Human milk-derived extracellular vesicle treatment promotes the heat shock response in neonates with perinatal high fat diet exposure
Bibliographic record
Abstract
Abstract Maternal consumption of a high-fat diet (mHFD) during perinatal life (the collective prenatal and postnatal periods) influences neonatal development, initiates hypothalamic-pituitary-adrenal (HPA) axis activation, and impacts the long-term physiological and metabolic health of offspring. Milk-derived extracellular vesicles (MEVs) are lipid-coated nanovesicles found in mammalian milk that survive intestinal degradation and cross complex biological barriers, including the blood-brain barrier. MEVs have known cytoprotective activity in peripheral organs; however, their pro-survival functions in response to chronic pro-inflammation stemming from early life nutrient stress remain unknown in the neonatal brain. Further, sex differences resulting from MEV treatment require investigation, as male and female neonates illicit variable responses to early life nutrient stress. We investigated whether MEVs promote the heat shock response (HSR), a principal pro-survival mechanism responsible for refolding or degrading misfolded protein aggregates through the action of heat shock protein (HSP) chaperones. We investigated the interaction between MEVs and the HSR in the liver, hypothalamus, and prefrontal cortex in male and female neonatal rats exposed to perinatal mHFD within the stress hyporesponsive period at postnatal day 11. MEV treatment robustly modulated the HSR in female neonates with the largest response recorded in the prefrontal cortex. Specifically, in the prefrontal cortex, MEV treatment led to an upregulation of the main transcription factor (HSF1), while downregulating the negative regulators of HSF1 (Hsp70 and Hsp90). These results suggest that MEVs may influence pro-survival outcomes in the prefrontal cortex by activating HSF1-mediated pro-survival in a sex specific manner in response to mHFD.
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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 | 0.000 | 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 | 0.001 |
| 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".