Lipopolysaccharide-QD Micelles Induce Marked Induction of TLR2 and Lipid Droplet Accumulation in Olfactory Bulb Microglia
Bibliographic record
Abstract
The intranasal entry of biological and artificial nanoparticles can induce inflammatory responses both locally and more widely in surrounding tissues. The aim of this study was to assess the microglia activation induced by nanoparticles with different surfaces in (i) a transgenic mouse (Toll-like receptor (TLR)-2-luciferase (Luc) reporter) which allowed the biophotonic imaging of microglial activation/innate immune response after intranasal delivery of nanoparticles and (ii) in microglial dispersed cells in vitro. Cadmium selenide nanoparticles (quantum dots, QD), surface-exchanged with lipopolysaccharide (LPS) to form micelles, were tested to assess microglia activation and lipid droplet formation in both model systems. In vivo imaging revealed a robust increase in the extent of microglial activation/TLR2 response, initially in the olfactory bulb, but also in other more caudal brain regions. The increased TLR2 expression was complemented with enhanced CD68 expression in activated microglia in the same regions. Intense in vitro microglial activation by LPS-QD micelles was accompanied by a significant enhancement of nitric oxide production and formation of large lipid droplets, suggesting the possibility of this organelle acting as an inflammatory biomarker in response to nanoparticles, and not simply as a storage site in fat tissues.
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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 | 0.000 |
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".