The Diagnosis of Mycobacterium avium Infections Using Serodiagnosis With Novel Lipid Antigens
Bibliographic record
Abstract
Mycobacterium avium subspecies Paratuberculosis (MAP) is an endemic pathogen in ruminants, present in a high proportion of herds worldwide. Its presence within cattle herds creates an economic burden on both farmers and the wider economy; due to lost milk production and premature culling. \nCurrently there is a lack of sensitive and rapid detection techniques, as culture can take months to give results and traditional PCR cannot distinguish viable from non-viable cells. Tests utilising defined synthetic mycolic acids and their sugar esters have already shown promise at diagnosing tuberculosis. This will form the basis of work described here, translating those procedures to the detection of MAP using both ELISA and a flow through device. \nMAP has also been shown to survive pasteurisation, thus making it into the food chain, and has been proposed as an aetiological agent for the development of Crohn’s disease. \n•\tFirst, a study of strongly positive experimentally infected cattle samples against negative serum from a herd with no history of MAP resulted in a single antigen sensitivity/specificity of 100/100. \n•\tSecond, a study of 40 negative and 40 positive, naturally infected cattle samples from Canada resulted in a sensitivity/specificity of 85/75. Combined with the first study and utilising all 5 common antigens for diagnosis resulted in a sensitivity/specificity of 84/93 \n•\tInitial testing and translation of flow through procedures from M. tb to MAP with a pooled cattle sample resulted in defined red spots, with the control remaining clear. \n•\tMAP specific antigens tested against human Crohn’s samples as compared with healthy samples resulted in a single antigen sensitivity/specificity of 91/100 \nThis work has identified promising antigens for further large-scale testing against both MAP in cattle and Crohn’s disease. Additionally, the first test of a flow through device shows promise in developing a rapid point of care device for the detection of MAP. \n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".