Neural tissue engineering using embryonic and induced pluripotent stem cells
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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