Quantum dot conjugated saporin activates microglia and induces selective substantia nigra degeneration
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by profound microglial driven inflammatory processes and the loss of dopamine neurons of the substantia nigra (SNc). Both microglia and dopamine neurons that are affected in the SNc are particularly vulnerable to environmental toxicants and finding more selective ways of targeting these cell types is of importance. Quantum dots (QDs) might be a useful vehicle for selectively delivering toxicants to microglia and owing to their fluorescent capability, they can be microscopically tracked within the cell. Accordingly, we assessed the impact of QDs alone and QDs conjugated to the ribosomal toxin, saporin, upon SNc microglia and dopamine neurons. We found that intra-SNc infused QDs selectively entered microglia and induced morphological changes consistent with an activated state. QDs conjugated to saporin also caused a significant loss of dopamine neurons and motor coordination (on a rotarod test) deficits, along with an increase in the inflammatory microglial actin regulatory factors, WAVE2. These data suggest that QDs might be a viable route for toxicant delivery and also has an added advantage of being fluorescently visible. Ultimately, we found SNc neurons to be exceptionally vulnerable to QD-saporin and suggest that this could be a novel targeted approach to model PD-like inflammatory pathology.
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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".