Molecular and Seroprevalence of <i>Mycoplasma gallisepticum</i> in Turkeys in Sylhet District of Bangladesh
Bibliographic record
Abstract
Mycoplasma gallisepticum (MG) poses a significant threat to Bangladesh's poultry industry, causing substantial economic losses every year. This study aimed to determine the prevalence of MG infection in turkeys using serum plate agglutination (SPA), enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) in Sylhet, Bangladesh from December 2019 to November 2020. In addition, we evaluated the diagnostic accuracy of these tests and identified potential risk factors associated with MG infection. A total of 250 blood samples and 250 tracheal swabs were collected from suspected turkeys across 25 farms from three sub-districts of Sylhet namely Sylhet Sadar, Golapganj and Beanibazar. Blood samples were tested with SPA and ELISA, while tracheal swabs were analysed by PCR targeting the 16S rRNA gene of MG. The overall prevalence of MG was 35.2%, 29.2% and 25.6% for SPA, ELISA and PCR respectively. Higher infection rates were observed in turkeys aged 0-4 months (SPA 57.1%, ELISA 52%, PCR 42.8%), during winter (SPA 43.1%, ELISA 37.8%, PCR 30%) and among female turkeys (SPA 54.5%, ELISA 49.5%, PCR 45.5%). Geographically, the Beanibazar had the highest prevalence (SPA 54.2%, ELISA 48.6%, PCR 41.4%), compared to the Sylhet Sadar and Golapganj sub-districts. Both SPA and ELISA tests showed 100% sensitivity, with specificity of 87.1% and 95.2%, respectively using PCR as a gold standard. Overall, these findings provide valuable insights for developing effective control measures for MG infections in the poultry industry of Bangladesh.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".