Why does Human Milk Fortification (HMF) increase Urinary F2‐Isoprostanes in the Premature Infant?
Bibliographic record
Abstract
Human milk fortifiers (HMF) are routinely added to mother's milk (HM) for premature infant to make up for potential nutritional deficiencies. We reported data on 65 premature infants (birth weight < 1500 g) who were either fed HM with varying levels of HMF or formula (F) while they were in hospital receiving oral feeds. A significant increase (P<0.05) in urine_F2‐isoprostanes was found as HMF supplementation increased (Pediatr Res. 2011 Feb; 69(2): 160–4). Subsequently we used an in vitro model of Caco‐2 cells with HMF and lipopolysaccharide (LPS) known to induce inflammation in vivo. When paired with HMF, there was a significant increase in IL‐6 secretion above that of LPS alone. A sample of breast milk from our study supplemented with HMF (>; 50%) generated higher levels of intracellular oxidation in intestinal cells, as indicated by the redox‐sensitive dye Dichlorofluorescein (DCF). Furthermore we now have data from DNA Microarray experiments that, in intestinal cells, HMF significantly up regulates a battery of antioxidant response element (ARE)‐driven genes. Additionally, we have pilot data from fetal intestinal cells (FHS) exposed to either human milk (HM) or HM + HMF). Briefly, enterocyte cell cultures were exposed to digested HM +/− HMF. Following 24 hours recovery, RNA was collected, prepared for qRT‐PCR as per the SABiosciences RT2 ProfilerTM array, which screens for cytokines, chemokines, toll‐like receptors (TLRs) and linkers. Of note is the significant up‐regulation of several interleukins (IL1A, IL6, IL10, IL18) or IL18 receptor accessory protein (IL18RAP), and TLR4. Furthermore, an integrin gene (ITGB2) was significantly down‐regulated, which has implications for intestinal barrier function. This data suggests that some HMFs as constituted induce oxidative stress in vitro and in vivo . Supported by CIHR and MICH.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".