A clinical-grade HLA haplobank of human induced pluripotent stem cells matching approximately 40% of the Japanese population
Bibliographic record
Abstract
BACKGROUND: Human induced pluripotent stem cells (iPSCs) are expected to be useful for regenerative medicine for many diseases. Many researchers have focused on and enabled the generation of differentiated cells or tissue-like structures, including organoids, which help to ameliorate target diseases. To promote such cell therapies, we established a clinically applicable iPSC haplobank matching as many people as possible in Japan. METHODS: Through cooperation with several organizations, we recruited donors whose human leukocyte antigens (HLAs) involved in immunorejection were homozygous. The peripheral or umbilical cord blood collected from the donors was used for iPSC production by electroporation of episomal vectors. These iPSC lines were then subjected to testing, including genome analyses and sterility, to maximize safety. FINDINGS: We constructed a clinical-grade haplobank of 27 iPSC lines from 7 donors according to good manufacturing practice regulations. However, reasons to avoid using iPSC lines include the presence of residual episomal vectors or genetic mutations in cancer-related genes. CONCLUSIONS: This haplobank provides HLA-matched iPSC lines for approximately 40% of the Japanese population. Since the haplobank's release in 2015, these iPSC lines have been used in more than 10 clinical trials. The establishment of this haplobank is an important step toward the clinical application of iPSCs in cell therapies. FUNDING: This study was supported by a research center network for the realization of regenerative medicine of the Japan Agency for Medical Research and Development (AMED) under grant number JP20bm0104001h0108.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".