Cytoskeletal Defects in Amyotrophic Lateral Sclerosis (Motor Neuron Disease)
Bibliographic record
Abstract
There is growing evidence for the involvement of cytoskeletal defects in the pathogenesis of motor neuron disease and especially in components of the microtubule-based transport system. Here we will review our recent work aiming to elucidate the role of peripherin in amyotrophic lateral sclerosis (ALS) and to address the mechanism of disease caused by deletions in the ALS2 gene that cause recessive forms of juvenile ALS and primary lateral sclerosis (PLS). Peripherin is an intermediate filament protein detected in spheroids, a hallmark of ALS, and increased levels of peripherin mRNA have been found in some ALS cases. Our transgenic mouse and cell culture studies support the view of a peripherin involvement in ALS. However, a gene knockout approach demonstrated that peripherin is not a key contributor of motor neuron disease caused by mutant superoxide dismutase linked to familial ALS. A recent breakthrough in the field of ALS came with the discovery of frameshift deletions in the ALS2 gene coding for Alsin. Our transfection experiments in cultured cells suggest that Alsin is a cytoskeletal protein with dual endosomal and centrosomal localizations. We have generated a mouse knockout for Alsin that develops progressive motor dysfunction during ageing. Thus, it is anticipated that this mouse model will be useful to investigate the pathogenic pathways linked to Alsin gene mutations.
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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 | 0.001 | 0.000 |
| 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".