Human milk-derived extracellular vesicles promote the heat shock response in polarized microglia
Bibliographic record
Abstract
Abstract Proteotoxic stress induces microglia polarization and attenuates cytoprotective, pro-survival cellular cascades. Milk-derived extracellular vesicles (MEVs) are lipid-coated nanovesicles that combat pro-inflammation in peripheral cells and tissues; however, the cytoprotective potential of MEVs remains unknown in brain macrophages. We investigated whether MEVs reduce neuroinflammation in human microglia by activating the heat shock response (HSR). The HSR triggers the upregulation of molecular chaperones (heat shock proteins; HSPs) to restore proteostasis by refolding or degrading misfolded aggregates. MEVs were isolated from unpasteurized human donor milk. Human microglia clone 3 (HMC3) cells were primed with 10 ng/mL IFN-γ to induce polarization, and a subset of cells were supplemented with 200 µg of MEVs. The abundance of HSF1 and candidate HSPs (Hsp70, Hsp90, Hsp40, Hsp27) were analyzed via RT-qPCR and western immunoblotting at 6h, 12h, and 24h post-MEV supplementation. We found that MEV supplementation promoted the HSR in polarized microglia, compared to homeostatic cells. Furthermore, MEVs increased the duration of the HSR in response to pro-inflammatory stress, exerting robust and continued pro-survival benefits.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".