Mycobacterium avium subspecies paratuberculosis diagnosis and geno-typing: Genomic insights
Bibliographic record
Abstract
Effective control of paratuberculosis and investigations of potential link to Crohn's disease have been hampered by the lack of effective assays for easy and accurate diagnosis of Mycobacterium avium subspecies paratuberculosis (Map). Map is extremely fastidious and depends on iron chelator (Mycobactin). Map strains from humans and sheep are very difficult to isolate and may require years to emerge. Therefore, small numbers of Map isolates have been maintained in available collections. This situation has limited the study of biodiversity of Map. Though, much is known about environmental and host factors that contribute to paratuberculosis disease, but little is known about bacterial genetic mechanism of infection. Diagnostic and strain typing markers still demand improvements. Complete genome sequence of Map K10 strain is available in public domain for comparative genomics with other mycobacteria and clinical isolates of Map. It is anticipated that the genome sequence will help in carrying molecular diagnosis and strain typing with respect to Map forward at rapid pace. This paper reviews the current diagnostic and strain typing markers, which may be useful in typing of clinical isolates in near future.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".