DOES THE CO-ADMINISTRATION OF PARENTERAL MULTIVITAMINS WITH LIPID EMULSION PROTECT PRETERM INFANTS AGAINST OXIDANT STRESS?
Bibliographic record
Abstract
Objective: The co-administration of parenteral multivitamins (MVP) with lipid emulsion (LIP) is recommend to prevent lipid peroxidation and enhance antioxidant vitamin bioavailability. The biological effects of this modality have not been studied in neonates. The aim was to test the hypothesis that this total parenteral nutrition (TPN) modality offers a better protection against oxidant stress. Methods: Three groups of preterm infants were randomly assigned to receive: C = amino acids (AA) + MVP exposed to light: MVP + AA, lipids provided separately (n = 10); LE = LIP + MVP exposed to light: MVP administered with lipids, amino acids provided separately (n = 10); LP = LIP + MVP protected from light (n = 10). Blood was sampled on day 7 to measure the redox ratio of glutathione GSSG/(GSSG + GSH) and plasma levels (μM/l) of vitamins A and E. Data (mean ± SEM) were compared by analysis of variance for babies on low (⩽0.25) versus high (>0.25) FiO2. Results: Clinical characteristics and nutrient intakes were similar between groups. In infants receiving C, the redox ratio was more oxidised (p Conclusions: LE protects against the oxidant stress associated with O2 supplementation. This is not explained by the availability of antioxidant vitamins A and E. These data support a trial to evaluate in premature infants the impact of TPN modality on long-term outcomes.
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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 | 0.001 |
| 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 | 0.001 | 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".