Natural Compounds Oridonin and Shikonin Exhibit Potentially Beneficial Regulatory Effects on Select Functions of Microglia
Bibliographic record
Abstract
Accumulating evidence indicates that the adverse neuroimmune activation of microglia, brain immunocytes that support neurons, contributes to a range of neuroinflammatory disorders, including Alzheimer's disease. Correcting the abnormal functions of microglia is a potential therapeutic strategy for these diseases. Nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor (NLRP) 3 inflammasomes are implicated in adverse microglial activation and their inhibitors, such as the natural compounds oridonin and shikonin, reduce microglial immune responses. We hypothesized that some of the beneficial effects of oridonin and shikonin on microglia are independent of their suppression of NLRP3 inflammasomes. Murine and human microglia-like cells were stimulated with bacterial lipopolysaccharide (LPS) only, which did not induce NLRP3 inflammasome activation or the resulting secretion of interleukin (IL)-1β, allowing for the identification of other anti-inflammatory effects. Under these experimental conditions, both oridonin and shikonin reduced nitric oxide (NO) secretion and the cytotoxicity of BV-2 murine microglia towards HT-22 murine neuronal cells, but upregulated BV-2 cell phagocytic activity. Only oridonin inhibited the secretion of tumor necrosis factor (TNF) by stimulated BV-2 microglia, while only shikonin suppressed the respiratory burst response of human HL-60 microglia-like cells. This observed discrepancy indicates that these natural compounds may have different molecular targets in microglia. Overall, our results suggest that oridonin and shikonin should be further investigated as pharmacological agents capable of correcting dysfunctional microglia, supporting their potential use in neuroinflammatory disorders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".