Current concepts and molecular pathology of neurodegenerative diseases
Bibliographic record
Abstract
Neurodegenerative diseases are a pathologically, clinically and genetically diverse group of diseases characterised by selective dysfunction, loss of synaptic connectivity and neurodegeneration, and are associated with the deposition of misfolded proteins in neurons and/or glia. Molecular studies have highlighted the role of conformationally altered proteins in the pathogenesis of neurodegenerative diseases and have paved the way for developing disease-specific biomarkers that capture and differentiate the main type/s of protein abnormality responsible for neurodegenerative diseases, some of which are currently used in clinical practice. These proteins follow sequential patterns of anatomical involvement and disease spread in the brain and may also be detected in peripheral organs. Recent studies suggest that glia are likely to have an important role in pathological spread throughout the brain and even follow distinct progression patterns from neurons. In addition to morphological and molecular approaches to the classification of these disorders, a further new stratification level incorporates the structure of protein filaments detected by cryogenic electron microscopy. Rather than occurring in isolation, combined deposition of tau, amyloid-β, α-synuclein and TDP-43 are frequently observed in neurodegenerative diseases and in the ageing brain. These can be overlooked, and their clinicopathological relevance is difficult to interpret. This review provides an overview of disease pathogenesis and diagnostic implications, recent molecular and ultrastructural classification of neurodegenerative diseases, how to approach ageing-related and mixed pathologies, and the importance of the protein-based classification system for practising neuropathologists and clinicians. This review also informs general pathologists about the relevance of ongoing full body autopsy studies to understand the spectrum and pathogenesis of neurodegenerative diseases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".