An Evidence-based Systematic Review of Pleiotropic Potential Health Benefits of <i>Sorghum bicolor</i> Supplement: A Polyphenol-rich Derivative of the Leaf Sheaths of <i>Sorghum</i> Plant
Bibliographic record
Abstract
Globally, across different cultures, humans have historically depended largely on medicinal plants for managing diseases that have hitherto threatened their optimal health, survival, and longevity. Evidently, the health-derived benefits of medicinal plants have been strongly attributed to the presence of secondary metabolites, particularly polyphenols. The potential health benefits of the leaf sheaths of the West African variety of Sorghum bicolor-based Jobelyn Supplement (SBJS) have also been ascribed to its high contents of polyphenols. This systematic review seeks to synthetically harmonize findings from various experimental and clinical studies on the health benefits of SBJS in different disease conditions including arthritis, cancer, chronic viral infections, stroke, anaemia, and premature aging. A systematic search was conducted using three primary databases (PubMed, Europe PMC, and Cochrane Library), to identify published articles on therapeutic potentials of SBJS and ethnomedicinal surveys on the application of the West African variety of S. bicolor using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) standard. The inclusion criteria were experimental and clinical studies conducted on SBJS and West African variety of S. bicolor; while ethnomedicinal surveys were on the therapeutic uses of the West African variety of S. bicolor published in the English language. The review provides valuable information suggesting that SBJS possesses pleiotropic therapeutic potentials in diverse pathological conditions through mechanisms relating to antioxidant, anti-inflammatory, immunomodulatory, chemopreventive, and neuroprotective activities. The review also showed that SBJS contains several bioactive substances with polyvalent pharmacological potentials including modulation of pathological mechanisms involved in the mediation of aging and age-related diseases, such as arthritis, stroke, memory loss and cancer as well as chronic viral infections. Taken together, these findings further suggest the need for more robust studies (including disease-specific clinical trial programs) in order to replicate and validate the prior insights gleaned from previous investigations on SBJS.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".