Treatment of Obesity and Diabetes in Mice by Transplant of Gut Cells Engineered to Produce Leptin
Bibliographic record
Abstract
Leptin injections evoke weight loss by causing a reduction in food consumption and an increase in energy expenditure. Also, the administration of leptin lowers blood glucose levels in some rodent models of diabetes and in humans with lipodystrophy. We explored the therapeutic potential of delivering leptin to obese, diabetic ob/ob mice and to mice fed on a high-fat diet (HFD), by transplanting gut-derived cells engineered to produce leptin, under the regulation of an inducing agent, mifepristone. These cells expressed and released leptin in a mifepristone dose-dependent and time-dependent manner. The engineered cells were either transplanted into the mice under the kidney capsule or were encapsulated in alginate and injected into the intraperitoneal cavity, while mifepristone was delivered by implanting 14-day release pellets. In ob/ob mice, leptin delivery by this method caused a significant reduction in food intake and profound weight loss, which was controllable by adjusting the dose of mifepristone. These transplants also achieved rapid and persistent amelioration of diabetes. However, mice fed on a HFD were resistant to the leptin therapy. These results indicate that gut cells can be modified to express leptin in an inducible manner and that the transplantation of these cells has a therapeutic effect in leptin-deficient mice, but not in mice fed on a HFD.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".