The Neglected Genes of ALS: Cytoskeletal Dynamics Impact Synaptic Degeneration in ALS
Bibliographic record
Abstract
) is of high importance for MN health and survival, considering that MNs are large polarized cells with axons that can reach up to 1 m in length. In particular, cytoskeletal dynamics facilitate the transport of organelles and molecules across the long axonal distances within the cell, playing a key role in synapse maintenance. The majority of ALS-related genes affecting cytoskeletal dynamics were identified within the past two decades, making it a new area to explore for ALS. The purpose of this review is to provide insights into ALS-associated cytoskeletal genes and outline how recent studies have pointed towards novel pathways that might be impacted in ALS. Further studies making use of extensive analysis models to look for true hits, the newest technologies such as CRIPSR/Cas9, human induced pluripotent stem cells (iPSCs) and axon sequencing, as well as the development of more transgenic animal models could potentially help to: differentiate the variants that truly act as a primary cause of the disease from the ones that act as risk factors or disease modifiers, identify potential interactions between two or more ALS-related genes in disease onset and progression and increase our understanding of the molecular mechanisms leading to cytoskeletal defects. Altogether, this information will give us a hint on the real contribution of the cytoskeletal ALS-related genes during this lethal disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".