Enhancing human islet xenotransplant survival and function in diabetic immunocompetent mice through LRH-1/NR5A2 pharmacological activation
Bibliographic record
Abstract
The intricate etiology of type 1 diabetes mellitus (T1D), characterized by harmful interactions between the immune system and insulin-producing beta cells, has hindered the development of effective therapies including human islet transplantation, which requires strong immunosuppressants that impair beta cell survival and function. As such alternative immunomodulating therapies are required for successful transplantation. The discovery that pharmacological activation of the nuclear receptor LRH-1/NR5A2 can reverse hyperglycemia in mouse models of T1D by altering, and not suppressing the autoimmune attack, prompted us to investigate whether LRH-1/NR5A2 activation could improve human islet function/survival after xenotransplantation in immunocompetent mice. Human islets were transplanted under the kidney capsule of streptozotocin (STZ)-induced diabetic mice, and treatment with BL001 (LRH-1/NR5A2 agonist) or vehicle was administered one week post-transplant. Our study, encompassing 3 independent experiments with 3 different islet donors, revealed that mice treated for 8 weeks with BL001 exhibited lower blood glucose levels correlating with improved mouse survival rates as compared to vehicle-treated controls. Human C-peptide was detectable in BL001-treated mice at both 4 and 8 weeks indicating functional islet beta cells. Accordingly, in mice treated with BL001 for 8 weeks, the beta cell mass was preserved, while a significant decrease in alpha cells was observed compared to mice treated with BL001 for only 4 weeks. In contrast, vehicle-treated mice exhibited a reduction in insulin-expressing cells at 8 weeks compared to those at 4 weeks. These results suggest that BL001 significantly enhances the survival, engraftment, and functionality of human islets in a STZ-induced diabetic mouse model.
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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) | 0.001 | 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) | 0.002 | 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".