Effect of an α-Tocopherol-Containing Antioxidant Parenteral Emulsion upon Gut Microbiota in Preterm Infants
Bibliographic record
Abstract
Preterm babies are born with an immature intestine and are at higher risk for intestinal failure and needing for parenteral nutrition than full-term newborns. These babies show an altered intestinal microbiota, which may results of key importance for later health. However, there is a lack of information on effect of parenteral formulas upon the establishing infant microbiota. Moreover, the microbiota alterations present in preterm newborns have been related with an altered redox-potential which hinders the establishment of strict anaerobes. Thus, interventions aimed at reducing the oxidative stress in these infants, including those under parenteral nutrition, could also affect the intestinal microbiota composition. We assessed the effect upon the gut microbiota of the administration during 30 days of a lipid emulsion, containing ω3 polyunsaturated fatty acids and α-tocopherol, or a control emulsion containing soybean-bases lipids to parenterally-fed preterm newborns. In spite of its high antioxidant potential, the infants receiving the experimental fat emulsion showed a trend to increase of facultative anaerobes such as enterobacteria and specifically of Klebsiella. This study stresses the need of further work monitoring the impact of early-life parenteral nutrition on the establishment of the intestinal microbiota.
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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 | 0.001 |
| 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 | 0.000 | 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".