Integration of Dialysis Membranes into a Poly(dimethylsiloxane) Microfluidic Chip for Isoelectric Focusing of Proteins Using Whole-Channel Imaging Detection
Bibliographic record
Abstract
A poly(dimethylsiloxane) microfluidic chip-based cartridge is developed and reported here for protein analysis using isoelectic focusing (IEF)-whole-channel imaging detection (WCID) technology. In this design, commercial dialysis membranes are integrated to separate electrolytes and samples and to reduce undesired pressure-driven flow. Fused-silica capillaries are also incorporated in this design for sample injection and channel surface preconditioning. This structure is equivalent to that of a commercial fused-silica capillary-based cartridge for adapting to an IEF analyzer (iCE280 analyzer) to perform IEF-WCID. The successful integration of dialysis membranes into a microfluidic chip significantly improves IEF repeatability by eliminating undesired pressure-driven hydrodynamics and also makes sample injection much easier than that using the first-generation chip as reported recently. In this study, two microfluidic chips with a 100-microm-high, 100-microm-wide and a 200-microm-high, 50-microm-wide microchannel, respectively, were applied for qualitative and quantitative analysis of proteins. The mixture containing six pI markers with a pH range of 3-10 was successfully separated using IEF-WCID. The pH gradient exhibited a good linearity by plotting the pI value versus peak position, and the correlation coefficient reached 0.9994 and 0.9995 separately for the two chips. The separation of more complicated human hemoglobin control sample containing HbA, HbF, HbS, and HbC was also achieved. Additionally, for the quantitative analysis, a good linearity of IEF peak value versus myoglobin concentration in the range of 20-100 microg/mL was obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".