A polysaccharide extract from the medicinal plant Maidong inhibits the IKK–NF-κB pathway and IL-1β–induced islet inflammation and increases insulin secretion
Bibliographic record
Abstract
The herb dwarf lilyturf tuber (Maidong, Ophiopogonis Radix ) is widely used in Chinese traditional medicine to manage diabetes and its complications. However, the role of Maidong polysaccharide extract (MPE) in pancreatic β-cell function is unclear. Here, we investigated whether MPE protects β-cell function and studied the underlying mechanisms. We treated db/db and high-fat diet (HFD)-induced obese mice with 800 or 400 mg/kg MPE or water for 4 weeks, followed by an oral glucose tolerance test. Pancreas and blood were collected for molecular analyses, and clonal MIN6 β-cells and primary islets from HFD-induced obese mice and normal chow diet–fed mice were used in additional analyses. In vivo , MPE both increased insulin secretion and reduced blood glucose in the db/db mice but increased only insulin secretion in the HFD-induced obese mice. MPE substantially increased the β-cell area in both models (3-fold and 2-fold, p < 0.01, for db/db and HFD mice, respectively). We observed reduced nuclear translocation of the p65 subunit of NF-κB in islets of MPE-treated db/db mice, coinciding with enhanced glucose-stimulated insulin secretion (GSIS). In vitro , MPE potentiated GSIS and decreased interleukin 1β (IL-1β) secretion in MIN6 β-cells. Incubation of MIN6 cells with tumor necrosis factor α (TNFα), interferon-γ, and IL-1β amplified IL-1β secretion and inhibited GSIS. These effects were partially reversed with MPE or the IκB kinase β inhibitor PS1145, coinciding with reduced activation of p65 and p-IκB in the NF-κB pathway. We conclude that MPE may have potential for therapeutic development for β-cell protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".