Induced Pluripotent Stem Cells for Traumatic Spinal Cord Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it