Cellular localisation of the kinin B1R in the pancreas of streptozotocin-treated rat and the anti-diabetic effect of the antagonist SSR240612
Bibliographic record
Abstract
The mechanism by which kinin B1 receptor (B1R) contributes to type 1 diabetes is addressed by determining the impact of its inhibition on diabetes and on its pancreatic expression and cellular localisation on immunocompetent cells and primary sensory C-fibres. Rats were made diabetic with streptozotocin (STZ). On day 4, they were treated daily for 7 days with a B1R antagonist (SSR240612, 10 mg/kg) or its vehicle. The surviving β-cells were measured by immunostaining. The expression of B1R, iNOS, TNF-α, macrophages, TCD4+, CGRP and TRPV1 was measured by Western blotting, qRT-PCR and immunofluorescence. Macrophages and TCD4+ lymphocytes were absent in control, but distributed abundantly in the pancreas of STZ-diabetic rats. B1R was upregulated on these immune cells infiltrating the diabetic rat pancreas while it was not expressed on primary sensory C-fibres even if the expression of TRPV1 and CGRP was enhanced. SSR240612 prevented the infiltration of macrophages and TCD4+ lymphocytes and the upregulation of B1R, iNOS, TNF-α and TRPV1. SSR240612 corrected hyperglycaemia and hypoinsulinaemia by improving the Langerhans islets survival or regeneration. It is concluded that kinin B1R antagonism exerts anti-diabetic action by preventing the infiltration of immune cells in the pancreas and by preserving the integrity of Langerhans islets β-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 | 0.000 |
| 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) | 0.002 | 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".