Cell Therapy; A New and Safe Strategy for the Treatment of Spinal Cord Injury: A Review
Bibliographic record
Abstract
Background: Spinal cord injury is a progressive process that initially causes abnormal nerve connections. Following spinal cord injury, the spinal cord is impaired after which cell death and apoptosis occurs. Primary damage happens in the spinal cord due to the demyelization of the large axons. Cell therapy is among the new strategies that have been considered for the treatment of neural injuries in recent years. Aim: In this narrative review article, we discuss "Cell Therapy" as a new and safe strategy for the treatment of spinal cord injury. we are going to explain the epidemiological and pathophysiological aspects of spinal cord injuries (SCI) as well as SCI experimental and clinical stem cell strategies. Conclusion: There are several promising advancements and findings in the field of stem cell biology and cell reprogramming, with the aim of treating patients with SCI via stem cell therapy. We reviewed critical issues for clinical translation and we also provided a commentary on recent developments such as termination of the first human embryonic stem cell transplantation trial in human SCI.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".