Alzheimer’s Disease: A Journey from Amyloid Peptides and Oxidative Stress, to Biomarker Technologies and Disease Prevention Strategies—Gains from AIBL and DIAN Cohort Studies
Bibliographic record
Abstract
Worldwide there are over 46 million people living with dementia, and this number is expected to double every 20 years reaching about 131 million by 2050. The cost to the community and government health systems, as well as the stress on families and carers is incalculable. Over three decades of research into this disease have been undertaken by several research groups in Australia, including work by our original research group in Western Australia which was involved in the discovery and sequencing of the amyloid-β peptide (also known as Aβ or A4 peptide) extracted from cerebral amyloid plaques. This review discusses the journey from the discovery of the Aβ peptide in Alzheimer's disease (AD) brain to the establishment of pre-clinical AD using PET amyloid tracers, a method now serving as the gold standard for developing peripheral diagnostic approaches in the blood and the eye. The latter developments for early diagnosis have been largely achieved through the establishment of the Australian Imaging Biomarker and Lifestyle research group that has followed 1,100 Australians for 11 years. AIBL has also been instrumental in providing insight into the role of the major genetic risk factor apolipoprotein E ɛ4, as well as better understanding the role of lifestyle factors particularly diet, physical activity and sleep to cognitive decline and the accumulation of cerebral Aβ.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".