Abstracts fromThe 33 <sup>rd</sup> AnnualNational Neurotrauma SymposiumJune 28–July 1, 2015Santa Fe, New Mexico
Bibliographic record
Abstract
Appropriate limb loading is essential for neurorehabilitation after SCI, in part, because it guides spinal cord neuroplasticity.Complete unloading such as prolonged bed rest may interfere with functional recovery, whereas appropriate afferent information through rehabilitation may improve function by modulating spinal plasticity.The impact of limb loading on synaptic plasticity in SCI remains poorly understood.We investigated long-term biological, biomechanical and physiological consequence of hindlimb unloading (HU) in the acute phase of SCI. Adult female SD rats received a mild SCI (T9; 50 kdyn, IH).Three days post-injury, subjects were randomized to two experimental groups: 1) HU by tail suspension, or 2) normal-loading control.After two weeks, the HU group was returned to normal loading condition.Animals were monitored until 8 weeks post-injury.Assessments included: 1) BBB locomotor recovery; 2) kinematic gait analysis; 3) electrophysiological H-reflex testing at 8 weeks-post injury; 4) spinal cord tissue analysis using biomolecular and robotic confocal microscopy assessments of plasticity-related changes in ventral motorneurons.Results indicated that: 1) HU early after SCI impaired recovery of coordinated gait characteristics and produced excessive excitation of spinal reflex circuits; 2) Chronically increased synaptic glutamate AMPA receptors on the plasma membrane of spinal motor neurons providing a cellular mechanism.Our findings suggest that limb unloading early after SCI induces maladaptive spinal cord plasticity that persists to impair functional recovery in chronic phase, providing a novel mechanistic target for early intervention after SCI to enhance the effect of rehabilitation in the chronic phase following injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.550 | 0.208 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".