Serological and Molecular Characterization of Mycobacterium avium Subsp. paratuberculosis (MAP) from Sheep, Goats, Cattle and Camels in the Eastern Province, Saudi Arabia
Bibliographic record
Abstract
The objectives of the present study were to characterize Mycobacterium avium subsp. paratuberculosis (MAP) infection using serological and molecular tools and investigate the distribution and molecular characterization of MAP strains (cattle (C) and sheep (S) types) in sheep, goat, cattle, and camel herds in Eastern Province, Saudi Arabia. Serum and fecal samples were collected from all animals aged >2 years old in 31 herds (sheep = 8, goats = 6, cattle = 8 and camels = 9) from January to December 2019. Serum samples were tested by ELISA for the detection of MAP antibodies. Fecal samples were tested by PCR for the detection of MAP IS900 gene and the identification of MAP strains. MAP antibodies were detected in 19 (61.3%) herds. At the animal level, antibodies against MAP were detected in 43 (19.5%) sheep, 21 (17.1%) goats, 13 (19.7%) cattle and 22 (9.1%) camels. The IS900 gene of MAP was detected in 23 (74.2%) herds and was directly amplified from fecal samples of 59 (26.8%) sheep, 34 (27.6%) goats, 20 (30.3%) cattle and 36 (15.0%) camels. The S-type was the most prevalent MAP type identified in 15 herds, and all were identified as type-I, while the C-type was identified in only 8 herds. The IS900 sequences revealed genetic differences among the MAP isolates recovered from sheep, goats, cattle and camels. Results from the present study show that MAP was prevalent and confirm the distribution of different MAP strains in sheep, goat, cattle and camel herds in Eastern Province, Saudi Arabia.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".