Herbal Medicine and Medicinal Herbs: Dominance in the Treatment of Sickle Cell Anaemia
Bibliographic record
Abstract
Sickle cell disease (SCD) is a genetic blood disorder impacting the shape and movement of red blood cells in the blood vessels, which leads to various health issues. Current drugs for SCD treatment often fall short in terms of effectiveness, safety, and affordability. Therefore, there's a growing need to explore indigenous plant-based remedies from traditional medicine. SCD affects millions globally, and due to limited progress in drug discovery, patients frequently turn to traditional Ayurvedic treatments utilizing plants and plants extracts. Complementary and alternative medicine (CAM) has gained global attention, particularly for chronic diseases like SCD. Sickle cell anaemia has been managed with natural products, including herbs and Ayurvedic medicines. Established treatments for SCA involve hydroxyurea, folic acid supplementation, but they can be expensive and pose certain risks. Research into medicinal plants with anti-sickling properties has shown promise, offering an alternative approach to reduce crises, reverse red blood cell sickling and improve the quality of life.[1] This paper discusses the substantial benefits of Phyto-medicine, nutraceuticals and ayurvedic herbs in managing SCD, with a focus on traditional Ayurvedic medicines.[2]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".