Body Weight, Limb Size, and Muscular Properties of Early Paraplegic Mice
Bibliographic record
Abstract
Patients with spinal cord injury (SCI) typically experience body weight loss, motor function deficits, and a general decline of physical fitness. Animal models with these characteristics can serve to study the detailed adaptive changes following SCI. In the present study, we report the use of an adult paraplegic mouse model to study SCI-induced changes. We characterized the early effects of complete thoracic spinal cord transection on (1) whole body weight, (2) forelimb and hindlimb weight and volume, and (3) contractile properties of hindlimb extensor muscle. Drastic changes were found at 7 days post-spinal cord transection. These included a 24% loss in whole body weight accompanied by a large decrease of weight and volume in the forelimbs and the hindlimbs. We also observed in the soleus muscle, a 32% decrease in mass and maximal tetanic tension (Po) as well as a 21% and 48% increase in time-to-peak tension (TPT) and half-relaxation time (1/2 RT) respectively. After 28 days, all of the changes remained, except for 1/2 RT and TPT which nearly returned to control levels. Altogether, the results reveal that large changes in body weight, limb size and musculoskeletal properties occur within only one week after complete spinal cord transection. The use of paraplegic mouse models may provide new therapeutic approaches to restore motor and locomotor functions after SCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".