Chemical composition and anti-herpes simplex virus type 1 (HSV-1) activity of extracts from Cornus canadensis
Bibliographic record
Abstract
Many plants of boreal forest of Quebec have been used by Native Americans to treat a variety of microbial infections. However, the antiviral activities of these plants have been seldom evaluated on cellular models to validate their in vitro efficiencies. In this study, Cornus canadensis L. (Cornaceae), a plant used in Native American traditional medicine to treat possible antiviral infections, has been selected for further examination. The plant was extracted by decoction and infusion with water, water/ethanol 1:1 and ethanol to obtain extracts similar to those used by Native Americans. The effects of the extracts were tested on herpes simplex virus type-1 (HSV-1) using a plaque reduction assay. Moreover, bioassay-guided fractionation was achieved to isolate bioactive compounds. Water/ethanol 1:1 infusion of C. canadensis leaves were the most active extracts to inhibit virus absorption with EC 50 of about 9 μg mL −1 , whereas for direct mode, both extraction methods using water or water/ethanol 1:1 as solvent were relatively similar with EC 50 ranging from 11 to 17 μg mL −1 . The fractionation led to the identification of active fractions containing hydrolysable tannins. Tellimagrandin I was found the most active compound with an EC 50 of 2.6 μM for the direct mode and 5.0 μM for the absorption mode. Altogether, the results presented in this work support the antiviral activity of Cornus canadensis used in Native American traditional medicine.
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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 | 0.001 |
| 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".