AT1-receptor blockade and insulin sensitivity in hypertensive subjects: the role of capillary recruitment
Bibliographic record
Abstract
Aims/hypothesis Glucagon-like peptide-1 receptor (GLP-1R) agonists improve glucose control in animals and humans with type 1 diabetes.However, there is little information on the role of the GLP-1R in the immune system.We studied the role of the GLP-1R in immune function in wild-type (WT) and nonobese diabetic (NOD) and Glp1r -/-mice.Methods Glp1r mRNA expression was examined in sorted immune subpopulations by RT-PCR.The effects of GLP-1R activation were assessed on cAMP production and proliferation, migration and survival of primary immune cells from WT and NOD mice.The ability of primary cells from Glp1r -/-mice to proliferate, migrate or survive apoptosis was determined.Immunophenotyping studies were performed to assess the frequency of immune subpopulations in Glp1r -/-mice.Results Ex vivo activation of the GLP-1R resulted in a modest but significant elevation of cAMP in primary thymocytes and splenocytes from both WT and NOD mice.GLP-1R activation did not increase proliferation of primary thymocytes, splenocytes or peripheral lymph node cells.In contrast, Glp1r -/-thymocytes exhibited a hypoproliferative response, whilst peripheral Glp1r -/-lymphocytes were hyperproliferative in response to mitogenic stimulation.Activation or loss of GLP-1R signalling did not modify apoptosis or chemotaxis in primary lymphocytes.Male Glp1r -/-mice exhibited a significantly lower percentage of peripheral regulatory T cells, although no differences were observed in the numbers of CD4+ and CD8+ T cells and B cells in the spleen and lymph nodes of Glp1r -/-mice. Conclusions/interpretationThese studies establish that GLP-1R signalling may regulate lymphocyte proliferation and maintenance of peripheral regulatory T cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".