<i>In vitro</i> killing of canine strains of <i>Staphylococcus pseudintermedius</i> and <i>Escherichia coli</i> by cefazolin, cefovecin, doxycycline and pradofloxacin over a range of bacterial densities
Bibliographic record
Abstract
Background Bacterial densities likely fluctuate during infection and may exceed the bacterial density used in susceptibility testing. As such, investigation of bacterial killing by antibiotics over a range of varying bacterial densities may provide important differences between compounds and could impact drug selection for therapy. Hypothesis/Objectives To measure killing of clinical isolates of Staphylococcus pseudintermedius and Escherichia coli by cefazolin, cefovecin, doxycycline and pradofloxacin at clinically relevant (minimum inhibitory, mutant prevention, maximum serum and maximum tissue) drug concentrations against varying densities of bacteria. Animals/Materials Bacterial strains collected from dogs with urinary tract infections were studied. Methods and materials High bacterial densities ranging from 10 6 to 10 9 colony forming units (cfu)/mL were exposed to minimum inhibitory, mutant prevention, blood and tissue drug concentrations, and the percentages (log 10 ) of viable cells killed following 30 min, 1, 2, 4, 6, 12 and 24 h of drug exposure were quantified. Results Doxycycline exhibited bacteriostatic properties with less killing than the other three agents. For example, at a 10 7 cfu/mL density of S. pseudintermedius , more cells were killed by pradofloxacin ( P < 0.0001) and cefovecin ( P = 0.0014) but not cefazolin when compared to doxycycline at the maximum serum drug concentration following 12 h of drug exposure. Conclusions and clinical importance Differences were seen between some drugs in the speed and extent of bacterial killing; this could be clinically important and may impact drug selection and length of therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".