Bibliographic record
Abstract
The islet tissue, called Brockmann bodies, in certain teleost fish is anatomically distinct from the pancreatic exocrine tissue and can be easily identified macroscopically. Expensive islet isolation procedures, such as required when procuring islet tissue from mammalian pancreases, are unnecessary. Tilapia islets transplanted into diabetic athymic nude mice will produce long-term normoglycemia and mammalian-like glucose tolerance profiles. Encapsulated tilapia islets function well after transplantation into euthymic recipients. Additionally, tilapia are potentially ideal xenogeneic donors because of markedly lower donor production costs, minimal islet procurement costs, and possibly decreased xenozoonotic potential relative to mammalian donors. Tilapia islets appear to be appropriately glucose responsive with high insulin output, can be cryopreserved, and are much more resistant to hypoxia than mammalian islets. Because tilapia and human insulin differ by 17 amino acids, we have cloned, sequenced, and modified the tilapia insulin gene by site-directed mutagenesis resulting in a tilapia insulin gene that codes for "humanized" insulin while still maintaining all of the tilapia regulatory (noncoding) sequences. We have now produced small numbers of founder transgenic tilapia with incorporation of our humanized tilapia insulin gene construct, and we have shown transmission and expression of the transgene in the beta cells and serum of their Fl offspring. Our ultimate goal is to achieve homologous recombination and to breed for homozygosity for the hybrid insulin gene. Subsequent generations of transgenic tilapia will undergo a program of genomic optimization selecting for growth, survival, and fecundity as well as stability of the transgene. Islets from the resulting transgenic fish, after extensive characterization, could be harvested, encapsulated, and then transplanted into diabetic patients.
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) | 0.000 | 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) | 0.004 | 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".