Comprehensive profiling and identification of bioactive components from methanolic leaves extract of <i>Juniperus deppeana</i> and its in vitro antidiabetic activity
Bibliographic record
Abstract
Juniperus plant species are rich sources of bioactive secondary metabolites and are traditionally used for the treatment of several illnesses, including those related to hyperglycemia and diabetes. The major bioactive compounds identified in certain species of this genus are terpenes and phenolics. Juniperus deppeana Steud. is mainly used as a wood resource and its chemical composition has been partially established. Our goal was to perform a comprehensive profiling of a methanolic extract of leaves of J. deppeana and determine its potential as a source of α-amylase and α-glucosidase inhibitors. Terpene and phenolic compounds were putatively identified based on their accurate mass spectrometric data. Regarding terpenes, we found mainly diterpenes, specifically dehydroabietic acid-like, hinokiol-like, agathic acid-like, and dihydroxyabietatrienoic acid-like compounds. Isopimaric acid was also identified and its identity was confirmed by coelution with an authentic standard via comparing retention time, mass spectrum, and collisional cross section values. For phenolic compounds, we identified mainly compounds with a chemical structure similar to the biflavonoids amentoflavone and bilobetin. Besides, the methanolic extract of J. deppeana leaves show inhibition of α-amylase (IC 50 = 85.11 ± 11.91 μg mL −1 ) and α-glucosidase (IC 50 = 32.50 ± 3.40 μg mL −1 ) enzymes, demonstrating a potential alternative for the search of antidiabetic natural products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".