Enrichment and immobilization of macromolecular analytes on a porous membrane utilizing permeation drag
Bibliographic record
Abstract
Enrichment and immobilization of analytes by chemical bonding or physical adsorption is typically the first step in many commonly used analytical techniques. In this paper, we discuss a permeation drag based technique as an alternative approach for carrying out location-specific immobilization of macromolecular analytes. Fluorescein isothiocyanate (FITC) labeled macromolecules and their complexes were enriched near the surface of ultrafiltration membranes and detected by direct visual observation and fluorescence imaging. The level of macromolecule enrichment at the immobilization sites could be controlled by manipulating the filtration rate and thereby the magnitude of permeation drag. Higher enrichment as indicated by higher fluorescence intensity was observed at higher filtration rates. Also, larger macromolecules were more easily enriched. The feasibility of using this technique for detecting immunocomplexes was demonstrated by carrying out experiments with FITC labeled bovine serum albumin (FITC-BSA) and its corresponding antibody. This permeation drag based enrichment technique could potentially be developed further to suit a range of analytical applications involving more sophisticated detection methods.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".