Induced Pluripotent Stem Cells for Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) is a common cause of mortality and neurological morbidity. Although progress had been made in the last decades in medical, surgical, and rehabilitation treatments for SCI, the outcomes of these approaches are not yet ideal. The use of cell transplantation as a therapeutic strategy for the treatment of SCI is very promising. Cell therapies for the treatment of SCI are limited by several translational road blocks, including ethical concerns in relation to cell sources. The use of iPSCs is particularly attractive, given that they provide an autologous cell source and avoid the ethical and moral considerations of other stem cell sources. In addition, different cell types, that are applicable to SCI, can be created from iPSCs. Common cell sources used for reprogramming are skin fibroblasts, keratinocytes, melanocytes, CD34+ cells, cord blood cells and adipose stem cells. Different cell types have different genetic and epigenetic considerations that affect their reprogramming efficiencies. Furthermore, in SCI the iPSCs can be differentiated to neural precursor cells, neural crest cells, neurons, oligodendrocytes, astrocytes, and even mesenchymal stromal cells. These can produce functional recovery by replacing lost cells and/or modulating the lesion microenvironment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".