Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia
Bibliographic record
Abstract
No treatment for frontotemporal dementia (FTD), the second most common type of early-onset dementia, is available, but therapeutics are being investigated to target the 2 main proteins associated with FTD pathological subtypes: TDP-43 (FTLD-TDP) and tau (FTLD-tau). Testing potential therapies in clinical trials is hampered by our inability to distinguish between patients with FTLD-TDP and FTLD-tau. Therefore, we evaluated truncated stathmin-2 (STMN2) as a proxy of TDP-43 pathology, given the reports that TDP-43 dysfunction causes truncated STMN2 accumulation. Truncated STMN2 accumulated in human induced pluripotent stem cell-derived neurons depleted of TDP-43, but not in those with pathogenic TARDBP mutations in the absence of TDP-43 aggregation or loss of nuclear protein. In RNA-Seq analyses of human brain samples from the NYGC ALS cohort, truncated STMN2 RNA was confined to tissues and disease subtypes marked by TDP-43 inclusions. Last, we validated that truncated STMN2 RNA was elevated in the frontal cortex of a cohort of patients with FTLD-TDP but not in controls or patients with progressive supranuclear palsy, a type of FTLD-tau. Further, in patients with FTLD-TDP, we observed significant associations of truncated STMN2 RNA with phosphorylated TDP-43 levels and an earlier age of disease onset. Overall, our data uncovered truncated STMN2 as a marker for TDP-43 dysfunction in FTD.
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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) | 0.002 | 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".