Effects of kisspeptin on diabetic rat platelets
Bibliographic record
Abstract
Hyperglycemia, hyperlipidemia, and free radicals result in platelet activation and atherogenesis. Kisspeptin (KP) is able to regulate metabolism, hemostasis, and the development of atherosclerosis. We examined whether platelet aggregation of streptozotocin-induced diabetic rats depends on the inducer type and if KP-13 and RF-9 (a kisspeptin receptor modifier) can influence platelet function. We measured the speed and the maximum of aggregation, along with the area under the curve. Serum glucose and calcium levels and urine formation of diabetic animals increased, while the body mass and platelet count decreased. Collagen was the most effective inducer of platelet aggregation. The aggregability of nondiabetic platelets was elevated in the presence of 5 × 10−8 mol/L KP-13. This effect was less expressed in diabetic animals. The effectivity of RF-9 was stronger than that of KP-13 in nondiabetic platelets, however it was ineffective in diabetic animals. RF-9 pre-treatment did not change the effects of 5 × 10−8 mol/L KP-13 in either animal group. The in vivo activation of diabetic platelets, which may be due to elevated serum calcium, induces thrombocytopenia and may lead to reduced in vitro aggregability. We could not demonstrate the antagonistic effect of RF-9 against KP-13 in isolated platelets.
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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".