Development of antigen-specific ELISA for circulating autoantibodies to extracellular matrix protein 1 in lichen sclerosus
Bibliographic record
Abstract
Lichen sclerosus is a common, acquired chronic inflammatory skin disease of unknown etiology, although circulating autoantibodies to the glycoprotein extracellular matrix protein 1 (ECM1) have been detected in most patients' sera.We have examined the nature of ECM1 epitopes in lichen sclerosus sera, developed an ELISA system for serologic diagnosis, and assessed clinicopathological correlation between ELISA titer and disease.Epitope-mapping studies revealed that lichen sclerosus sera most frequently recognized the distal second tandem repeat domain and carboxyl-terminus of ECM1.We analyzed serum autoantibody reactivity against this immunodominant epitope in 413 individuals (95 subjects with lichen sclerosus, 161 normal control subjects, and 157 subjects with other autoimmune basement membrane or sclerosing diseases).The ELISA assay was highly sensitive; 76 of 95 lichen sclerosus patients (80.0%) exhibited IgG reactivity.It was also highly specific (93.7%) in discriminating between lichen sclerosus and other disease/control sera.Higher anti-ECM1 titers also correlated with more longstanding and refractory disease and cases complicated by squamous cell carcinoma.Furthermore, passive transfer of affinity-purified patient IgG reproduced some histologic and immunopathologic features of lichen sclerosus skin.This new ELISA is valuable for the accurate detection and quantification of anti-ECM1 autoantibodies.Moreover, the values may have clinical significance in patients with lichen sclerosus.Nonstandard abbreviations used: arbitrary unit (AU); area under the curve (AUC); confidence interval (CI); extracellular matrix protein 1 (ECM1); glutathione S-transferase (GST); receiver operating characteristic (ROC); Tris-buffered saline (TBS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".