Comparative <i>in vitro</i> killing of canine strains of <i>Staphylococcus pseudintermedius</i> and <i>Escherichia coli</i> by cefovecin, cefazolin, doxycycline and pradofloxacin
Bibliographic record
Abstract
BACKGROUND: Bacterial eradication is necessary for clinical cure of infections and antimicrobial agents are important adjunctive therapies for inhibiting the growth of or killing bacteria. Pre-existing skin diseases predispose animals to infection by Staphylococcus pseudintermedius and, more rarely, by Gram-negative bacilli. The property of rapid killing of bacteria may influence drug selection and duration of therapy in the setting of infection. OBJECTIVES: To test the killing of canine isolates of S. pseudintermedius and Escherichia coli by cefazolin, cefovecin, doxycycline and pradofloxacin at the minimum inhibitory, mutant prevention, maximum serum and maximum tissue drug concentrations. METHODS: Under standard conditions, bacterial cells were exposed to clinically relevant drug concentrations in vitro and the log10 reduction (and % kill) of viable cells measured at 5, 10, 15, 20, 25, 30, 60, 120 and 180 min after drug exposure. RESULTS: Statistically significant differences were seen between killing efficiencies by pradofloxacin versus the other agents, whereby pradofloxacin killed cells more rapidly than the others. For example, against the S. pseudintermedius strains, significantly more cells were killed by pradofloxacin following 15 min of maximum tissue drug concentration exposure than for cefazolin (P = 0.0002), cefovecin (P = 0.0007) and doxycycline (P ≤ 0.0001). CONCLUSION AND CLINICAL IMPORTANCE: The rank order of potency based on these kill experiments was pradofloxacin > cefazolin > cefovecin > doxycycline. Rapid killing of bacteria affects the speed of clinical resolution and may influence drug selection and duration of therapy for skin infections.
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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 | 0.001 |
| 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".