CRISPR/Cas9 gene editing: New hope for Alzheimer's disease therapeutics
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is an insidious, irreversible, and progressive neurodegenerative health condition manifesting as cognitive deficits and amyloid beta (Aβ) plaques and neurofibrillary tangles. Approximately 50 million individuals are affected by AD, and the number is rapidly increasing globally. This review explores the role of CRISPR/Cas9 gene editing in the management of AD and its clinical manifestations. AIM OF REVIEW: This review aims to provide a deep insight into the recent progress in CRISPR/Cas9-mediated genome editing and its use against neurodegenerative disorders, specifically AD. However, we have referred to its use against parkinsons's disease (PD), Huntington's disease (HD), and other human diseases, as is one of the most promising and emerging technologies for disease treatment. KEY SCIENTIFIC CONCEPTS OF REVIEW: The pathophysiology of AD is known to be linked with gene mutations, that is, presenilin (PSEN) and amyloid beta precursor protein (APP). However, clinical trials focused at the genetic level could not meet the desired efficiency. The CRISPR/Cas9 genome editing tool is one of the most powerful technologies for correcting inconsistent genetic signatures and now extensively used for AD management. It has significant potential for the correction of undesired gene mutations associated with AD. This technology has allowed the development of empirical AD models, therapeutic lines, and diagnostic approaches for better understanding the nervous system, from in vitro to in vivo models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".