Metabolic changes in plasma from the umbilical cord of small for gestational age babies and from a rat model of placental insufficiency
Bibliographic record
Abstract
Small for gestational age (SGA) can have lifelong consequences. Placental dysfunction is implicated in its pathophysiology. A reduced uterine perfusion pressure (RUPP) rat model can create an in vivo model of placental insufficiency. Metabolomics is the holistic study of the basic biochemistry within a biological system. The authors aimed to examine the metabolomic differences in (1) venous cord blood (VCB) plasma between SGA babies and normal controls and (2) plasma from the RUPP rat. Cord blood was collected from normally grown babies, and babies with confirmed SGA (n=7–8). Blood was also collected from RUPP, sham operated and control rats (n=7–9). All samples sets were analysed using Ultra Performance Liquid Chromatography/LTQ-Orbitrap Mass Spectrometry. In VCB, over 1700 metabolite features were detected, of which 900 (52%) showed significant difference between SGA and normally grown babies (p<0.05). Multivariate data analysis (Canonical Variates Analysis – CVA) showed that the metabolic profiles of normal and SGA samples were clearly different. Chemical identity of the metabolites will be elucidated fully but there was a preponderance of phospholipids and vitamin D derivatives. In rat plasma, there were 3825 metabolite features detected. 712 of these features had a p value <0.05. Multivariate data analysis showed that metabolite differences between normal and RUPP samples were highly correlated. Carnitine and phosphocholine metabolism were of notable interest. This study has shown metabolomic differences in VCB plasma between SGA babies and normal controls as well as in an animal model of placental insufficiency. Both studies may lead to a closer understanding of the aetiology of SGA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".