FUS‐Immunoreactive Intranuclear Inclusions in Neurodegenerative Disease
Bibliographic record
Abstract
Neuronal intranuclear inclusions (NIIs) are a histopathological hallmark of several neurodegenerative disorders. However, the role played by NIIs in neurodegenerative pathogenesis remains enigmatic. Defining their molecular composition represents an important step in understanding the pathophysiology of these disorders. Recently, a nuclear protein, "fused-in-sarcoma" (FUS) was identified as the pathological protein in two forms of frontotemporal lobar degeneration (FTLD-IF, formerly known as neuronal intermediate filament inclusion disease, and FTLD-UPS, formerly known as atypical FTLD-U), both of which are characterized by the presence of NII. The objective of the present study was to determine the range of neurodegenerative disorders characterized by FUS-positive NIIs. Immunostaining for FUS revealed intense reactivity of NIIs in FTLD-IF and FTLD-UPS as well as in Huntington's disease, spinocerebellar ataxias 1 and 3, and neuronal intranuclear inclusion body disease. In contrast, there was no FUS staining of NIIs in inherited forms of FTLD-TDP caused by GRN and VCP mutations, fragile-X-associated tremor ataxia syndrome, or oculopharyngeal muscular dystrophy. In a cell culture model of Huntington's disease, NIIs were intensely FUS-positive. NII-bearing cells displayed loss of the normal diffuse nuclear pattern of FUS staining. This suggests that sequestration of nuclear FUS by NIIs may interfere with its normal nuclear localization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".