Pandrug-resistant isolate of Klebsiella pneumoniae causes less damage than drug-susceptible isolates in a rabbit model
Bibliographic record
Abstract
PURPOSE: Bacterial infections induce a series of inflammatory responses and lead to longer hospital stays and increased mortality. In clinical work, we often find that infections caused by drug-susceptible isolates have a worse outcome than those caused by pandrug-resistant isolates. To goal of this study was to assess the impact of drug resistant in a rabbit model of Klebsiella pneumoniae infection. METHODS: This study used a rabbit model of experimental bacteremia, challenged by susceptible (A), multidrug-resistant (B) and pandrug-resistant (C) isolates of Klebsiella pneumoniae. Mimimal inhibitory concentrations (MIC), leukocyte, TNFα, IL-17, and HMGB-1 levels and survival times were measured. RESULTS: Mean survival times after challenge by isolates A, B and C were 10.5 ± 3.63, 12.7 ± 2.31 and 13.9 ± 0.32 days, respectively. Leukocytes levels after challenge with isolate C were lower compared with those after challenge with isolate A (p = 0.002). Blood counts of the offending pathogens and concentrations of TNFα, IL-17, and HMGB-1 were higher in the group challenged by isolate A in comparison with isolate B or C. Tissue bacterial load after animal death was significantly higher in rabbits of group A in comparison with isolates B and C. CONCLUSION: Bacteremia induced by pandrug-resistant isolates is accompanied by less damage compared with bacteremia by drug-susceptible isolates. Rabbits infected with a pandrug-resistant isolate of K. pneumoniae survived longer and had a lower inflammatory response than did animals infected with drug-susceptible isolates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".