Altered levels and distribution of amyloid precursor protein and its processing enzymes in Niemann‐Pick type C1‐deficient mouse brains
Bibliographic record
Abstract
Niemann-Pick type C (NPC) disease is an autosomal recessive neurodegenerative disorder characterized by intracellular accumulation of cholesterol and glycosphingolipids in many tissues including the brain. The disease is caused by mutations of either NPC1 or NPC2 gene and is accompanied by a severe loss of neurons in the cerebellum, but not in the hippocampus. NPC pathology exhibits some similarities with Alzheimer's disease, including increased levels of amyloid beta (Abeta)-related peptides in vulnerable brain regions, but very little is known about the expression of amyloid precursor protein (APP) or APP secretases in NPC disease. In this article, we evaluated age-related alterations in the level/distribution of APP and its processing enzymes, beta- and gamma-secretases, in the hippocampus and cerebellum of Npc1(-/-) mice, a well-established model of NPC pathology. Our results show that levels and expression of APP and beta-secretase are elevated in the cerebellum prior to changes in the hippocampus, whereas gamma-secretase components are enhanced in both brain regions at the same time in Npc1(-/-) mice. Interestingly, a subset of reactive astrocytes in Npc1(-/-) mouse brains expresses high levels of APP as well as beta- and gamma-secretase components. Additionally, the activity of beta-secretase is enhanced in both the hippocampus and cerebellum of Npc1(-/-) mice at all ages, while the level of C-terminal APP fragments is increased in the cerebellum of 10-week-old Npc1(-/-) mice. These results, taken together, suggest that increased level and processing of APP may be associated with the development of pathology and/or degenerative events observed in Npc1(-/-) mouse brains.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".