelectric Focusing of Proteins Using Whole-Channel Imaging Detection
Bibliographic record
Abstract
Capillary isoelectric focusing (CIEF) is a high-resolution capillary electrophoresis (CE) technique for separating zwitterionic biomolecules, such as proteins and peptides. In this method, by generating a stable pH gradient along the length of the capillary and under the influence of a constant electric field, samples can be separated according to their different isoelectric points (pI). For identifying the focused zones in CIEF, the whole column imaging detection (WCID) is more reliable than any other single point detection methods since it avoids the need of focused peak mobilization, presenting several advantages such as lower detection time, minimized peak dispersion and consequently higher resolution. Capillary-based IEF-WCID has been invented by Convergent Bioscience Inc. (iCE280 analyzer) for separation of proteins and biomarkers [1–2]. In the iCE280 analyzer, hollow fibers are glued to the capillary to separate electrolytes from the samples and a metal slit with a 65 μm opening is glued to the top of the capillary to improve detection sensitivity by blocking stray light. However, this method has several limitations because of the use of capillary such as low throughput, difficulty to be integrated with other separation modes and low detection sensitivity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".