Microchip UV Absorbance Detection Applied to Isoelectric Focusing of Proteins
Bibliographic record
Abstract
Isoelectric focusing (IEF) is considered as an attractive separation technique for biologically amphoteric compounds (e.g., proteins and peptides) based on their isoelectric point (pI). With the advancement in micromachining technology, microchip format IEF has attracted significant attention. Both single-point and whole column imaging detection (WCID) methods have been employed for analyzing the separation performance in a microchip. WCID is more favorable than single-point detection because the latter requires the focused bands to be mobilized and thus adds more complexity to the design and operation of such microchips. Fluorescence- and UV absorbance-based WCID have been successfully adapted in glass and PDMS microchips. We have developed polydimethylsiloxane (PDMS) microchips for IEF applications where UV-WCID is employed for evaluating the separation performance. The chips are designed for use in the iCE280 analyzer (Convergent Bioscience Inc., Toronto), for capillary-based IEF where UV-WCID is employed for analyzing the separation performance. Three kinds of microchips that have been successfully developed using standard soft lithography technology are described in detail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".