Biological sample analysis with immunoaffinity solid-phase microextraction
Bibliographic record
Abstract
A theophylline antiserum was covalently immobilized on the surface of a fused silica fiber, modified with 3-aminopropyltriethoxysilane (APTES) and glutaraldehyde, and used as a selective and sensitive extraction medium for the immunoaffinity solid-phase microextraction (SPME) determination of theophylline in serum samples. The specificity of the immunoaffinity SPME fiber was first investigated using a fixed concentration of [3H]theophylline together with various amounts of interference, possessing no cross-reactivity with the theophylline antibody. No significant non-specific binding was observed. The reproducibility of the fiber preparation and the immunoaffinity SPME analysis was also investigated, resulting in a relative standard deviation of 6.1% for five analyses of the same fiber. The antigen-antibody binding isotherm was obtained by analyzing theophylline standards of various concentrations (0.1-5 ng mL(-1)) until saturation values were reached. Initial binding of theophylline was linear with a r2 = 0.968. The cross-reactivity of the theophylline immunoaffinity SPME fiber for the structural analog caffeine was investigated by adding various amounts of caffeine in the presence of theophylline at a saturation concentration and produced a low cross-reactivity value of 0.1%. Finally. spiked serum samples (10 and 50 ng mL(-1)) were successfully analyzed with an excellent correlation with the standard binding isotherm, thus confirming the performance of the immunoaffinity SPME coating for improved bioanalysis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".