Schisandrin B Attenuates Diabetic Cardiomyopathy by Targeting MyD88 and Inhibiting MyD88‐Dependent Inflammation
Bibliographic record
Abstract
Diabetes manifests as chronic inflammation and leads to the development diabetic cardiomyopathy (DCM). Targeting key proteins in inflammatory signaling may provide new therapy for DCM. In this study, the authors explore the pharmacological effects and mechanisms of Schisandrin B (Sch B), a natural compound with anti-inflammatory activity against DCM. It is shown that Sch B prevents high-level glucose (HG)-induced hypertrophic and fibrotic responses in cultured cardiomyocytes. RNA sequencing and inflammatory qPCR microarray show that Sch B mainly affects myeloid differentiation primary response 88 (MyD88)-dependent inflammatory gene expression in HG-challenged cardiomyocytes. Further studies indicate that Sch B directly binds to and inhibits MyD88 activation, but does not alter MyD88-independent Toll-like receptor signaling in vivo and in vitro. Inhibiting or silencing MyD88 is associated with reduced levels of HG-induced inflammatory cytokines and myocardial injuries in vitro. Treatment of type 1 and type 2 diabetic mice with Sch B protects heart function, reduces myocardial injuries, and decreases secretion of inflammatory cytokines. Cardiomyocyte-specific MyD88 knockout also protects mice against cardiac inflammation and injury in type 1 diabetic mice. In conclusion, these studies show that cardiomyocyte MyD88 plays an apathogenetic role in DCM and Sch B specifically targets MyD88 to reduce inflammatory DCM.
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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".