Significance of cytosolic cathepsin D in Alzheimer's disease pathology: Protective cellular effects of PLGA nanoparticles against β‐amyloid‐toxicity
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that amyloid β (Aβ) peptides play an important role in the degeneration of neurons during the development of Alzheimer's disease (AD), the prevalent cause of dementia affecting the elderly. The endosomal-lysosomal system, which acts as a major site for Aβ metabolism, has been shown to exhibit abnormalities in vulnerable neurons of the AD brain, reflected by enhanced levels/expression of lysosomal enzymes including cathepsin D (CatD). At present, the implication of CatD in selective neuronal vulnerability in AD pathology remains unclear. METHODS: We evaluated the role of CatD in the degeneration of neurons in Aβ-treated cultures, transgenic AD mouse model (that is 5xFAD) and post mortem AD brain samples. RESULTS: -induced toxicity in cortical cultured neurons is associated with impaired lysosomal integrity, enhanced levels of carbonylated proteins and tau phosphorylation. The cellular and cytosolic levels/activity of CatD are also elevated in cultured neurons following exposure to Aβ peptide. Additionally, we observed that CatD cellular and subcellular levels/activity are increased in the affected cortex, but not in the unaffected cerebellum, of 5xFAD mice and post mortem AD brains. Interestingly, treatment of cultured neurons with nanoparticles PLGA, which targets lysosomal system, attenuated Aβ toxicity by reducing the levels of carbonylated proteins, tau phosphorylation and the level/distribution/activity of CatD. CONCLUSION: Our study reveals that increased cytosolic level/activity of CatD play an important role in determining neuronal vulnerability in AD. Additionally, native PLGA can protect neurons against Aβ toxicity by restoring lysosomal membrane integrity, thus signifying its implication in attenuating AD.
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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".