Preventing intense false positive and negative reactions attributed to the principle of ELISA to re-investigate antibody studies in autoimmune diseases
Bibliographic record
Abstract
To study the possible involvement of potential environmental pathogens in the pathogenesis of autoimmune diseases, it is essential to investigate antibody responses to a variety of environmental agents and autologous components. However, none of the conventional ELISA buffers can prevent the false positive and negative reactions attributed to its principal, which utilizes the high binding affinity of proteins to plastic surfaces. The aims of this study are to reveal all types of non-specific reactions associated with conventional buffer systems, and to re-investigate antibody responses to potential environmental pathogenic and autologous antigens in patients with autoimmune diseases using a newly developed buffer system "ChonBlock™" by ELISA. Compared to conventional buffers, the new buffer was highly effective in reducing the most intense false positive reaction caused by hydrophobic binding of immunoglobulin in sample specimens to plastic surfaces, "background (BG) noise reaction", and other non-specific reactions without interfering with antigen-antibody reactions. Applying this buffer, we found that IgG antibody responses to Escherichia coli O111:B4, E. coli lipopolysaccharide (LPS) and peptidoglycan polysaccharide (PG-PS) were significantly lower or tended to be lower in patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), whereas IgA antibody responses to these antigens were equal or tended to be higher compared to normal controls. As a consequence, the IgA/IgG antibody ratios against these agents were significantly higher in patients with RA and SLE, except for Crohn's disease, which showed significantly higher IgG responses to these antigens. To assay antibodies in human sera, it is indispensable to eliminate false positive and negative reactions by using an appropriate buffer system, and to include antigen non-coated blank wells to determine BG noise reactions of invidual samples. Finally, based on our preliminary analysis in this study, we propose that low IgG antibody responses to potential pathogenic environmental factors may be the fundamental disorder in autoimmune diseases.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".