Bibliographic record
Abstract
Alzheimer's disease (AD) is an insidious and progressive neurodegenerative disease, and the incidence rate is generally high with the age above 70.In 2015, the number of people with dementia worldwide reached 46.8 million, and 50-75 percent were AD.The expected number of patients with AD will reach 131 million by 2050.AD can cause serious effects, including abnormal behaviors and cognitive dysfunction.The neuropathological examination can help confirm the diagnosis of early stage.However, if daily life and social functioning are significantly impaired, it will be considered as severe symptoms.In the current stage, the treatment of AD relies on traditional medications, such as donepezil and memantine.The two most indispensable mechanisms are tau protein buildup and Amyloid-beta (Aβ) deposit, which can cause neurotoxicity and cellular decay.Although people have a certain understanding of AD, the mechanisms are not optimized yet.The existing treatment methods can only try to control the development of the disease; however, the recovery of patients is continuously being studied.In this review, a comprehensive understanding of the pathology and existing treatments can help further analyze AD and investigate the future development of treatments.It introduces a general overview of pathology including the factor of aging, hippocampal alterations, and oxidative stress.Tau proteins and Aβ are also mentioned as two portions of mechanisms.Moreover, several potential treatment options have been proposed, such as anti-amyloid therapy, monoclonal antibodies, tau-targeted therapy.iPSC and CRISPR belong to two types of future treatments that are also being tested to be effective against AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".