Interface between tauopathies and synucleinopathies: A tale of two proteins
Bibliographic record
Abstract
Neurodegenerative diseases are often classified based on the abnormal accumulation of synuclein or tau. Traditionally, these disorders have been viewed as distinct clinical and pathological entities. However, advances in molecular genetics and protein biochemistry have shown intriguing overlaps. The most common synucleinopathy, Parkinson's disease, is characterized by extrapyramidal motor dysfunction, whereas the most common tauopathy, Alzheimer's disease, is defined by dementia. Yet there is overlap of clinical features; Parkinson's disease patients frequently have dementia, and Alzheimer's disease patients often manifest parkinsonism. Dementia with Lewy bodies exemplifies the existence of a continuum among these diseases. This overlap extends to the neuropathological findings; the pathognomonic hallmark for one set of disorders, Lewy bodies or neurofibrillary tangles, is present more often than expected in the other set. Moreover, mutations in LRRK2 known to cause parkinsonism are associated not only with dopaminergic neuronal degeneration, but also with the accumulation of synuclein, tau, neither, or both proteins. Other shared genetic features between tauopathies and synucleinopathies also exist. Finally, the known protein interactions between tau and synuclein further highlight the interface. Evidence for the intersection of tauopathies and synucleinopathies indicates the need for an updated disease classification scheme and may have important implications for therapeutic development.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".