Glp-1 as an adjunct to prolactin and anti-cd3 in type 1 diabetes treatment
Bibliographic record
Abstract
Type 1 diabetes mellitus (T1DM) is an autoimmune disease where the destruction of the beta-cells causes insulin deficiency and hyperglycemia. Many immune modulators have effectively prevented the further destruction of beta-cells in animal models of TIDM. However, due to the small beta-cell mass present at the time of diabetes, immune modulators (such as anti-CD3) have limited efficacy in treating T1DM in humans. We hypothesize that addition of a growth factor that can augment beta-cell number (i.e. prolactin) and recruit stem cells (i.e. GLP-1) may improve effectiveness of immune modulator in inducing diabetes remission. In our current study, we found that the diabetic NOD mice treated with anti-CD3+prolactin+GLP-1 had significantly higher diabetes remission rate in comparison to mice treated with anti-CD3+prolactin or anti-CD3 alone group. This improvement in diabetes remission rate is accompanied by a higher glucose-stimulated insulin secretion rate. By performing immunohistochemistry (IHC), our results show that the anti-CD3+prolactin+GLP-1 group has a similar beta-cell fraction as the anti-CD3+prolactin group. Future experiments determining the pancreatic insulin content and expression of GLUT2 would reveal the mechanism underlying novel therapeutic approach to improve the cure rates in children suffering from T1DM.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".