Bibliographic record
Abstract
This review is devoted to the study of the biological activity of polyether ionophores produced by bacteria, unicellular marine algae, red seaweeds, marine sponges, and coelenterates. Biological activities have been studied experimentally in various laboratories, as well as data obtained using QSAR (Quantitative Structure-Activity Relationships) algorithms. According to the data obtained, it was shown that polyether toxins exhibit strong antibacterial, antimicrobial, antifungal, antitumor, and other activities. Along with this, it was found that natural polyether ionophores exhibit such properties as antiparasitic, antiprotozoal, cytostatic, anti-mycoplasmal, and antieczema activities. In addition, polyethers have been found to be potential regulators of lipid metabolism or inhibitors of DNA synthesis. Further study of the mechanisms of action and the search for new polyether ionophores and their derivatives may provide more effective therapeutic natural polyether ionophores for the treatment of cancer and other diseases. For some polyether ionophores, 3D graphs are presented, which demonstrate the predicted and calculated activities. The data presented in this review will be of interest to pharmacologists, chemists, practical medicine, and the pharmaceutical industry.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".