Effects and Mechanisms of Dietary Natural Products on Ischemic Stroke: An Updated Review
Bibliographic record
Abstract
Ischemic stroke ranks among the primary contributors to mortality and prolonged disability globally, representing a significant public health challenge. Some clinical drugs for the treatment of ischemic stroke have significant side effects. Therefore, exploring effective therapeutic strategies is crucial. Some dietary natural products, such as fruits, vegetables, teas, herbs, nuts, probiotics and prebiotics, exert potential neuroprotective effects on ischemic stroke. The underlying mechanisms of action include suppressing oxidative stress, inhibiting inflammation, alleviating excitotoxicity, promoting angiogenesis, protecting blood-brain barrier, regulating gut microbiota, attenuating apoptosis, inhibiting autophagy, suppressing platelet aggregation and thrombosis, and improving mitochondrial function. This review mainly summarizes recent advancements in the potential therapeutic effects and mechanisms of dietary natural products on ischemic stroke. Additionally, it highlights future research directions, including the synergistic effects of combining dietary natural products, as well as the incorporation of nanotechnology to enhance bioavailability and targeted delivery. Overall, this review provides a useful reference for the application of dietary natural products in the prevention and management of ischemic stroke.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".