Developing Human-Nonhuman Chimeras in Human Stem Cell Research: Ethical Issues and Boundaries
Bibliographic record
Abstract
The transplantation of adult human neural stem cells into prenatal non-humans offers an avenue for studying human neural cell development without direct use of human embryos. However, such experiments raise significant ethical concerns about mixing human and nonhuman materials in ways that could result in the development of human-nonhuman chimeras. This paper examines four arguments against such research, the moral taboo, species integrity, "unnaturalness," and human dignity arguments, and finds the last plausible. It argues that the transfer of human brain or retinal stem cells to nonhuman embryos would not result in the development of human-nonhuman chimeras that denigrate human dignity, provided such stem cells are dissociated. The article provides guidelines that set ethical boundaries for conducting such research that are consonant with the requirements of human dignity.
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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 | 0.155 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".