Marine-derived antimicrobial molecules from the sponges and their associated bacteria
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is one of the leading global health issues that demand urgent attention. Very soon the world will have to bear the consequences of increased drug resistance if new anti-infectives are not pumped into the clinical pipeline in a short period. This presses on the need for novel chemical entities, and the marine environment is one such hotspot to look for. The Ocean harbours a variety of organisms, of which from this aspect, "Sponges (Phylum Porifera)" are of particular interest. To tackle the stresses faced due to their sessile and filter-feeding lifestyle, sponges produce various bioactive compounds, which can be tapped for human use. The sponges harbour several microorganisms of different types and in most cases; the microbial symbionts are the actual producers of the bioactive compounds. This review describes the alarming need for the development of new antimicrobials and how marine sponges can contribute to this. Selected antimicrobial compounds from the marine sponges and their associated bacteria have been described. Additionally, measures to tackle the supply problem have been covered, which is the primary obstacle in marine natural product drug discovery.
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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 | 0.002 |
| 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) | 0.004 | 0.002 |
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".