New hypoglycemic effects of indorenate mediated by the 5-HT1a and 5-HT2a receptors: in vivo and in silico studies
Bibliographic record
Abstract
The treatment of metabolic syndrome (MS), characterized by type 2 diabetes, obesity, dyslipidemias, and cardiovascular problems, requires integral treatment. In addition to its central activity, serotonin also has peripheral effects, implying regulation of blood glucose and insulin levels. Indorenate is a serotonin analog with antihypertensive properties and possible activity on the metabolism of carbohydrates. However, its effect on glucose levels has not been explored, which is relevant in searching for the indorenate's potential as an alternative to treatment of MS. This investigation aimed to study the effects of indorenate on glycemia through in vivo and in silico assays. In normal rats, indorenate was co-administrated with two serotoninergic antagonists: pelanserin (5-HT2a antagonist) and WAY-100635 (5-HT1a antagonist). Indorenate caused a hypoglycemic effect in normal rats. Pelanserin and WAY-100635 inhibited this effect. In diabetic rats, indorenate increased insulin levels. In addition, indorenate decreased glycemia in an euglycemic clamp test, while pelanserin inhibited this effect. In silico, indorenate exhibited a higher affinity and interactions than serotonin for 5-HT2a and 5HT1a receptors. The data suggest that the hypoglycemic effect of indorenate requires the participation of the 5-HT2a receptor and partially of the 5-HT1a receptor. Finding drugs with beneficial multimodal effects for blood pressure and glycemic control, such as indorenate, might be relevant for treating MS and its associated pathologies.
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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 | 0.000 |
| 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".