A Retinal Biophysical Biomarker for Amyotrophic Lateral Sclerosis (ALS)
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is an incurable motor neuron disease with no current valid diagnostic imaging biomarkers. The retina is an extension of the central nervous system and axonal transport defects have been documented in various neurodegenerative diseases. This study reports evidence of axonal pathology in the retina of ALS patients using an interdisciplinary approach that includes the neuropathological study of retinal sections in ALS patients expanded to the optical characteristics of the whole retina preparations using eye imaging technology. The histopathological examination of retina sections revealed round profiles in the retinal nerve fibre layer in 10 out 10 ALS patients and in 4 out of 10 age-matched control patients. All 10 ALS patients showed increased phosphorylated neurofilament immunoreactivity in the retinal nerve fibre layer compared to all 10 control patients. Retinal imaging of whole globes and retina flat-mounts by blue reflectance retinal funduscopy and optical coherence tomography revealed hyper-reflective profiles in the retinal nerve fibre layer. For the first time, approximately 1µm retinal ganglion cells axons were visualized in immunofluorescence stained retina flat-mounts using near-infrared retina fundus imaging and Image Mapping Spectrometer. These findings suggest axonal pathology in retinal ganglion cells and its potential use as a novel non-invasive ocular imaging biomarker for ALS.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".