Neuropathological and behavioral characterization of aged Grn R493X progranulin-deficient frontotemporal dementia knockin mice
Bibliographic record
Abstract
Abstract Frontotemporal lobar degeneration (FTLD) causes a spectrum of clinical presentations of frontotemporal dementia (FTD), including progressive changes in behavior, personality, executive function, and language. Up to 20% of familial FTLD cases are caused by progranulin (GRN) haploinsufficiency (FTD-GRN), with one of the most common causal variant being a nonsense mutation at arginine 493 (R493X). Recently, a genetic knockin FTD-GRNmouse model was generated bearing thisGrnR493Xmutation, at the analogous arginine in murineGrn. Aged, homozygousGrnR493Xmice (GrnR493X/R493X) have been shown to phenotypically replicate several neuropathological hallmarks previously demonstrated inGrnnull mice. We conducted a comprehensive neuropathological and behavioral assessment of 18 month oldGrnR493X/R493Xmice, observing a striking lysosomal dysfunction and thalamic neurodegeneration not previously described in this model, as well as a male-specific increase in generalized anxiety. These findings provide additional phenotypic markers of pathogenesis in agedGrnR493X/R493Xmice that will contribute to better defining mechanisms underlying FTD-GRN, and offer relevant outcome measures for preclinical efficacy testing of novel therapeutics that target nonsense mutations leading to this devastating disease.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".