Chimera Research and Stem Cell Therapies for Human Neurodegenerative Disorders
Bibliographic record
Abstract
In April 2005, the U.S. National Academy of Sciences (NAS) published its Guidelines for Human Embryonic Stem Cell Research. These voluntary guidelines are among the most permissive in the world—in a country that prohibits federal funding of research to derive human embryonic stem (hES) cells (cells that can self-renew or differentiate into most cells in the human body). One of the few research prohibitions in the NAS guidelines concerns the creation of certain kinds of human–nonhuman chimeras. A chimera is an organism with a mixture of cells from two different organisms, from the same or different species. Figure 1 provides a useful overview of different types of chimeras.This work was supported, in part, by a Stem Cell Network grant to Françoise Baylis and Jason Scott Robert and a CIHR grant to Françoise Baylis. We sincerely thank Alan Fine, Rich Campbell, Cynthia Cohen, and Tim Krahn for helpful comments on an earlier draft of this paper. Thanks are also owed to Tim Krahn for his research assistance. An earlier version of this paper was presented to the Department of Bioethics and the Novel Tech Ethics research team (www.noveltechethics.ca). We thank the participants at each of these meetings for their helpful comments.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".