Generation of Two-color Antigen Microarrays for the Simultaneous Detection of IgG and IgM Autoantibodies
Bibliographic record
Abstract
Autoantibodies, which are antibodies against self-antigens, are present in many disease states and can serve as markers for disease activity. The levels of autoantibodies to specific antigens are typically detected with the enzyme-linked immunosorbent assay (ELISA) technique. However, screening for multiple autoantibodies with ELISA can be time-consuming and requires a large quantity of patient sample. The antigen microarray technique is an alternative method that can be used to screen for autoantibodies in a multiplex fashion. In this technique, antigens are arrayed onto specially coated microscope slides with a robotic microarrayer. The slides are probed with patient serum samples and subsequently fluorescent-labeled secondary antibodies are added to detect binding of serum autoantibodies to the antigens. The autoantibody reactivities are revealed and quantified by scanning the slides with a scanner that can detect fluorescent signals. Here we describe methods to generate custom antigen microarrays. Our current arrays are printed with 9 solid pins and can include up to 162 antigens spotted in duplicate. The arrays can be easily customized by changing the antigens in the source plate that is used by the microarrayer. We have developed a two-color secondary antibody detection scheme that can distinguish IgG and IgM reactivities on the same slide surface. The detection system has been optimized to study binding of human and murine autoantibodies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".