A review of piscine islet xenotransplantation using wild‐type tilapia donors and the production of transgenic tilapia expressing a “humanized” tilapia insulin
Bibliographic record
Abstract
Most islet xenotransplantation laboratories have focused on porcine islets, which are both costly and difficult to isolate. Teleost (bony) fish, such as tilapia, possess macroscopically visible distinct islet organs called Brockmann bodies which can be inexpensively harvested. When transplanted into diabetic nude mice, tilapia islets maintain long-term normoglycemia and provide human-like glucose tolerance profiles. Like porcine islets, when transplanted into euthymic mice, they are rejected in a CD4 T-cell-dependent manner. However, unlike pigs, tilapia are so phylogenetically primitive that their cells do not express α(1,3)Gal and, because tilapia are highly evolved to live in warm stagnant waters nearly devoid of dissolved oxygen, their islet cells are exceedingly resistant to hypoxia, making them ideal for transplantation within encapsulation devices. Encapsulation, especially when combined with co-stimulatory blockade, markedly prolongs tilapia islet xenograft survival in small animal recipients, and a collaborator has shown function in diabetic cynomolgus monkeys. In anticipation of preclinical xenotransplantation studies, we have extensively characterized tilapia islets (morphology, embryologic development, cell biology, peptides, etc.) and their regulation of glucose homeostasis. Because tilapia insulin differs structurally from human insulin by 17 amino acids, we have produced transgenic tilapia whose islets stably express physiological levels of humanized insulin and have now bred these to homozygosity. These transgenic fish can serve as a platform for further development into a cell therapy product for 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".