Bibliographic record
Abstract
Enzyme linked immunosorbent assay (ELISAs) has been used as a convenient routine immunological assay for many specific antigens to monitor antibody responses to vaccination, and has replaced other methods, e.g., radioimmunoassay (FARR assay) ( 1 , 2 ). For example, ELISA using tetanus toxoid, 1diphtheria toxoid, Haemophilus influenzae Type b (Hib) polysaccharide and Neisseria meningitidis A/C polysaccharide are used to determine serum antibodies following vaccination. In these assays, the antigen, e.g., toxoid (tetanus, diphtheria) or polysaccharide (Hib PRP, Men C) alone or conjugated to poly-L-lysine or methylated human serum albumin (mHSA), is coated onto plastic of 96-well microtiter plates ( 3 - 5 ). By direct or indirect methods the antibodies in serum samples can be detected using enzyme-linked secondary antibody conjugates, e.g., horseradish peroxidase or alkaline phosphatase, and appropriate substrates using color reaction at specific absorbance e.g., ODA 405. The principle of the indirect ELISA is shown in Fig. 1. ( 6 ). Using dilutions of a standard serum or monoclonal antibody with known concentration of total/ specific antibody or given arbitrary units, it is possible to compare dilutions of unknown samples against a standard curve. In this chapter we demonstrate how to establish an ELISA for antibody to H. influenzae (Hi) inner-core lipopolysaccharide (LPS) epitopes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.160 |
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".