Comparative genomic analyses of Klebsiella pneumoniae K57 capsule serotypes isolated from bovine mastitis in China
Bibliographic record
Abstract
Klebsiella pneumoniae can cause severe clinical mastitis in dairy cows, with K. pneumoniae type K57 (K57-KP) being the most common capsular serotype. To identify virulence factors and antimicrobial-resistance (AMR) genes of K57-KP with varying virulence, Galleria mellonella (greater wax moth) larvae were infected as a screening model to characterize virulence of 90 K57-KP strains, with 10 and 11 strains defined as virulent or attenuated, respectively, based on larval survival rates. Next, virulence of these 21 isolates was subsequently confirmed in adhesion and lactate dehydrogenase release assays, using bovine mammary epithelial cells cultured in vitro. Finally, genes associated with virulence and AMR were characterize with whole-genome sequencing. These 21 K57-KP strains were designated into 16 sequence types based on multi-locus sequence typing and allocated in phylogenetic analysis based on single nucleotide polymorphisms. We found great genetic diversity among isolates. In addition, adhesion-associated genes (e.g., fimA, sfaA, and focA) aminoglycoside-resistance genes (aph(6)-Id, strAB) were associated with virulence. This study provided new knowledge regarding virulence of K57-KP associated with bovine mastitis, which may inform development of novel diagnostic tools and prevention strategies for bovine mastitis.
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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 | 0.001 | 0.002 |
| 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".