Impact of Antibiotic Treatment Intensity on Long-Term Sepsis-Associated Kidney Injury in a Polymicrobial Peritoneal Contamination and Infection Model
Bibliographic record
Abstract
BACKGROUND/AIMS: Long-term kidney affections after sepsis are poorly understood. Animal models for investigating kidney damage in the late phase of disease progression are limited. The aim of this study was to investigate the impact of two antibiotic regimes on persistence of kidney injury after peritonitis. METHODS: Kidney damage was investigated 65 days after polymicrobial peritoneal contamination and infection (PCI) sepsis induction in C57BL/6 mice. Short-term antibiotic therapy (STA, 4 days) was compared to long-term (LTA, 10 days) treatment using plasma creatinine, plasma and urine neutrophil gelatinase-associated lipocalin (NGAL), urine albumin/creatinine ratio and renal histology. RESULTS: Sepsis resulted in mortality rates of 68.2% (STA) and 61.0% (LTA). Surviving STA animals showed the most pronounced kidney damage indicated by significantly elevated levels of creatinine and acute tubular damage (ATD), whereas NGAL was significantly increased in LTA survivors only. A creatinine level above 0.3 mg/dl was used to define kidney injury, found in 21.4% of STA animals and 7.8% of LTA animals. While animals with kidney injury demonstrated significantly higher ATD scores and persistent tubular damage, no significant differences were found for plasma or urine NGAL levels or urine albumin/creatinine ratios. CONCLUSION: Prolonged antibiotic treatment reduced the rate of ongoing peritonitis-induced kidney injury in a C57BL/6 mouse model. Plasma or urine NGAL levels were not able to identify animals with or without persistent kidney injury. The kidney injury after the PCI mouse model represents prototypic clinical findings and should be used for further studies investigating disease mechanisms.
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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) | 0.001 | 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 | 0.001 |
| 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".