Membrane chromatographic immunoassay method for rapid quantitative analysis of specific serum antibodies
Bibliographic record
Abstract
This paper discusses a membrane chromatographic immunoassay method for rapid detection and quantitative analysis of specific serum antibodies. A type of polyvinylidine fluoride (PVDF) microfiltration membrane was used in the method for its ability to reversibly and specifically bind IgG antibodies from antiserum samples by hydrophobic interaction. Using this form of selective antibody binding and enrichment an affinity membrane with antigen binding ability was obtained in-situ. This was done by passing a pulse of diluted antiserum sample through a stack of microporous PVDF membranes. The affinity membrane thus formed was challenged with a pulse of antigen solution and the amount of antigen bound was accurately determined using chromatographic methods. The antigen binding correlated well with the antibody loading on the membrane. This method is direct, rapid and accurate, does not involve any chemical reaction, and uses very few reagents. Moreover, the same membrane could be repeatedly used for sequential immunoassays on account of the reversible nature of the antibody binding. Proof of concept of this method is provided using human hemoglobin as model antigen and rabbit antiserum against human hemoglobin as the antibody source.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".