Current Concepts of Mixed Pathologies in Neurodegenerative Diseases
Bibliographic record
Abstract
Neurodegenerative diseases are a pathologically, clinically and genetically diverse group of disorders without effective disease-modifying therapies. Pathologically, these disorders are characterised by disease-specific protein aggregates in neurons and/or glia and referred to as proteinopathies. Many neurodegenerative diseases show pathological overlap with the same abnormally deposited protein occurring in anatomically distinct regions, which give rise to specific patterns of cognitive and motor clinical phenotypes. Sequential distribution patterns of protein inclusions throughout the brain have been described. Rather than occurring in isolation, it is increasingly recognised that combinations of one or more proteinopathies with or without cerebrovascular disease frequently occur in individuals with neurodegenerative diseases. In addition, complex constellations of ageing-related and incidental pathologies associated with tau, TDP-43, Aβ, α-synuclein deposition have been commonly reported in longitudinal ageing studies. This review provides an overview of current classification of neurodegenerative and age-related pathologies and presents the spectrum and complexity of mixed pathologies in community-based, longitudinal ageing studies, in major proteinopathies, and genetic conditions. Mixed pathologies are commonly reported in individuals >65 years with and without cognitive impairment; however, they are increasingly recognised in younger individuals (<65 years). Mixed pathologies are thought to lower the threshold for developing cognitive impairment and dementia. Hereditary neurodegenerative diseases also show a diverse range of mixed pathologies beyond the proteinopathy primarily linked to the genetic abnormality. Cases with mixed pathologies might show a different clinical course, which has prognostic relevance and obvious implications for biomarker and therapy development, and stratifying patients for clinical trials.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".