Extraction of bioactive moieties of Cupressus arizonica and Cupressus sempervirens wood knots
Bibliographic record
Abstract
This research was aimed to determine the hydrophilic bioactive extractives of Arizona cypress. The extractives of Arizona cypress were isolated and characterized by gas chromatography-mass spectrometry (GC-MS). Hydrophilic compounds of the extractives were mildly isolated by soaking the wood flour in ethanol: water (9:1 v/v) solution followed by n-hexane extraction to remove the lipophilic moieties. Raw extract of Arizona cypress was further purified to isolate the bioactive phenols using dichloromethane-ethanol in a solvent-solvent system and precipitation with potassium acetate. The bioactivity of the hydrophilic extracts of Cupressus arizonica was determined and compared with the raw hydrophilic extractives of Cupressus sempervirens and Picea excelsa. The total phenol content was determined according to the folin-ciocalteu method. The antioxidant capacity was determined by iron (II) chelating activity and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. From the GC/MS analysis, different amounts of bioactive moieties, including matairesinol (MAT), curcumin, dienestrol, arctigenin (ARC) and sescoisolariciresinol (SEC), were found in the extract of C. arizonica wood knots. Comparative evaluation of the total phenolics by folin-ciocalteu analysis showed that extraction by simple soaking could precisely indicate the quantity of phenolic compounds in the extracts. The antioxidant activity of extracts indicated by DPPH radical scavenging and iron (II) chelating capacity showed that the antioxidant activity is dependent on the amount and category of bioactive phenols in the extracts.
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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) | 0.001 | 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".