In Vitro Killing of Canine Urinary Tract Infection Pathogens by Ampicillin, Cephalexin, Marbofloxacin, Pradofloxacin, and Trimethoprim/Sulfamethoxazole
Bibliographic record
Abstract
Urinary tract infections are common in dogs, necessitating antimicrobial therapy. We determined the speed and extent of in vitro killing of canine urinary tract infection pathogens by five antimicrobial agents (ampicillin, cephalexin, marbofloxacin, pradofloxacin, and trimethoprim/sulfamethoxazole) following the first 3 h of drug exposure. Minimum inhibitory and mutant prevention drug concentrations were determined for each strain. In vitro killing was determined by exposing bacteria to clinically relevant drug concentrations and recording the log10 reduction and percent kill in viable cells at timed intervals. Marbofloxacin and pradofloxacin killed more bacterial cells, and faster than other agents, depending on the time of sampling and drug concentration. Significant differences were seen between drugs for killing Escherichia coli, Proteus mirabilis, Enterococcus faecalis, and Staphylococcus pseudintermedius strains. At the maximum urine drug concentrations, significantly more E. coli cells were killed by marbofloxacin than by ampicillin (p < 0.0001), cephalexin (p < 0.0001), and TMP/SMX (p < 0.0001) and by pradofloxacin than by cephalexin (p < 0.0001) and TMP/SMX (p < 0.0001), following 5 min of drug exposure. Rapid killing of bacteria should inform thinking on drug selection for short course therapy for uncomplicated UTIs, without compromising patient care, and is consistent with appropriate antimicrobial use and stewardship principles.
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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) | 0.000 | 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 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".