Effects of silica nanoparticles on endolysosome function in primary cultured neurons
Bibliographic record
Abstract
Silica nanoparticles (SiNPs) have been used as vehicles for drug delivery, molecular detection, and cellular manipulations in nanoneuromedicine. SiNPs may cause adverse effects in the brain including neurotoxicity, neuroinflammation, neurodegeneration, and enhancing levels of amyloid beta (Aβ) protein—all pathological hallmarks of Alzheimer’s disease. Therefore, the extent to which SiNPs influence Aβ generation and the underlying mechanisms by which this occurs deserve investigation. Our studies were focused on the effects of SiNPs on endolysosomes which uptake, traffic, and mediate the actions of SiNPs. These organelles are also where amyloidogenesis largely originates. We found that SiNPs, in primary cultured hippocampal neurons, accumulated in endolysosomes and caused a rapid and persistent deacidification of endolysosomes. SiNPs significantly reduced endolysosome calcium stores as indicated by a significant reduction in the ability of the lysosomotropic agent glycyl-l-phenylalanine 2-naphthylamide (GPN) to release calcium from endolysosomes. SiNPs increased Aβ1–40 secretion, whereas 2 agents that acidified endolysosomes, ML-SA1 and CGS21680, blocked SiNP-induced deacidification and increased generation of Aβ1–40. Our findings suggest that SiNP-induced deacidification of and calcium release from endolysosomes might be mechanistically linked to increased amyloidogenesis. The use of SiNPs might not be the best nanomaterial for therapeutic strategies against Alzheimer’s disease and other neurological disorders linked to endolysosome dysfunction.
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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) | 0.000 | 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".