A vegetable fat-based diet delays psychomotor and cognitive development compared with maternal dairy fat intake in infant gray mouse lemurs
Bibliographic record
Abstract
Dairy fat has a unique lipid profile; it is rich in short- and medium-chain saturated fatty acids that induce ketone production and has a balanced ω6/ω3 ratio that promotes cognitive development in early life. Moreover, the high consumption of vegetable oils in pregnant and lactating women raises concerns regarding the quality of lipids provided to offspring. Here, we investigate maternal dairy fat intake during gestation and lactation in a highly valuable primate model for infant nutritional studies, the gray mouse lemur (Microcebus murinus). Two experimental diets are provided to gestant mouse lemurs: a dairy fat-based (DF) or vegetable fat-based diet (VF). The psychomotor performance of neonates is tested during their first 30 days. Across all tasks, we observe more successful neonates born to mothers fed a DF diet. A greater rate of falls is observed in 8-day-old VF neonates, which is associated with delayed psychomotor development. Our findings suggest the potential benefits of lipids originating from a lactovegetarian diet compared with those originating from a vegan diet for the psychomotor development of neonates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".