Detection of Autoantibodies by Enzyme-Linked Immunosorbent Assay and Bead Assays
Bibliographic record
Abstract
Autoantibodies directed against intracellular antigens are characteristic features of a number of human autoimmune diseases and certain malignancies (1–3). Studies of systemic autoimmune rheumatic diseases have provided strong evidence that autoantibodies are maintained by antigen-driven responses (4, 5) and that autoantibodies can be reporters from the immune system, revealing the identities of antigens involved in disease pathogenesis. Historically, autoantibody detection and analysis have relied on a number of different technologies, such as hemagglutination and particle aggregation, immunodiffusion, indirect immunofluorescence (IIF), complement fixation, counterimmunoelectrophoresis (CIE), Western and dot blotting, immunoprecipitation (IP), and enzyme-linked immunosorbent assay (ELISA), and on functional assays that demonstrate inhibition of the catalytic or other functional activity of the antigen of interest. These technologies have limitations because they tend to be labor-intensive and time-consuming, are limited in throughput, are semiquantitative, and are not adaptable to leading-edge research. Immunodiffusion has been used for over 50 years, and it is still used in some clinical laboratories because it is inexpensive and has high specificity, but it lacks sensitivity and can take up to 48 h before precipitin lines are interpretable. Western blotting is more costly and time-consuming, and not all autoantibodies are detected by this technique. For example, in the SS-A/Ro system, it has been observed that IP techniques are required to identify some sera that contain antibodies reacting only with the “native” SS-A/Ro particle (6). IP protocols that use extracts from [35S]methionine-labeled cells are not suitable for the detection of all autoantibodies, such as antibodies to Ro52/TRIM21 protein (7). ELISA techniques have rapidly advanced, but highly specific, sensitive, and reliable assays that use highly purified or recombinant proteins are limited by intermanufacturer and interlaboratory variation of results (8). Immunodiffusion and CIE generally favor high-titer sera and often cannot discriminate multiple autoantibody responses that are characteristic of systemic autoimmune rheumatic disease sera.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.012 |
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".