Factors Influencing <i>Escherichia coli</i> and <i>Enterococcus durans</i> Growth in Parenteral Nutrition With and Without Lipid Emulsion to Inform Maximum Duration of Infusion Policy Decisions
Bibliographic record
Abstract
BACKGROUND: Recommendations effectively restrict the infusion duration of lipid-containing parenteral nutrition (PN) from a single bag, purportedly because it encourages growth of potential microbial contaminants more than lipid-free PN. Since other variables, including osmolarity, may independently affect microbial growth, this study examined variables affecting growth of Escherichia coli and Enterococcus durans in PN infusates. MATERIALS AND METHODS: Growth of E coli and E durans was assessed in quadruplicate in 12 different PN infusates, with and without lipid, in varying glucose concentrations. RESULTS: Results are presented as mean log10 colony-forming units (cfu)/mL ± SEM at 48 hours. The log10cfu/mL of both E coli and E durans in PN increased considerably after adjustment for baseline log10cfu/mL and pH, from 1.093 to 2.241 (P < .001) and from 0.843 to 3.451 (P < .001) respectively. Growth of each microorganism was independently increased by lipid inclusion, or increasing the proportion of nonnitrogen energy from lipid, and reduced by raising the glucose concentration or energy density. Increasing the osmolarity of lipid-PN with glucose or sodium chloride reduced growth but only significantly for sodium chloride (E coli, P = .025; E durans, P = .045). Induced changes in pH affected the growth of the 2 organisms differently. CONCLUSION: The presence of lipid and an increasing proportion of energy from lipid in PN favored the growth of E coli and E durans. Osmolarity changes and the nutrient type causing these changes independently affect the growth of these microbes. Each effect needs to be considered when establishing guidelines based on the growth of potential contaminants in different types of PN.
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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 | 0.004 |
| 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".