“FTIR, Uv- Vis And GCMS Analysis Of Potential Bioactive Compounds From Tinospora Cordifolia And Its Antibacterial Activity”
Bibliographic record
Abstract
Medicinal plants are assuming greater importance in the primary health care of individuals and communities in many developing countries. Endophytes are the microorganisms inhabiting the living tissues of plants. Endophytic fungi from medicinal plants have created a huge potential in generating novel drugs. The secondary metabolites and their semi-synthetic derivatives play an important role in anticancer drug therapy. The present study is investigated for the isolation of endophytes from the inner bark of twigs and leaves of Tinospora cordifolia, collected from the Shankaraghatta regions, Shivamogga district of Karnataka and examined as a potential source of anticancer drug lead compound. Bioactive components of Tinospora cordifolia have been evaluated using GCMS, UV-VIS and FTIR and its antibacterial activity. GC-MS analysis revealed the chemical profile of extract of different compound, distinct peak, retention time (RT), molecular formula, molecular weight (MW) and chemical structure. The GC–MS analysis of methanol extracts detected the presence of 30 phytochemical compounds. The UV-VIS profile showed the presence of peaks at 190-500nm revealing the presence of secondary metabolites in T. cordifolia. The results of FTIR analysis confirmed the presence of phenol, alkanes, alkenes, alcohol, aromatic, aliphatic amines and amine compound. The results show that important bioactive compounds present in plant extract and these constituents may be responsible for pharmacological activities.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".