Anti-depressant effect of hesperidin in diabetic rats
Bibliographic record
Abstract
This study aimed to investigate the anti-depressant effect of hesperidin (Hsp) in streptozotocin (STZ)-induced diabetic rats. Additionally, the effect of Hsp on hyperglycaemia, oxidative stress, inflammation, brain-derived neurotrophic factor (BDNF), and brain monoamines in diabetic rats was also assessed. The Wistar rats in the experimental groups were rendered hyperglycaemic with a single dose of STZ (52.5 mg·(kg body mass)(-1), by intraperitoneal injection). The normal group received the vehicle only. Hyperglycaemic rats were treated with Hsp (25.0, 50.0, or 100.0 mg·(kg body mass)(-1)·day(-1), per oral) and fluoxetine (Flu) (5.0 mg·(kg body mass)(-1)·day(-1), per oral) 48 h after the STZ injection, for 21 consecutive days. The normal and STZ control groups received the vehicle (distilled water). Behavioral and biochemical parameters were then assessed. When Hsp was administered to the STZ-treated rats, this reversed the STZ-induced increase in immobility duration in the forced swimming test (FST) and attenuated hyperglycaemia, decreased malondialdehyde (MDA), increased reduced glutathione (GSH) decreased interleukin-6 (IL-6), and increased BDNF levels in the brain. Treatment with Hsp attenuated STZ-induced neurochemical alterations, as indicated by increased levels of monoamines in the brain, namely, norepinephrine (NE), dopamine (DA), and serotonin (5-hydroxytryptamine; 5-HT). All of these effects of Hsp were similar to those observed with the established anti-depressant Flu. This study shows that Hsp exerted anti-depressant effect in diabetic rats, which may have been partly mediated by its amelioration of hyperglycaemia as well as its anti-oxidant and anti-inflammatory activities, the enhancement of neurogenesis, and changes in the levels of monoamines in the brain.
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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".