Novel compounds from endophytic fungi of Ceriops decandra inhibit breast cancer cell growth through estrogen receptor alpha in in-silico study
Bibliographic record
Abstract
Endophytic fungi can thrive without producing illness or injury to the host plant tissues, and they are also one of the major natural sources of bioactive compounds. The objective of the present study was to assess the anticancer potential of fungal extracts obtained from Ceriops decandra and in-silico-based identification of the anticancer constituents from those fungal extracts. Endophytic fungi were isolated and identified by internal transcribed spacer (ITS-rDNA) sequence analysis. The fungal crude extract was subjected to column chromatography and different fractions were screened for bioactive compounds using Gas Chromatography Mass Spectroscopy (GCMS). Fusarium oxysporum, Chlonostachys sp., Fusarium solani were identified from C. decandra. Eighty (80) compounds were identified from these endophytic fungi through GCMS analysis. A molecular docking study of the identified compounds confirmed the anti-cancer potential of breast cancer cells through the estrogen receptor, with good binding affinities (−9.17 to −7.19 kcal/mol) and interaction patterns. Furthermore, in molecular dynamic simulation studies, Isoparvifuran (CID_617473) and 2,4-di-tert-butylphenol (CID_7311) revealed a stable complex with the target protein of estrogen receptor alpha. The result of the study showed that C. decandra harbors diverse group of endophytic fungi that can be a potential source of bioactive (Isoparvifuran and 2,4-di-tert-butylphenol) pharmaceuticals to treat breast cancer through modulation of estrogen receptor alpha.
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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 | 0.000 | 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".