Adding Enriched Eggs in Ready-to-use foods Improve Recovery Rate in Malnourished Rats
Bibliographic record
Abstract
Ready-to-use foods (RUFs) using indigenous sources in developing countries is highly required to treat moderate acute malnutrition (MAM). However, incorporating an animal protein may affect their effectiveness. Thus, two local RUFs were produced without (LF-1) and with eggs (LF-2). The objective of this study was to assess and compare to Plumpy'Sup (PS), the impact of adding enriched eggs in cashew/soy/rice based RUF on the proximate composition, growth and blood biochemical parameters in malnourished Wistar rats by Anagobaka diet. Proximate composition revealed that, with the exception of fiber and ash contents, the two RUFs recorded protein, lipid, carbohydrate and energy values globally comparable to PS. They also met WFP's recommendations for foods to treat MAM. Results of growth parameters show that Anagobaka diet leads to the installation of a moderate emaciation, confirmed by an average weight loss of -17 %. Moreover, recovery diets showed higher weight gain and good palatability (DMI, TPI, FER and PER) in rats fed with PS followed by those fed with LF-2 and LF-1. For the serum biochemical parameters, the rats fed with LF-2 had on the whole a better functioning of blood metabolites (glucose, total proteins, albumin, urea, creatinine, ASAT, ALAT) as well as a better accumulation of blood lipids (total cholesterol, HDL-cholesterol, LDL-cholesterol and triglycerides) than those of rats fed with PS and LF-1. In conclusion, local RUFs which include enriched eggs present the best nutritional profile to treat MAM in Côte d'Ivoire but to sustain recovery a mineral supplementation will be needed.
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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".