Detection and quantification of bacterial species DNA in bovine digital dermatitis lesions in swabs and fine-needle aspiration versus biopsies
Bibliographic record
Abstract
Digital Dermatitis (DD) is a polymicrobial disease characterized by ulcerative lesions on the heel bulb of cattle and for which, despite being reported almost 50 years ago, information on the causative agent is still lacking. Tissue biopsies are regularly collected to identify bacterial presence-absence and their relative abundance in the microbiome, with sufficient evidence for the high abundance of species of Treponema spp. and other anaerobes in lesions. However, it is unclear what the potential of less-invasive sampling methods is for bacterial detection and quantification. This study aimed to test whether less-invasive sampling techniques, such as swabs and fine-needle aspiration (FNA), can be a convenient alternative to tissue biopsies in detecting and quantifying seven DD-associated bacteria in active, ulcerative DD lesions by qPCR. Twenty-two M2 DD lesions were collected using corresponding swabs, aspirates, and biopsies from dairy cows. Presence/absence and quantities of Treponema phagedenis, Treponema medium, Treponema pedis, Porphryromonas levii, Bacteroides pyogenes, Fusobacterium necrophorum, and Fusobacterium mortiferum were correlated, and Bland-Altman plot, McNemar's test, and Cohen's kappa coefficient were used to calculate the agreement among the methods. The quantities of all species were larger in swabs and smaller in aspirates compared to biopsies; however, the differences in bacterial enumeration observed between biopsies and swabs were smaller than in biopsies and aspirates. A strong correlation was observed between the quantity of T. pedis, T. medium, P. levii, and F. mortiferum in biopsies, swabs, and FNA. Yet, T. phagedenis presented the smallest difference between biopsies and swabs, followed by T. pedis and T. medium. In conclusion, swabs, aspirates, and biopsies were equal in their capacity to detect Treponema species based on the good agreement for bacteria presence/absence, with a more limited agreement for the other anaerobes, which were more often present in M2 lesions swabs by qPCR. Bacterial numbers were higher in swabs and lower in aspirates compared to biopsies, with the amounts of treponemes in swabs being closer to biopsies than in aspirates to biopsies. Therefore, aspirates were less suitable for bacterial quantification in DD lesions compared to the other methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".