Multiple inflammatory and antiviral activities in Adansonia digitata (Baobab) leaves, fruits and seeds
Bibliographic record
Abstract
Adansonia digitata (Baobab) is a traditional African medicinal plant with numerous applications, including treatment of symptoms of infectious diseases. Standardized commercial preparations of Adansonia digitata leaves, fruit-pulp and seeds were acquired and extracted with three different solvents, water, methanol and DMSO. The extracts were compared quantitatively for antiviral MIC100 (minimal inhibitory concentration) values against influenza virus, herpes simplex virus and respiratory syncytial virus and for their effects on cytokine secretion (IL-6 and IL-8) in human epithelial cell cultures. The leaf extracts had the most potent antiviral properties, especially the DMSO extracts and influenza virus was the most susceptible virus. Pulp and seed extracts were less active but significant. Cytotoxic activities were only evident at much higher concentrations of extract. Several of the extracts, especially leaf extracts, were also active as cytokine modulators, some being pro-inflammatory and others being anti-inflammatory. The results overall indicated the presence of multiple bioactive compounds in different parts of the plant and these activities could explain some of the medical benefits attributed to traditional leaf and pulp preparations, in the treatment of infectious diseases and inflammatory conditions. Key words: Adansonia digitata, Baobab, antiviral, inflammatory, cytokines.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".