P.144 Comparative analysis of spinal cord-derived and induced pluripotent-derived neural stem & progenitor cells for SCI therapy
Bibliographic record
Abstract
Background: Induced pluripotent stem cells (iPSCs) have revolutionized spinal cord injury (SCI) treatment by generating neural stem/progenitor cells (NSPCs). However, understanding how iPSC-derived NSPCs compare to authentic spinal cord NSPCs remains unclear. This study thoroughly characterizes bona fide spinal cord NSPCs and their isogenic iPSC-derived counterparts, iPSC-SC and iPSC-Br. Methods: Human spinal cord and skin tissue were obtained with ethics approval to establish primary NSPC cultures. iPSCs were derived from these primary cells and differentiated into iPSC-SC and iPSC-Br NSPCs. Assessments encompassed differentiation, proliferation capabilities, immunostaining, and RNA sequencing for differential gene expression. Results: Functional and transcriptional differences were identified between bona fide NSPCs and iPSC-SC/iPSC-Br. Bona fide and iPSC-SC NSPCs exhibited spinal cord regionalization, while iPSC-Br displayed forebrain regionalization. iPSC-derived NSPCs shared features reminiscent of early developmental stages, including embryonic patterning genes and increased proliferation rates. Notably, differentiation profiles were most similar between bona fide and iPSC-Br, with substantial distinctions observed between bona fide and iPSC-SC. Conclusions: This study unveils unique regional, developmental, and functional characteristics distinguishing spinal cord NSPCs from iPSC-derived counterparts. Addressing these disparities holds promise for enhancing iPSC-derived NSPC therapies in spinal cord injuries, contributing to a deeper understanding of their potential applications in regenerative medicine.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".