Truncated TDP‐43 proteoforms diagnostic of frontotemporal dementia with TDP‐43 pathology
Bibliographic record
Abstract
INTRODUCTION: Biomarkers of TDP-43 pathology are needed to distinguish frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP) from phenotypically related disorders. While normal physiological TDP-43 is not a promising biomarker, low-resolution techniques have suggested truncated forms of TDP-43 may be specific to TDP-43 pathology. To advance biomarker efforts for FTLD-TDP, we employed a high-resolution structural technique to characterize TDP-43 post-translational modifications in FTLD-TDP. METHODS: High-resolution mass spectrometry was used to characterize TDP-43 proteoforms in brain tissue from FTLD-TDP, non-TDP-43 dementias and neuropathologically unaffected cases. Findings were then verified in a larger cohort of FTLD-TDP and non-TDP-43 dementias via targeted quantitative mass spectrometry. RESULTS: In the discovery phase, truncated TDP-43 identified FTLD-TDP with 85% sensitivity and 100% specificity. The verification phase revealed similar findings, with 83% sensitivity and 89% specificity. DISCUSSION: The concentration of truncated TDP-43 proteoforms-in particular, in vivo generated C-terminal fragments-have high diagnostic accuracy for FTLD-TDP. HIGHLIGHTS: Discovery: Truncated TDP-43 differentiates FTLD-TDP from related dementias. Verification: Truncated TDP-43 concentration has high accuracy for FTLD-TDP. TDP-43 proteoforms <28 kDa have highest discriminatory power for TDP-43 pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".