Comparative study of HA and HNB staining RT-LAMP assays for peste des petits ruminants virus detection in West African Dwarf goats
Bibliographic record
Abstract
Abstract Peste des petits ruminants (PPR) causes severe economic losses to many countries of the world where the disease is endemic. It has been targeted for global eradication by 2030 following the successful eradication of rinderpest in 2011. The proposed eradication program would benefit from efficient and relatively reliable diagnostic tools for early PPR virus (PPRV) detection. A total of 33 eight to 12 months old West African Dwarf (WAD) goats were used. Nineteen goats infected by comingling with two PPR virus positive animals formed the infected group (PPRV-infected goats) while 14 non-infected goats formed the control group (CTG). The suitability of a molecular method (Hydroxyl naphthol blue (HNB) staining of reverse transcription loop mediated isothermal amplification (RT-LAMP)) and a non-molecular tool (haemagluttination assay (HA)) were compared for their sensitivity to detect the PPRV in PPRV-infected goats and non-infected CTG. PPR disease severity in WAD goats at different days post infection (dpi) was evaluated by clinical scoring and haemagluttination titre (HAT). HNB staining RT-LAMP reaction and HA showed sensitivities of 100% and 73.68%, respectively, for PPRV detection. Expression of PPR clinical signs began from 3 dpi, attained peak at 5 dpi, thereafter showed irregular patterns till 24 dpi. Evaluation of HAT in PPRV-infected goats at 12 dpi ranged from 2 to 64 haemagluttination units (HAU), while CTG goats had 0 HAU. In conclusion, HNB staining RT-LAMP assay demonstrated reasonable potential for accurate diagnoses of PPRV and as an important diagnostic tool in areas with poor electricity supply and less sophisticated laboratory equipment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".