Fluorescent detection of target proteins <i>via</i> a molecularly imprinted hydrogel
Bibliographic record
Abstract
Proteins are typically separated by an immune reaction, such as an enzyme-linked immunosorbent assay, and are detected by selective fluorescent labeling. This has potential for complicated procedures and the denaturation of proteins by labeling, and is cost consuming. In this study, we propose a technique for the selective separation and detection of a target protein using a molecularly imprinted hydrogel (PI gel) with fluorescent monomers. We focused on 8-anilino-1-naphthalenesulfonic acid (ANS), where the fluorescence intensity is easily changed by the interaction with proteins, and successfully synthesized the ANS monomer and a poly(ethylene glycol) (PEG) conjugated ANS monomer. The PI gel with the ANS monomers using bovine serum albumin (BSA) as a template showed the selective adsorption of BSA and the fluorescence intensity increased due to the adsorption of BSA.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".