GCMS-based phytochemical profiling and in vitro pharmacological activities of plant Alangium salviifolium (L.f) Wang
Bibliographic record
Abstract
Abstract Background There is an urge for traditional herbal remedies as an alternative to modern medicine in treating several diseases. A significant number of modern pharmaceutical drugs are based on or derived from medicinal plants or their extracts. These drugs derived from the plant origin have various antimicrobial, antioxidant, anticancer, anti-inflammatory activities. Alangium salviifolium belongs to Cornaceae family and is well known for its medicinal properties. The present study was carried out to evaluate the antibacterial, antioxidant effect and possible bioactive components present in the chloroform, acetone, ethanol, methanol and aqueous extract of Alangium salviifolium leaves. Methodology Dried leaves of Alangium salviifolium were subjected to serial solvent extraction using increasing polarity of solvents, i.e., chloroform, acetone, methanol, ethanol, and distilled water. Crude extracts were further tested for qualitative analysis of phytochemicals using standard procedure, while GCMS analysis was performed to identify the probable phytocompounds. Antibacterial activity was performed against bacterial pathogens using agar well method, whereas antioxidant activity was performed using in vitro PM, DPPH and FRAP assays. Results Phytochemical analysis of the extracts revealed the presence of key phytochemical classes. Using gas chromatography-mass spectrometry, several high and low molecular weight chemical compound kinds were discovered. These chemical substances are regarded as having significant biological and pharmacological effects. All crude extracts had considerable and comparable in vitro antioxidant and antibacterial properties. Conclusions According to the findings of this study, Alangium salviifolium leaves are a rich source of phytoconstituents that are crucial in stopping the advancement of numerous disorders.
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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) | 0.000 | 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 | 0.000 |
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".