Assessment and Translation of the Antibody-in-Lymphocyte Supernatant (ALS) Assay to Improve the Diagnosis of Enteric Fever in Two Controlled Human Infection Models and an Endemic Area of Nepal
Bibliographic record
Abstract
New diagnostic tests for enteric fever are urgently needed to assist with timely antimicrobial treatment of patients and to measure the efficacy of prevention measures such as vaccination. In a novel translational approach, here we use two recently developed controlled human infection models (CHIM) of enteric fever to evaluate an antibody-in-lymphocyte supernatant (ALS) assay, which can detect recent IgA antibody production by circulating B cells in ex vivo mononuclear cell culture. We calculated the discriminative ability of the ALS assay to distinguish diagnosed cases in the two CHIM studies in Oxford, prior to evaluating blood culture-confirmed diagnoses of patients presenting with fever to hospital in an endemic areas of Kathmandu, Nepal. Antibody responses to membrane preparations and lipopolysaccharide provided good sensitivity (>90%) for diagnosing systemic infection after oral challenge with Salmonella Typhi or S. Paratyphi A. Assay specificity was moderate (~60%) due to imperfect sensitivity of blood culture as the reference standard and likely unrecognized subclinical infection. These findings were augmented through the translation of the assay into the endemic setting in Nepal. Anti-MP IgA responses again exhibited good sensitivity (86%) but poor specificity (51%) for detecting blood culture-confirmed enteric fever cases (ROC AUC 0.79, 95%CI 0.70–0.88). Patients with anti-MP IgA ALS titers in the upper quartile exhibited a clinical syndrome synonymous with enteric fever. While better reference standards are need to assess enteric fever diagnostics, routine use of this ALS assay could be used to rule out infection and has the potential to double the laboratory detection rate of enteric fever in this setting over blood culture alone.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".