Piscine Islet Xenotransplantation
Bibliographic record
Abstract
Tilapia, a teleost fish species with large anatomically discrete islet organs (Brockmann bodies; BBs) that can be easily harvested without expensive and fickle islet isolation procedures, make an excellent donor species for experimental islet xenotransplantation research. When transplanted into streptozotocin-diabetic nude or severe combined immunodeficient mice, BBs provide long-term normoglycemia and mammalian-like glucose tolerance profiles. However, when transplanted into euthymic recipients, the mechanism of islet xenograft rejection appears very similar to that of islets from "large animal" donor species such as the very popular fetal/neonatal porcine islet cell clusters (ICCs). Tilapia islets are more versatile than ICCs and can be transplanted (1) into the renal subcapsular space, the cryptorchid or noncryptorchid testis, or intraportally as neovascularized cell transplants; (2) as directly vascularized organ transplants; or (3) intraperitoneally after microencapsulation. Unlike the popular porcine ICCs, BBs function immediately after transplantation; thus, their rejection can be assessed on the basis of loss of function as well as other parameters. We have also shown that transplantation of tilapia BBs into nude mice can be used to study the possible implications of cross-species physiological incompatibilities in xenotransplantation. Unfortunately, tilapia BBs might be unsuitable for clinical islet xenotransplantation because tilapia insulin differs from human insulin by 17 amino acids and, thus, would be immunogenic and less biologically active in humans. Therefore, we have produced transgenic tilapia that express a "humanized" tilapia insulin gene. Future improvements on these transgenic fish may allow tilapia to play an important role in clinical islet xenotransplantation.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".