Sphingomyelinase inhibitory and free radical scavenging potential ofselected Nigerian medicinal plant extracts
Bibliographic record
Abstract
Ceramides from sphingolipid breakdown, and other sphingolipid metabolites, mediate cellular signalling in infectious and other diseases. Therefore, inhibitors of sphingomyelinases (SMases), hold promise as prospective therapeutic agents. Considering the potential therapeutic utility, this in vitro study explored the sphingomyelinase inhibitory, and free radical scavenging potential of five Nigerian medicinal plant leaf extracts, purported to have efficacy against diseases, including HIV/AIDS. The extracts’ sphingomyelinase inhibitory potencies were assessed colorimetrically and theirfree radical scavenging capabilities were assayed by the ability to quench 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radical and superoxide anion (O2.‐) radical. Considering their IC50 (μg/ml) values, the extracts inhibited the biochemical activity of sphingomyelinase in a dose-dependent manner, relative to imipramine the standard inhibitor (IC50 38.5 ± 2.4 μg/ml). With Aloe vera as least inhibitory, inhibition increased as follows: Aloe vera (Asphodelaceae) (1132 ± 10.8) < Senna siamea (Fabaceae) (992.2 ± 11.2) < Azadirachta indica (Meliaceae) (984 ± 7.4) < Landolphia owariensis (Apocynaceae) (146.3 ± 9.4) < Stachytarpheta angustifolia (Verbenacae) (100.3 ± 8.7). DPPH radical scavenging relative to ascorbic acid standard increased as: A. indica < A. vera < S. siamea < S. angustifolia < L. owariensis; and superoxide anion quenching, relative to standard rutin increased as: A. vera < S. angustifolia < L. owariensis < S. siamea < A. indica.These results showed thatthe most potent SMase inhibitor was S. angustifolia; whereas, for DPPH radical scavenging and superoxide inhibition, the most potent of the five extracts were L. owariensis and A. indica respectively.These extracts deserve further investigation into their biological effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".