Can Natural Products Exert Neuroprotection without Crossing the Blood–Brain Barrier?
Bibliographic record
Abstract
The scope of evidence on the neuroprotective impact of natural products has been greatly extended in recent years. However, a key question that remains to be answered is whether natural products act directly on targets located in the central nervous system (CNS), or whether they act indirectly through other mechanisms in the periphery. While molecules utilized for brain diseases are typically bestowed with a capacity to cross the blood-brain barrier, it has been recently uncovered that peripheral metabolism impacts brain functions, including cognition. The gut-microbiota-brain axis is receiving increasing attention as another indirect pathway for orally administered compounds to act on the CNS. In this review, we will briefly explore these possibilities focusing on two classes of natural products: omega-3 polyunsaturated fatty acids (n-3 PUFAs) from marine sources and polyphenols from plants. The former will be used as an example of a natural product with relatively high brain bioavailability but with tightly regulated transport and metabolism, and the latter as an example of natural compounds with low brain bioavailability, yet with a growing amount of preclinical and clinical evidence of efficacy. In conclusion, it is proposed that bioavailability data should be sought early in the development of natural products to help identifying relevant mechanisms and potential impact on prevalent CNS disorders, such as Alzheimer's disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".