Abnormalities of the nucleus and nuclear inclusions in neurodegenerative disease: a work in progress
Bibliographic record
Abstract
Neurodegenerative diseases are characterized pathologically by the abnormal accumulation of pathogenic protein species within the cell. Several neurodegenerative diseases feature intranuclear protein aggregation in the form of intranuclear inclusion bodies. Studies of these intranuclear inclusions are providing important clues regarding the cellular pathophysiology of these diseases, as exemplified by recent progress in defining the genetic basis of a subset of frontotemporal dementia cases. The precise role of intranuclear inclusion bodies in disease pathogenesis is currently a focus of debate. The present review provides an overview of the diverse family of neurodegenerative diseases in which nuclear inclusions form part of the neuropathological spectrum. In addition, current pathogenetic concepts relevant to these diseases will be reviewed and arguments for and against a protective role for intranuclear inclusions will be presented. The relationship of pathological intranuclear inclusions to functional intranuclear bodies will also be discussed. Finally, by analogy with pathological intranuclear inclusions, I will speculate on the possibility that intranuclear protein aggregation may represent a constitutive cellular protective mechanism occurring in neurons under physiological conditions.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".