Presenilin 1 gene mutations: a potential marker to help diagnose early onset Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease is a prevalent neurodegenerative disorder that affects millions of people worldwide with a substantial healthcare expenditure of almost $500 billion dollars. Alzheimer’s is primarily considered a disease of the aging population, with 95% of cases being diagnosed in patients over 65. Alzheimer’s in patients under 65 years of age are termed early onset Alzheimer’s disease (EOAD). The clinical presentation and disease mechanism of EOAD are quite distinct from typical Alzheimer’s. For example, EOAD patients often experience more rapid cognitive decline and have cortical region atrophy as opposed to temporal region atrophy. The atypical nature of EOAD frequently leads to delayed diagnosis and treatment. This delay is especially problematic for EOAD patients because they are at a stage of life with significant responsibilities such as maintaining their careers and families. This case study examines a previously healthy 32-year-old man that was mistakenly suspected of having frontotemporal dementia. Alzheimer’s was not considered as a correct diagnosis due to the atypical symptoms and weak family history for the disease. For these reasons, genetic analysis was not used to help make a diagnosis. The patient died at the age of 36 and autopsy revealed markers of Alzheimer’s including neuritic plaques and tau neurofibrillary tangles. Genetic analysis revealed no presenilin 1 (PSEN1) mutations which has been associated with EOAD. However, this patient may have had deletion mutations that could not be investigated due to the lack of frozen tissue for RNA analysis. This case illustrates the importance of genetic testing for EOAD diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".