Preliminary examination of time-resolved fluorometry for protein array applications
Bibliographic record
Abstract
The advantages of time-resolved fluorometry over conventional fluorometric analysis are well known. However, time-resolved fluorescence has not as yet found wide applications in protein microarray or other multiparametric methods of analysis. Here we describe a general method which is suitable for multiparametric and microarray analysis, based on time-resolved fluorometry. A polystyrene surface is coated with different monoclonal antibodies, specific for certain analytes. The analyte mixtures are then universally biotinylated, using an active biotin ester. After removing excess biotin, the biotinylated samples are applied on the polystyrene surface, incubated and the excess is washed away. The bound moieties are then quantified by adding a universal detection reagent containing streptavidin, labelled with a fluorescent europium chelate. After washing and drying of the solid surface, the immobilized moieties are detected by using solid-phase, laser-excited time-resolved fluorometric analysis. In a preliminary examination of this principle, we have demonstrated that we can correctly identify upregulation of three secreted proteins, following stimulation of a breast carcinoma cell line with various steroids. Our method should be suitable for high-density microarray analysis of proteins, captured by specific monoclonal antibodies or other binding reagents.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".