Impact of Treatment with RUTF on Plasma Lipid Profiles of Severely Malnourished Pakistani Children
Bibliographic record
Abstract
(1) Background: Little is known on impacts of ready-to-use therapeutic food (RUTF) treatment on lipid metabolism in children with severe acute malnutrition (SAM). (2) Methods: We analyzed glycerophospholipid fatty acids (FA) and polar lipids in plasma of 41 Pakistani children with SAM before and after 3 months of RUTF treatment using gas chromatography and flow-injection analysis tandem mass spectrometry, respectively. Statistical analysis was performed using univariate, multivariate tests and evaluated for the impact of age, sex, breastfeeding status, hemoglobin, and anthropometry. (3) Results: Essential fatty acid (EFA) depletion at baseline was corrected by RUTF treatment which increased EFA. In addition, long-chain polyunsaturated fatty acids (LC-PUFA) and the ratio of arachidonic acid (AA)/linoleic acid increased reflecting greater EFA conversion to LC-PUFA, whereas Mead acid/AA decreased. Among phospholipids, lysophosphatidylcholines (lyso.PC) were most impacted by treatment; in particular, saturated lyso.PC decreased. Higher child age and breastfeeding were associated with great decrease in total saturated FA (ΣSFA) and lesser decrease in monounsaturated FA and total phosphatidylcholines (ΣPC). Conclusions: RUTF treatment improves EFA deficiency in SAM, appears to enhance EFA conversion to biologically active LC-PUFA, and reduces lipolysis reflected in decreased ΣSFA and saturated lyso.PC. Child age and breastfeeding modify treatment-induced changes in ΣSFA and ΣPC.
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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".