Sulfated and Sulfur-Containing Steroids and Their Pharmacological Profile
Bibliographic record
Abstract
The review focuses on sulfated steroids that have been isolated from seaweeds, marine sponges, soft corals, ascidians, starfish, and other marine invertebrates. Sulfur-containing steroids and triterpenoids are sourced from sedentary marine coelenterates, plants, marine sediments, crude oil, and other geological deposits. The review presents the pharmacological profile of sulfated steroids, sulfur-containing steroids, and triterpenoids, which is based on data obtained using the PASS program. In addition, several semi-synthetic and synthetic epithio steroids, which represent a rare group of bioactive lipids that have not yet been found in nature, but possess a high level of antitumor activity, were included in this review for the comparative pharmacological characterization of this class of compounds. About 140 steroids and triterpenoids are presented in this review, which demonstrate a wide range of biological activities. Therefore, out of 71 sulfated steroids, thirteen show strong antitumor activity with a confidence level of more than 90%, out of 50 sulfur-containing steroids, only four show strong antitumor activity with a confidence level of more than 93%, and out of eighteen epithio steroids, thirteen steroids show strong antitumor activity with a confidence level of 91% to 97.4%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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".