Neural tissue engineering using embryonic and induced pluripotent stem cells
Bibliographic record
Abstract
With the recent start of the first clinical trial evaluating a human embryonic stem cell-derived therapy for the treatment of acute spinal cord injury, it is important to review the current literature examining the use of embryonic stem cells for neural tissue engineering applications with a focus on diseases and disorders that affect the central nervous system. Embryonic stem cells exhibit pluripotency and thus can differentiate into any cell type found in the body, including those found in the nervous system. A range of studies have investigated how to direct the differentiation of embryonic cells into specific neural phenotypes using a variety of cues to achieve the goal of replacing diseased or damaged neural tissue. Additionally, the recent development of induced pluripotent stem cells provides an intriguing alternative to the use of human embryonic stem cell lines for these applications. This review will discuss relevant studies that have used embryonic stem cells to replicate the tissue found in the central nervous system as well as evaluate the potential of induced pluripotent stem cells for the aforementioned applications.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".