Mechanism and effects of artesunate on the liver function of rats with type 1 diabetic periodontitis
Bibliographic record
Abstract
Periodontitis is an inflammatory disease of the gums. Periodontitis in diabetic patients can aggravate insulin resistance; however, its molecular and biological mechanism remains unclear. This study aimed to explore the effects of diabetic periodontitis on liver function and determine the mechanism by which artesunate improves liver function. Rats with streptozotocin-induced diabetes were divided into five groups: normal control (NC), diabetic periodontitis (DM + PD), artesunate intervention (ART), insulin intervention (INS), and combined medication intervention (ART + INS) groups. Drug interventions were then administered to the rats in each group as follows: 50 mg/kg artesunate to the ART group, 6 U/kg insulin to the INS group, and 50 mg/kg artesunate + 6 U/kg insulin to the ART + INS group. Blood samples, liver tissues, and the maxillary alveolar bone were collected postsacrifice. ART was found to significantly ameliorate hyperglycemia, blood lipid concentrations, and liver function. The levels of inflammatory factors reduced; the effect was more pronounced in the ART + INS group. Artesunate presumably inhibits the TLR4/NF-κB signaling pathway and expression of downstream inflammatory factors, thereby exerting a protective effect on diabetes-related liver function. This offers a fresh approach to treat diabetes mellitus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".