Dimeric immunoglobulin A as a novel diagnostic marker of measles infection
Bibliographic record
Abstract
Abstract Despite tremendous measles incidence reduction through universal vaccination, elimination efforts rely on improved surveillance. Detection of anti-measles immunoglobulin M (IgM) by ELISA is the standard laboratory diagnostic method. However, true infection is rare and seroconversion following MMR vaccination also generates IgM, which results in low positive predictive values of assays in elimination settings, thus necessitating confirmatory testing. Improved diagnostic tests for measles infection are a World Health Organization (WHO) research priority. We investigated whether dimeric immunoglobulin A (dIgA), the predominant antibody produced in mucosal immunity, may be a marker of recent or acute measles infection. We examined a serological panel of confirmed measles infection (anti-measles IgM positives, n=50), and non-measles infection with rubella (n=36), roseola (n=40), chikungunya/dengue/zika (n=41), parvovirus (n=35) and other fever-rash illness of unknown cause (n=37). Sera were examined on a Micrommune anti-measles IgM, Euroimmun anti-measles virus lysate (VL) and nucleoprotein (NP) IgM kits. Assays were then modified to detect dIgA using an in-house protocol based on a recombinant chimeric secretory component protein and anti-secretory component monoclonal antibody. We observed significantly higher levels of anti-measles VL dIgA in measles samples than non-measles controls (p<0.001), and there was low correlation with IgM (R 2 : 0.01, p value:0.487). Unlike IgM, dIgA reactive to measles NP was not detected in most samples. Comparable diagnostic potential of anti-measles dIgA (AUC 0.920 - 0.945) to anti-measles IgM (AUC 0.986 – 0.995), suggests that dIgA may be a new blood-based marker of acute measles, independent of IgM, which merits further investigation and optimization.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".