Selective pharmacological inhibition of phosphoinositide 3-kinase p110delta opposes the progression of autoimmune diabetes in non-obese diabetic (NOD) mice
Bibliographic record
Abstract
During the progression of autoimmune (type 1) diabetes, T cells and macrophages infiltrate the pancreas, disrupt islet function, and destroy insulin-producing beta cells. B-lymphocytes, particularly innate like B-cell populations such as marginal zone B cells and B-1 cells, have been implicated in many autoimmune diseases, and non-obese diabetic (NOD) mice that lack B cells do not develop spontaneous autoimmune diabetes. Hence, inhibitors of B cell signaling pathways could be useful for limiting the autoimmune processes that contribute to type 1 diabetes. Signaling via phosphoinositide 3-kinase (PI3K) regulates many cellular processes. The p110δ isoform of PI3K is expressed primarily in cells of hematopoietic origin and the catalytic activity of p110δ is important for B cell migration, activation, proliferation, and antigen presentation. Because innate-like B cells are particularly sensitive to inhibition of p110δ activity, and p110δ inhibitors also suppress pro-inflammatory functions of other cell types that contribute to autoimmunity, we tested whether a p110δ inhibitor could delay the onset or reduce the incidence of autoimmune diabetes in NOD mice. We found that long-term preventative treatment of pre-diabetic NOD mice with IC87114, a highly selective small molecule inhibitor of p110δ, reduced the infiltration of inflammatory cells into the pancreatic islets and, accordingly, delayed and reduced the loss of glucose homeostasis. Moreover in a therapeutic treatment mode, IC87114 treatment conferred prolonged protection from progression to overt diabetes in a number of animals. These findings suggest that PI3Kδ inhibitors could be useful for managing type 1 diabetes.
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 | 0.000 |
| 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".