Milk-derived extracellular vesicles mitigate NF-κB pathway and NLRP3 inflammasome formation in Long Evans neonates
Bibliographic record
Abstract
Abstract Exposure to a maternal high fat diet (HFD) during perinatal (prenatal and postnatal combined) life increases offspring’s risk of developing metabolic diseases (obesity, type II diabetes and hypertension), impairs immunity, behaviour, and neurodevelopment. Exclusive breast/chest milk feeding is a potential solution to reduce the negative developmental effects of HFD, mainly chronic systemic pro-inflammation. This study focuses on analyzing anti-inflammatory effects of a group of biological nanovesicles found in human milk, entitled milk-derived extracellular vesicles (MEVs). Specifically, we characterized the modulation of the nuclear factor κB (NF-κB) signaling pathway and NLR family pyrin domain containing 3 (NLRP3) inflammasome formation by MEVs in male and female neonatal rats with perinatal HFD exposure in the liver and hypothalamus. Female Long Evans dams were placed on a HFD or a control diet (CHD), with matching sucrose levels, 4 weeks before breeding and remained on the diets through gestation and lactation. HFD and CHD offspring received human MEVs through oral gavage twice a day from postnatal day (PND) 4 to 11. Transcript and protein abundance of candidate targets in the NF-κB signaling pathway and NLRP3 inflammasome were measured by quantitative reverse transcription polymerase chain reaction (RT-qPCR) and western immunoblotting, respectively. Our results indicate that MEV treatment attenuates the activation of NF-κB pathway and NLRP3 inflammasome formation at critical checkpoints, in males and females with perinatal HFD exposure in liver and the hypothalamus. Taken together, our data suggests that MEVs may elicit anti-inflammatory benefits postnatally that mitigates gestational HFD exposure.
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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".