Bibliographic record
Abstract
This review article delves into the realm of furanosteroids and related isoprenoid lipids derived from diverse terrestrial and marine sources, exploring their wide array of biological activities and potential pharmacological applications. Fungi, fungal endophytes, plants, and various marine organisms, including sponges, corals, molluscs, and other invertebrates, have proven to be abundant reservoirs of these compounds. The biological activities exhibited by furanosteroids and related lipids encompass anticancer, cytotoxic effects against various cancer cell lines, antiviral, and antifungal effects. Notably, the discovery of exceptional compounds such as nakiterpiosin, malabaricol, dysideasterols, and cortistatins has revealed their potent anti-tuberculosis, antibacterial, and anti-hepatitis C attributes. These compounds also exhibit activity in inhibiting protein kinase C, phospholipase A2, and eliciting cytotoxicity against cancer cells. This comprehensive study emphasizes the significance of furanosteroids and related lipids as valuable natural products with promising therapeutic potential. The remarkable biodiversity found in both terrestrial and marine ecosystems offers an extensive resource for unearthing novel biologically active compounds, paving the way for future drug development and advancements in biomedical research. This review presents a compilation of data obtained from various studies conducted by different authors who employed the PASS software 9.1 to evaluate the biological activity of natural furanosteroids and compounds closely related to them. The utilization of the PASS software in this context offers valuable advantages, such as screening large chemical libraries, identifying compounds for subsequent experimental investigations, and gaining insights into potential biological activities based on their structural features. Nevertheless, it is crucial to emphasize that experimental validation remains indispensable for confirming the predicted 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".