Klebsiella pneumoniae isolated from bovine mastitis is cytopathogenic for bovine mammary epithelial cells
Bibliographic record
Abstract
Klebsiella pneumoniae, a common cause of clinical mastitis (CM) in dairy cows, can cause severe clinical symptoms. However, its pathogenicity in the bovine mammary gland is not well understood. Our objectives were to establish an in vitro infection model of K. pneumoniae on bovine mammary epithelial cells (bMEC) to assess (1) cytopathogenicity (adhesive and invasive ability, damage and apoptosis, pro-inflammatory effects) of K. pneumoniae on bMEC and (2) the role of hypermucoviscous (HMV) phenotype on cytopathogenicity. Two K. pneumoniae isolates from CM cows, 1 HMV and 1 non-HMV, were used to infect bMEC. Adhesion and invasion ability, release of lactate dehydrogenase (LDH), ultrastructural morphology, apoptosis, transcriptional expression of pro-inflammatory genes and production of pro-inflammatory cytokines were characterized at various intervals. Both K. pneumoniae isolates rapidly adhered to and invaded bMEC within 1 h post infection (pi), causing ultrastructural damage (swelling of mitochondria and vesicle formation on cell surface) after 3 h pi and apoptotic death after 9 h pi. In addition, K. pneumoniae promoted transcriptional expression of pro-inflammatory genes IL-6, IL-8, IL-1β, and tumor necrosis factor (TNF)-α and production of IL-8, IL-1β, and TNF-α cytokines. Compared with non-HMV K. pneumoniae, the HMV isolate had lower adhesive and invasive abilities but caused more serious cellular damage. In conclusion, K. pneumoniae was cytopathogenic on bMEC and induced a pro-inflammatory response; however, the HMV phenotype did not have a key role in pathogenicity. Therefore, more attention should be paid to milk loss, and targeted prevention and treatment strategies should be implemented in Klebsiella mastitis episodes.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".