Diosmetin alleviates liver inflammation by improving liver sinusoidal endothelial cell dysfunction
Bibliographic record
Abstract
Abstract Background & Aims Tumor necrosis factor-alpha (TNFα) induces pro-inflammatory activation in liver sinusoidal endothelial cells (LSEC) and liver inflammation. However, knowledge about whether modulating LSEC activation can alleviate liver inflammation is scarce. This study aimed to establish and validate an animal model mimicking LSEC dysfunction observed in patients with elevated plasma levels of TNFα, and explore whether vasoactive flavonoid diosmetin could serve as a therapeutic agent for liver inflammation. Approach & Results Genetic deletion of Mcpip1 in myeloid leukocytes (Mcpip1 fl/fl LysM Cre ) resulted in the development of systemic and liver inflammation in mice. Symptoms were compared with those in liver samples from obese humans with elevated TNFα. Mice were treated with diosmetin, and its effectiveness in alleviating liver inflammation was evaluated. Elevated TNFα correlated with reduced Mcpip1 expression in peripheral blood mononuclear cells and LSEC dysfunction in obese patients. Mcpip1 knockout in myeloid cells in mice replicated molecular signs observed in human samples. Diosmetin efficiently reduced LSEC activation and liver inflammation in Mcpip1 fl/fl LysM Cre mice. Diosmetin’s effects may stem from inhibiting NF-κB-p50 subunit production in TNFα-activated endothelial cells. Conclusions Diosmetin treatment efficiently restricted liver inflammation, despite ongoing systemic inflammation, by diminishing LSEC dysfunction. Mcpip1 fl/fl LysM Cre mice mimic symptoms of liver inflammation observed in humans and can be useful in studies on new anti-inflammatory therapies for the liver. We show that diosmetin, a vasoactive flavonoid that is successfully used in the clinic to treat chronic venous insufficiency, has also strong anti-inflammatory properties in the liver. This suggests that diosmetin treatment may be tested in humans as a supportive therapy for liver inflammation. Graphical abstract
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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".