Carryover of bovine leukemia virus antibodies in samples from shared milk meters
Notice bibliographique
Résumé
Screening for infectious diseases of cattle using milk from the dairy herd improvement (DHI) sampling process is very convenient. However, when samples from shared milk meters are used, carryover of antibodies or other diagnostic targets can complicate the interpretation of the diagnostic test results for diseases, including bovine leukosis. The objectives of this study were (1) to assess the potential for carryover of antibodies against bovine leukemia virus (BLV) in milk samples obtained from shared meters, and (2) to determine if adjustment of the diagnostic test cut-off value would improve the test characteristics for meter-collected milk ELISA results. Eight dairy farms were randomly selected from herds with a wide range of BLV prevalence levels in Prince Edward Island, Canada. Within each chosen farm, 2 to 4milk meters were randomly selected. During the routine procedures of DHI sampling, 2 simultaneous milk samples, 1 hand-collected at the beginning of milking (after udder preparation) and the other from the corresponding milk meter, were taken from all lactating cows (n=236) that were milked at the selected meters (n=26). The sequence of cows using each meter was recorded. All samples were tested for BLV antibodies using a commercial indirect ELISA. Antibody carryover potential was assessed in meter-collected samples which were preceded by other cows using the same meters. Applying the hand-collected sample results as our reference standard, a new cut-off was defined for meter-collected samples to optimize the test characteristics. At the standard cut-off value of the diagnostic test, 110 (46.6%) of the hand-collected and 136 (57.6%) of the meter-collected samples were positive. For low-titer cows (e.g., true negatives), the likelihood of antibody carryover significantly increased as the titer of preceding cows increased, whereas this change was not substantial for high-titer cows. The odds of obtaining false diagnoses in meter-positive samples became larger with increase in the titer of preceding cows. A suspicious category for meter ELISA results was defined, and a retest was recommended for the cows falling into this category. This strategy effectively assisted in reducing the number of consequent false-positive results. When DHI-collected samples are used, carryover can affect the interpretation of dichotomous test results and may require adjustment of assay cut-off values.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».