Sensitive Detection of Human IgG in ELISA Using a Monoclonal Anti-IgG-Peroxidase Conjugate
Bibliographic record
Abstract
Enzyme-antibody (Ab) conjugates specific for IgG are widely used in indirect immunological assays and have been until recently routinely prepared with polyclonal IgG-specific animal Abs. The use of monoclonal Abs (MAbs) could permit a better standardization of the IgG-specific conjugate reagents but is expected to result in lower reactivity due to the recognition of a single epitope by the MAbs. In this work, we have characterized a monoclonal anti-human IgG-peroxidase (HRP) reagent and compared its reactivity with commercial reagents. The murine C5-1 anti-human IgG MAb was selected for conjugation because of its high affinity (K(a) = 1.9 x 10(10)M), pan-IgG reactivity and absence of cross-reactivity with various structures including animal IgGs. The specific activity and binding kinetics of the C5-1:HRP conjugate were similar to the ones of two polyclonal anti-IgG:HRP conjugates when tested with immobilized human IgG. The C5-1:HRP conjugate could detect low amounts of human IgG much more effectively than two commercial monoclonal conjugates although it was slightly less effective than a polyclonal conjugate. However, the C5-1 conjugate yielded reduced background reactivity compared to the polyclonal conjugate, resulting in similar signal-to-noise ratios. These results indicate that the C5-1:HRP conjugate could be a suitable substitute for anti-human IgG conjugates prepared from animal antisera.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".