TDP-43 as a potential retinal biomarker for neurodegenerative diseases
Bibliographic record
Abstract
TDP-43 proteinopathies are a spectrum of neurodegenerative diseases (NDDs) characterized by the pathological cytoplasmic aggregation of the TDP-43 protein. These include amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), Alzheimer's disease (AD), chronic traumatic encephalopathy (CTE), and others. TDP-43 in the eye shows promise as a biomarker for these NDDs. Several studies have identified cytoplasmic TDP-43 inclusions in retinal layers of donors with ALS, FTLD, AD, CTE, and other conditions using immunohistochemistry. Our findings suggest that pathological aggregates of TDP-43 in the human retina are most prevalent in FTLD-TDP, ALS, and CTE, suggesting these diseases may provide the most reliable context for studying the potential of TDP-43 as a retinal biomarker. Animal model studies have been pivotal in exploring TDP-43's roles in the retina, including its nuclear and cytoplasmic localization, RNA binding properties, and interactions with other proteins. Despite these advances, more research is needed to develop therapeutic strategies. A major limitation of human autopsy studies is the lack of corresponding brain pathology assessments to confirm TDP-43 proteinopathy diagnosis and staging. Other limitations include small sample sizes, lack of antemortem eye pathology and clinical histories, and limited comparisons across multiple NDDs. Future directions for the TDP-43 as a retinal biomarker for NDDs include retinal tracers, hyperspectral imaging, oculomics, and machine learning development.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".