Investigating the Chemical–Biological Link in <i>Ziziphora galinae</i> Extracts for the Discovery of Novel Raw Materials via <i>In Silico</i> and In Vitro Assays
Bibliographic record
Abstract
ABSTRACT This study examined the impact of the extraction methods (70% ethanolic extraction, infusion) on the overall biological profile and concentration of phenolic compounds of Ziziphora galinae (ZG) . Infusion yielded significantly higher phenolics and flavonoids (75.73 ± 0.22 mg GAE/g and 8.87 ± 0.36 mg RE/g) than the ethanol extract (10.44 ± 0.1 and 2.44 ± 0.17, respectively). Nineteen key bioactive compounds, including caffeoylquinic acids, rutin, and p‐coumaric acid, were identified. Both extracts exhibited strong antibacterial and moderate antifungal activity, but no notable cytotoxicity (IC50 > 400 μg/mL). Furthermore, in silico analyses involving molecular docking, molecular dynamics (MD) simulations, and MM‐PBSA free energy calculations revealed that the phytochemicals identified from ZG exhibited strong binding affinities and high structural stability against key human metabolic enzymes and essential bacterial proteins involved in cell wall biosynthesis and DNA replication. Based on these results, the varying effects produced by the two extracts of endemic ZG may be attributed to the presence of distinct compounds, making them a valuable source of bioactive compounds to benefit human health.
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 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 | 0.000 | 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) | 0.001 | 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".