UPLC–ESI–QTOF–MS profiling, antioxidant, antidiabetic, antibacterial, anti-inflammatory, antiproliferative activities and in silico molecular docking analysis of Barleria strigosa
Bibliographic record
Abstract
Abstract Background This study investigated the in vitro antidiabetic, antioxidant, antibacterial, anti-inflammatory and antiproliferative effects of B. strigosa hydrophilic (BSTR) and lipophilic (LSB) leaves extracts. The phytochemical profile was also performed using UHPLC–ESI–QTOF–MS. Results The results indicated that BSTR and LSB showed excellent antioxidant properties in the DPPH scavenging, ABTS scavenging, FRAP and MCA assays. The extracts also demonstrated α-glucosidase (81.56–157.56 µg/mL) and α-amylase (204.44 µg/mL) inhibitory activities. In addition, the extracts showed significant cytotoxic and antiproliferative effects against oral squamous carcinoma (CLS-354/WT) cancer cells. Furthermore, the extracts showed excellent antibacterial activity against Listeria monocytogenes , Vibrio parahaemolyticus , Escherichia coli , Pseudomonas aeruginosa and Staphylococcus aureus . Both extracts exhibited a significant reduction in nitric oxide secretion against activated macrophage cells. The UHPLC–MS analysis revealed that B. strigosa is rich in terpenoids, iridoid glycosides, flavonoids, and phenolic compounds. The plethora of these compounds may be responsible for the observed activities. In addition, the bioactive compounds identified by UHPLC–ESI–QTOF–MS were analyzed using silico molecular docking studies to determine the binding affinity with α-amylase and α-glucosidase. Conclusions These results suggest that B. strigosa is an excellent pharmacological active plant and it provides the basis for further studies on the exploration of its potentials in oxidative stress induced disorders. Graphical Abstract
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".