New molecular diagnostic targets for Avibacterium paragallinarum and a set of single-plex and multiplex qPCR methods for the rapid differential diagnosis of Mycoplasma gallisepticum, Mycoplasma synoviae, and Avibacterium paragallinarum
Bibliographic record
Abstract
ABSTRACT Mycoplasma gallisepticum ( MG ), Mycoplasma synoviae ( MS ), and Avibacterium paragallinarum ( APG ) are respiratory-borne bacterial pathogens that severely harm the poultry industry. The clinical symptoms caused by them share many similarities, such as respiratory disease, growth retardation, and decreased egg production. They are not suitable for rapid diagnosis through isolation and culture and often need to detect nucleic acids or antibodies for differential diagnosis. In this study, bioinformatics analyses were used, and six specific coding genes were identified as being shared among all the APG strains that were absent in other species with published genome sequences. Combined with MG- and MS-specific genes identified in previous studies, we established a set of single-plex and multiplex qPCR assays for the rapid differential diagnosis of these three pathogens. The results indicated that the correlation coefficients (R 2 ) of the standard curve established in these methods were not less than 0.999, and the amplification efficiencies (E) were between 90% and 110%. In terms of specificity, with the exception of the amplification curve and C T value generated in the positive control, other related pathogens, chicken cells, and empty plasmid did not amplify. In terms of sensitivity, the 100% detection sensitivity of MG single-plex qPCR, MS single-plex qPCR, APG single-plex qPCR, and MG-MS duplex qPCR established in this study was 5 copies/reaction. The 100% detection sensitivity of MG-MS-APG triplex qPCR was 5 copies/reaction in both the MG and MS detection channels and 10 copies/reaction in the APG detection channel. The detection rate of triplex qPCR in the APG detection channel at 5 copies/reaction was 80%. The intra-group and inter-group variation coefficients of the qPCR methods established in this study were all within 2% in the repeatability evaluation. In terms of the coincidence rate of clinical sample testing, the qPCR methods showed 100% detection consistency for the clinical samples tested. The established qPCR methods exhibited good specificity, sensitivity, and repeatability, which provide powerful technical support for the rapid and efficient differential diagnosis of MG, MS, and APG simultaneously.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".