Prolactin as an Adjunct for Type 1 Diabetes Immunotherapy
Bibliographic record
Abstract
Type 1 diabetes is caused by autoimmune destruction of β-cells. Although immunotherapy can restore self-tolerance thereby halting continued immune-mediated β-cell loss, residual β-cell mass and function is often insufficient for normoglycemia. Using a growth factor to boost β-cell mass can potentially overcome this barrier and prolactin (PRL) may fill this role. Previous studies have shown that PRL can stimulate β-cell proliferation and up-regulate insulin synthesis and secretion while reducing lymphocytic infiltration of islets, suggesting that it may restore normoglycemia through complementary mechanisms. Here, we test the hypothesis that PRL can improve the efficacy of an immune modulator, the anticluster of differentiation 3 monoclonal antibody (aCD3), in inducing diabetes remission by up-regulating β-cell mass and function. Diabetic nonobese diabetic (NOD) mice were treated with a 5-day course of aCD3 with or without a concurrent 3-week course of PRL. We found that a higher proportion of diabetic mice treated with the aCD3 and PRL combined therapy achieved diabetes reversal than those treated with aCD3 alone. The aCD3 and PRL combined group had a higher β-cell proliferation rate, an increased β-cell fraction, larger islets, higher pancreatic insulin content, and greater glucose-stimulated insulin release. Lineage-tracing analysis found minimal contribution of β-cell neogenesis to the formation of new β-cells. Although we did not detect a significant difference in the number or proliferative capacity of T cells, we observed a higher proportion of insulitis-free islets in the aCD3 and PRL group. These results suggest that combining a growth factor with an immunotherapy may be an effective treatment paradigm for autoimmune diabetes.
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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) | 0.001 | 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".