Comparisons of <i>Scutellaria baicalensis, Scutellaria lateriflora</i> and <i>Scutellaria racemosa:</i> Genome Size, Antioxidant Potential and Phytochemistry
Bibliographic record
Abstract
The genus Scutellaria in the family Lamiaceae has over 350 species, many of which are medicinally active. One species, Scutellaria baicalensis, is one of the most widely prescribed plants in Traditional Chinese Medicine, used for neurological disorders, cancer and inflammatory diseases and has been the subject of detailed scientific study but little is known about the phytochemistry of other Scutellaria. The current study was designed to compare the medicinal phytochemistry of 3 species of Scutellaria used to treat neurological disorders. To accomplish this objective, the specific objectives were (a) to establish an in vitro collection of the South American native; S. racemosa, (b) to botanically characterize S. racemosa and (c) to compare the phytochemistry of S. racemosa with S. baicalensis and S. lateriflora. S. racemosa was established in vitro from wild populations in Florida. Botanically, S. racemosa is diploid with 18 chromosomes, and flow cytometry data indicated that S. baicalensis and S. racemosa have small nuclei with estimated small genomes (377 mbp and 411 mbp respectively). Antioxidant potential studies showed that there were no significant differences in the 3 Scutellaria species. Phytochemical analyses detected and quantified the flavonoids baicalin, baicalein, scutellarin, and wogonin as well as the human neurohormones melatonin and serotonin in leaf and stem tissues from S. baicalensis, S. lateriflora, and S. racemosa. These findings represent the first phytochemical analysis of S. racemosa and establish S. racemosa as a model system for study of medicinal plant secondary metabolism and as a potential source of important phytopharmaceuticals for treatment of human disease.
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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 | 0.000 |
| 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".