Improvement of inflammatory and toxic stress biomarkers by silymarin in a murine model of type one diabetes mellitus
Bibliographic record
Abstract
Abstract Type 1 diabetes mellitus (T1DM) is characterized by an impairment of the insulin-secreting beta cells with an immunologic base. Inflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β, and free radicals are believed to play key roles in destruction of pancreatic β cells. The present study was designed to investigate the effect of Silybum marianum seed extract (silymarin), a combination of several flavonolignans with immunomodulatory, anti-oxidant, and anti-inflammatory potential on streptozotocin (STZ)-induced T1DM in mouse. Experimental T1DM was induced in male albino mice by IV injection of multiplelow- doses of STZ for 5 days. Seventy-two male mice in separate groups received various doses of silymarin (20, 40, and 80 mg/kg) concomitant or after induction of diabetes for 21 days. Blood glucose and pancreatic biomarkers of inflammation and toxic stress (IL-1β, TNF-α, myeloperoxidase, lipid peroxidation, protein oxidation, thiol molecules, and total antioxidant capacity) were determined. Silymarin treatment reduced levels of inflammatory cytokines such as TNF-α and IL-1β and oxidative stress mediators like myeloperoxidase activity, lipid peroxidation, carbonyl and thiol content of pancreatic tissue in an almost dose dependent manner. No marked difference between the prevention of T1DM and the reversion of this disease by silymarin was found. Use of silymarin seems to be helpful in T1DM when used as pretreatment or treatment. Benefit of silymarin in human T1DM remains to be elucidated by clinical trials.
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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 | 0.001 | 0.002 |
| 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".