Development of Renal Dysfunction in an Infant Model of Polymicrobial Sepsis
Bibliographic record
Abstract
Sepsis‐induced acute kidney injury increases mortality from 25% to nearly 45% in infants. We used a clinically relevant cecal ligation and puncture (CLP) sepsis model in rat pups 17–19 days old (developmentally comparable to a 5 to 6‐month‐old human infant) to characterize the development of renal dysfunction. Renal injury was assessed using intravital video microscopy (IVVM), blood urea nitrogen (BUN), and glomerular filtration rate (GFR) at 4, 6, and 18h following CLP. Temperature was measured as an indicator of Systemic Inflammatory Response Syndrome. Temperature was decreased as early as 6h following CLP and remained decreased through 18h. Lactate, an indicator of general organ hypoperfusion and hypoxia, was nearly doubled as early as 4h following CLP and remained elevated through 18h. IVVM analysis of the renal microcirculation showed a significant decrease in the percentage of continuously flowing capillaries (72% to 36%) and an increase in the percentage of capillaries with no flow (2% to 26%) at 6h following CLP that persisted through 18h. Clinically, measurement of BUN is used to assess renal function when GFR is difficult to measure. In this model, BUN was significantly increased by 4h following CLP and was increased even further by 18h. GFR is the gold standard measurement of renal function and it was measured using non‐invasive transcutaneous measurements of sinistrin clearance in conscious rat pups. GFR was decreased 50% as early as 4h following CLP and remained decreased through 18h. These findings show that this model of polymicrobial sepsis produces early and sustained renal dysfunction suggesting it could be used in preclinical studies to evaluate new targeted therapies for sepsis‐induced renal injury in infants. Support or Funding Information CRS supported by T32 GM106999, F31DK104533, AHA PRE20450050; PRM supported by AHA 15GRNT2508025 and UAMS Medical Research Foundation; PCG supported by ASPET Institutional SURF Award 2015.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".