Evaluating the Potential for ABO-incompatible Islet Transplantation: Expression of ABH Antigens on Human Pancreata, Isolated Islets, and Embryonic Stem Cell-derived Islets
Bibliographic record
Abstract
BACKGROUND: ABO-incompatible transplantation has improved accessibility of kidney, heart, and liver transplantation. Pancreatic islet transplantation continues to be ABO-matched, yet ABH antigen expression within isolated human islets or novel human embryonic stem cell (hESC)-derived islets remain uncharacterized. METHODS: We evaluated ABH glycans within human pancreata, isolated islets, hESC-derived pancreatic progenitors, and the ensuing in vivo mature islets following kidney subcapsular transplantation in rats. Analyses include fluorescence immunohistochemistry and single-cell analysis using flow cytometry. RESULTS: Within the pancreas, endocrine and ductal cells do not express ABH antigens. Conversely, pancreatic acinar tissues strongly express these antigens. Acinar tissues are present in a substantial portion of cells within islet preparations obtained for clinical transplantation. The hESC-derived pancreatic progenitors and their ensuing in vivo-matured islet-like clusters do not express ABH antigens. CONCLUSIONS: Clinical pancreatic islet transplantation should remain ABO-matched because of contaminant acinar tissue within islet preparations that express ABH glycans. Alternatively, hESC-derived pancreatic progenitors and the resulting in vivo-matured hESC-derived islets do not express ABH antigens. These findings introduce the potential for ABO-incompatible cell replacement treatment and offer evidence to support scalability of hESC-derived cell therapies in type 1 diabetes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".