Experimental animal models for alzheimer disease [Alzheimer hastaliğinda deneysel hayvan modelleri]
Bibliographic record
Abstract
Alzheimer’s disease (AD) which is an age related disorder is characterized by progressive cognitive decline. Accumulation of extracellular amyloid plaques, intracellular neurofibrillary tangles and neuronal loss is observed in AD brain. Exploring the neuropathology of AD in human pre-clinical stages is not easy. Mechanisms which causes AD in preclinical stage and potential new therapeutic targets are understood by transgenic animal models. McGill-R-Thy1-APP rat model is the only model to reproduce AD-like amyloid pathology with a single transgene. Aβ has been the most cited probable causative factor in the onset and progression of AD. In Aβ-based rodent models, there is a significant correlation between increased Aβ levels and cognitive decline. Risk genes in the development of AD are amiloid precursor protein (APP), presenilin-1 (PS-1), presenilin-2 (PS-2) which contain autosomal dominant mutations. In rat models of Tau pathology, transgenic rats which overexpress mutant APP/PS1 display increased Tau alterations in the brain. Injection of Aβ into rat brain is an alternative AD animal model to the use of transgenic animals. In this model, after Aβ1-42 is injected into the CA3 region of hippocampus, progressive decline in behavioral responses are observed. Despite the large variety of therapeutic approaches, AD remains incurable. Because, in clinical diagnosis stage, the brain has suffered irreversible and extensive damage. © 2018, Nobelmedicus. All rights reserved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".