Inflammation in the Niemann‐Pick type‐C brain
Bibliographic record
Abstract
Niemann‐Pick Type‐C (NPC) disease is a fatal neurodegenerative disorder characterized by cholesterol accumulation in late endosomes/lysosomes. Microglia are resident immune cells of the central nervous system, which upon activation secrete potentially neurotoxic molecules such as tumor necrosis factor‐alpha (TNF‐α). Inappropriate activation of microglia has been implicated in several neurodegenerative disorders including NPC disease. We hypothesize that microglia play a primary role in neurodegeneration in NPC disease. To test this hypothesis, primary glia and microglia cultures were prepared from Npc1‐/‐ mice. Filipin staining of unesterified cholesterol shows that NPC1‐deficient microglia accumulate cholesterol. Additionally, treatment of wild‐type microglia with U18666A, a compound which mimics the cholesterol accumulation in NPC disease, causes microglia to assume an activated morphology. The TNF‐α content of conditioned media from Npc1‐/‐ glia was also higher than that of Npc1+/+ glia. Taken together, these results suggest that cholesterol accumulation activates microglia and increases the secretion of potentially toxic molecules, such as TNF‐α. Furthermore, elevated levels of TNF‐Receptor‐1, the TNF‐α receptor involved in apoptosis, were observed in Npc1‐/‐ brain regions such as the cerebellum. We also found that elimination of the Fc‐receptor common gamma chain, required for antibody‐based autoimmunity, in Npc1‐/‐ mice did not improve the clinical outcome of the disease. This observation is consistent with the idea that cholesterol accumulation causes microglial dysfunction, rather than the production of autoantibodies leading to an autoimmune response. Research Support: NSERC and CIHR
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".