Influence of Peritoneal Dialysis Solution Type and Strength on Bacterial Growth
Bibliographic record
Abstract
Background: The role of solution type and strength on peritonitis risk is unclear in patients receiving peritoneal dialysis (PD). We evaluated the in vitro growth characteristics (inhibition or promotion) of common peritonitis bacterial species in various PD solution types. Methods: Common bacterial pathogens including: Staphylococcus aureus (SA), Staphylococcus epidermidis (SE), Pseudomonas aeruginosa (PA), and Enterococcus faecalis (EF) were cultured in a compatible growth medium overnight (Brain heart infusion, Luria-Bertani broth, or Todd Hewitt broth). An aliquot from the cultures was grown in a 1:1 combination of growth media (control- CON) + PD solution (Baxter: 1.5%, 2.5%, 4.25% dextrose (DEX), and 7.5% icodextrin (ICO)). All cultures were incubated in glass tubes aerobically for 16 h and 20 h at 37 °C; growth at 16h is reported due to minimal increase after that. Bacterial concentrations were measured by optical density of each sample at 600 nm (OD600) and a mean of three independent experiments were taken. The error bars represent the standard deviations. Results: Compared to control, PA growth was most robust in combined growth media and PD fluid (Figure: growth in PD fluid to growth medium 1:1 mixture at 16h). In particular, PA underwent stronger growth in DEX (p <0.001 DEX vs CON) and grew less in ICO versus all DEX (p = 0.002). In contrast, SA had greater growth in ICO versus all DEX (p < 0.001). EF demonstrated the weakest growth in growth medium + PD fluid. Conclusions: Common PD-associated bacteria appear to grow similarly in routine PD fluids with the most favorable growth environment for SE. When PD fluids are combined with growth media to better simulate physiologic conditions, ICO limits PA and DEX limits SA growth. Further research is warranted in clinical settings.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".