A Case for the Neuroprotective Potential of African Phytochemicals in the Management of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized of cognitive dysfunction. AD is believed to be a global menace with an estimated fourfold increase in prevalence by the year 2050. This increasing prevalence is linked to the unavailability of efficient treatment to halt the disease progression. While several hypotheses have been postulated on AD, oxidative stress, a state of an imbalance between antioxidant and free radical generation, has long been implicated in the pathogenesis of age-dependent late-onset AD. This state induces cognitive decline by stimulating neuronal damage, notably involving increased free radical production, and mitochondrial dysfunction. Pharmacological agents used in AD management have serious adverse effects and inability to halt disease progression. This has led to the emergence of naturally occurring neuroprotective phytochemical agents and herbal supplements as therapeutic option agents. Indeed, emerging studies have revealed the neuroprotective potential of different African herbal products, containing bioflavonoid compounds with central nervous system permeability and high antioxidant actions. Given this background, this chapter aims to discuss some of these African antioxidant bioflavonoids\\nutraceuticals, their neuroprotective functions against different epigenetic-derived oxidative stress, and ways ahead to facilitate their translation from “bench to bedside” as primary intervention or co-adjuvant therapies for AD treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".