The neuropathology of frontotemporal lobar degeneration caused by mutations in the progranulin gene
Bibliographic record
Abstract
The most common pathology in frontotemporal dementia (FTD) is tau-negative, ubiquitin-immunoreactive (ub-ir) neuronal inclusions (FTLD-U). Recently, we identified mutations in the progranulin (PGRN) gene as the cause of autosomal dominant FTLD-U linked to chromosome 17. Here, we describe the neuropathology in 13 patients from 6 different families, each with FTD caused by a different PGRN mutation. The most consistent feature was the presence of ub-ir lentiform neuronal intranuclear inclusions (NII) in the neocortex and striatum. In addition, the neocortex showed moderate-to-severe superficial laminar spongiosis, chronic degenerative changes, ub-ir neurites and well-defined ub-ir neuronal cytoplasmic inclusions (NCI). In the striatum, there were numerous ub-ir neurites. NCI in the hippocampus usually had a granular appearance. In contrast, familial FTLD-U cases without PGRN mutations had no NII, less severe neocortical and striatal pathology and hippocampal NCI that were more often solid. Eight cases in which genetic analysis was not available also had NII and an overall pathology similar to those with proven mutations. None of our cases of FTLD-U without NII showed the same pattern of pathology as those with mutations. These findings suggest that FTD caused by PGRN mutations has a recognizable pathology with the most characteristic feature being ub-ir NII.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".