Arsenic Exposure and Amyloid Precursor Protein Processing: A Focus onAlzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: Arsenic is present in above permissible safe limits in groundwater, soil, and food, in various areas of the world. This is increasing exposure to humankind and affecting health in various ways. Alternation in cognition is one among them. Epidemiological research has reflected the impact of arsenic exposure on children in the form of diminished cognition. AIMS: Considering this fact, the present study reviewed the impact of arsenic on amyloid precursor protein, which is known to cause one of the commonest cognitive disorders such as Alzheimer's disease. METHODS: The present study reviews the arsenic role in the generation of amyloid-beta from its precursor that leads to Alzheimer's disease through the published article from Pubmed and Scopus. DESCRIPTION: According to the findings, regular, long-term exposure to arsenic beginning in infancy changes numerous arsenic level-regulating regions in the rat brain, which are related to cognitive impairments. Arsenic also affects the BBB clearance route by increasing RAGE expression. Arsenic triggers the proamyloidogenic pathway by increasing APP expression and subsequently, its processing by β-secretase and presenilin. Arsenic also affects mitochondrial dynamics, DNA repair pathway and epigenetic changes. The mechanism behind all these changes is explained in the present review article. CONCLUSION: A raised level of arsenic exposure affects the amyloid precursor protein, a factor for the early precipitation of Alzheimer's disease.
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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 | 0.001 | 0.001 |
| 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".