Composition of the teat canal and intramammary microbiota of dairy cows subjected to antimicrobial dry cow therapy and internal teat sealant
Bibliographic record
Abstract
Antimicrobial dry cow therapy (DCT) is an important component of mastitis control programs aimed to eliminate existing intramammary infections and prevent the development of new ones during the dry period. However, to what extent the microbiota profiles of different niches of the udder change during the dry period and following administration of DCT remains poorly understood. Therefore, the main objective of the present study was to qualitatively evaluate dynamics of the microbiota of teat canal (TC) and mammary secretions (i.e., milk and colostrum) of healthy udder quarters subjected to DCT using a long-acting antimicrobial product, containing penicillin G and novobiocin, in combination with internal teat sealant. To this end, TC swabs (n = 58) and their corresponding milk (n = 29) and colostrum samples (n = 29) were collected at the time of drying off and immediately after calving from clinically healthy udder quarters of Holstein dairy cows from a commercial dairy farm. All samples were subjected to DNA extraction and high-throughput sequencing of the V1-V2 hypervariable regions of bacterial 16S rRNA genes. Overall, shifts were more pronounced within the microbiota of mammary secretions than the TC. In particular, microbiota of colostrum samples collected immediately after calving were less species-rich compared with the pre-DCT milk samples. Proportions of several bacterial genera belonging to the phylum Proteobacteria, including Pseudomonas, Stenotrophomonas, and unclassified Alcaligenaceae, were enriched within the microbiota of colostrum samples, whereas Firmicutes genera, including Butyrivibrio, unclassified Clostridiaceae, and unclassified Bacillales, were overrepresented in pre-DCT milk microbiota. Apart from shifts in the proportion of main bacterial genera and phyla, qualitative analysis revealed a high degree of commonality between pre-DCT and postpartum microbiota of both niches of the udder. Most importantly, a considerable number of bacterial genera and species commonly regarded as mastitis pathogens or opportunists (or both), including Staphylococcus spp., unclassified Enterobacteriaceae, and Corynebacterium spp., were shared between pre-DCT and postpartum microbiota of mammary secretions. Percentage of shared bacterial genera and species was even higher between pre-DCT and postpartum microbiota of TC samples, suggesting that the DCT approach of the present study had limited success in eliminating a considerable proportion of bacteria during the dry period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".