Identification and characterization of a peptide mimetic that may detect a species of disease‐associated anticardiolipin antibodies in patients with the antiphospholipid syndrome
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility of applying a mimetic (specific for a patient-derived prothrombotic anticardiolipin antibody [aCL]) to study the homologous, disease-associated aCL in patients with antiphospholipid syndrome (APS). METHODS: We used the CL15 monoclonal aCL to screen 17 phage-display peptide libraries. Peptides (corresponding to recurrent peptide sequences) and their derivatives were synthesized and analyzed for binding to CL15 and for their abilities to inhibit CL15 from binding to cardiolipin. A peptide was chosen and used to study CL15-like IgG aCL in plasma samples from patients with APS, patients with systemic lupus erythematosus (SLE) but without APS, and normal healthy donors. RESULTS: Library screening with CL15 yielded 4 recurrent peptide sequences. Analyses of peptides showed that peptide CL154C reacted with antibody CL15 and inhibited binding of CL15 to cardiolipin, indicating that peptide CL154C may be a peptide mimetic for the CL15 aCL. Initial studies with plasma samples revealed that CL154C-reactive IgG was present (positivity defined as the mean + 3 SD optical density of the 25 normal controls) in 15 of 21 APS patients and 1 of 12 SLE patients. CONCLUSION: These findings suggest that it is feasible to develop a specific enzyme-linked immunosorbent assay for each immunologically and functionally distinct disease-associated aCL. Additional testing of CL154C with a larger number of APS patients and SLE patients, as well as identification of peptide mimetics for each distinct aCL, will reveal the diagnostic potential of CL154C and other mimetics in identifying patients with aCL who are at risk of developing life-threatening thrombosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".