A novel assessment of fine-motor function reveals early hindlimb and detectable forelimb deficits in an experimental model of ALS
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated with the loss of cortical and spinal motor neurons (MNs) and muscle degeneration (Kiernan et al. in Lancet 377:942-955, 2011). In the preclinical setting, functional tests that can detect early changes in motor function in rodent models of ALS are critical to understanding the etiology of the disease and treatment development. Here, we established a string-pulling paradigm that can detect forelimb and hindlimb motor deficits in the SOD1 mouse model of ALS earlier than traditional motor performance tasks. Additionally, our findings indicate that early loss of forelimb and hindlimb function is correlated with cortical and spinal MN loss, respectively. This task is not only ecological, low-cost, efficient, and non-onerous, it also requires little animal handling and reduces the stress placed on the animal. It has long been a concern in the field that the SOD1 mouse does not display forelimb motor deficits and does not give researchers a complete picture of the disease. Here, we provide evidence that the SOD1 model does in fact develop early forelimb motor deficits due to the task's ability to assess fine-motor function, reconciling this model with the various clinical presentation of ALS. Taken together, the string-pulling paradigm may provide novel insights into the pathogenesis of ALS, offer nuanced evaluation of prospective treatments, and has high translational potential to the clinic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".