Comparative In Vitro and In Silico Analyses of Phytochemicals From Butea monosperma for Wound-Healing Potential in Human Cells
Bibliographic record
Abstract
Aim The objective of this study is to investigate the phytochemicals present in Butea monosperma and assess their potential for healing wounds using a computational comparative method. Materials and methods The phytochemical substances derived from B. monosperma were examined using a phytochemical test, Fourier-transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectroscopy (GCMS). The chemical structures of these substances were investigated in silico using computational techniques to predict their wound-healing capacity. The molecular docking tests evaluate the binding strengths of the phytochemicals to specific proteins that play a major role in wound-healing mechanisms. The pharmacokinetic features of the substances were evaluated by analyzing their ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Results The computer analysis found several phytochemicals from B. monosperma that bind strongly to the proteins for wound healing: compounds such as hexanoic acid, 2,7-dimethyloct-7-en-5-yn-4-yl ester, 1,3,5-pentanetriol, 3-methyl-, and 2-butyne-1,4-diol. The ADMET analysis indicated favorable pharmacokinetic properties for the majority of the identified compounds, with low predicted toxicity. Conclusion Based on the in silico analysis, the phytochemicals in B. monosperma possess significant potential for use in wound-healing applications. These findings required additional in vitro and in vivo studies to confirm the effectiveness and safety of these drugs for improving wound healing. This study emphasizes the potential of B. monosperma as a source of innovative medicinal substances for wound care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".