A technological platform to optimize combinatorial treatment design and discovery for chronic spinal cord injury
Bibliographic record
Abstract
Chronic spinal cord injury (SCI) is associated with the development of serious medical concerns. In fact, it is increasingly well documented that most SCI patients who survive the first 24 hr will rapidly develop, within a few months to a few years, cardiovascular problems, type II diabetes, muscle wasting, osteoporosis, immune deficiencies, and other life-threatening problems. The cellular mechanisms underlying these so-called secondary health complications remain unclear, and no drug or standard approach has been developed to specifically treat these complications. To investigate the cellular and metabolic changes associated with chronic SCI and functional recovery, work mainly from our laboratory recently has led to the characterization of a mouse model of chronic paraplegia. This review reports cellular, systemic, and metabolic changes (associated mainly with secondary health complications) occurring within a few days to a few weeks after SCI in low-thoracic spinal cord-transected mice. We also describe our research platform developed to ease technological transfer and to accelerate drug-screening studies in animals. A global understanding of the many chronic changes occurring after SCI together with efficient tools and approaches for testing new or existing drug candidates is likely to yield the design of innovative treatments against secondary complications that combine cellular plasticity-modulating agents, locomotor network-activating drugs, hormonal therapy, and exercise training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".