C9ORF72 Deficiency Results in Neurodegeneration in the Zebrafish Retina
Bibliographic record
Abstract
Hexanucleotide repeat expansions within the gene C9ORF72 are the most common cause of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). This disease-causing expansion leads to a reduction in C9ORF72 expression levels in patients, suggesting loss of C9ORF72 function could contribute to disease. To further understand the consequences of C9ORF72 deficiency in vivo, we generated a c9orf72 mutant zebrafish line. Analysis of the adult female spinal cords revealed no appreciable neurodegenerative pathology such as loss of motor neurons or increased levels of neuroinflammation. However, detailed examination of adult female c9orf72 −/− retinas showed prominent neurodegenerative features, including a decrease in retinal thickness, gliosis, and an overall reduction in neurons of all subtypes. Analysis of rod and cone cells within the photoreceptor layer showed a disturbance in their outer segment structure and rhodopsin mislocalization from rod outer segments to their cell bodies and synaptic terminals. Thus, C9ORF72 may play a previously unappreciated role in retinal homeostasis and suggests C9ORF72 deficiency can induce tissue specific neuronal loss.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".