Experimental animal models for alzheimer disease [Alzheimer hastaliğinda deneysel hayvan modelleri]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it