Human antibodies against Mycobacterium avium ssp. paratuberculosis combined with cytokine levels for the diagnosis and selection of Crohn’s disease patients for anti-mycobacterial therapy—A pilot study
Bibliographic record
Abstract
Increasing evidence links a worldwide bacterial infection of cattle and other animal species by Mycobacterium avium ssp. paratuberculosis (MAP) to Crohn's disease (CD). A large, FDA phase 2/3 controlled clinical trial of combination antimycobacterial antibiotic therapy for CD has been completed, and the report describing the trial is pending publication. The identification of MAP infection in CD patients will become increasingly important. Thus, it is desirable to develop MAP-based tests that accurately predict which CD patients have a MAP infection. A prospective, case-control laboratory test study of 199 subjects (61 CD patients and 138 non-CD controls) was performed using a panel of MAP antigens, including Hsp65, PknG, PtpA, CL1, and MAP IDEXX, which were measured under blind conditions in the plasma of the 199 subjects. Results showed that compared to any individual MAP antigen, combinations of antigens showed improved CD classification performance. For the Hsp65 antigen, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), correct classification (CC), and area under the curve (AUC) were 59.02%, 58.70%, 38.71%, 76.42%, 59.3% and 0.606, respectively. For the best combination of MAP antibodies (Hsp65 and PknG), the SEN, SPE, PPV, NPV, CC, and AUC were 59.02%, 60.87%, 40.00%, 77.06%, 60.30%, and 0.631, respectively. Further improvement of the CD classification performance was achieved by combining IFN-γ, IL-8, and IL-17 cytokines with antibodies against MAP antigens, yielding SEN, SPE, PPV, NPV, CC, and AUC of 62.3%, 62.32%, 42.22%, 78.9%, 62.31% and 0.708, respectively. Thus, combinations of antibodies against MAP antigens and cytokine levels yield better CD diagnostic predictive performance than any individual antibodies against MAP antigens.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".