Unveiling the Hidden Allies in the Fight Against Antimicrobial Resistance—Medicinal Plant Endophytes
Bibliographic record
Abstract
Medicinal plants have long been a vital source of various natural products in the form of pure compounds or standardized extracts. The World Health Organization estimated that 80% of populations in Africa, Asia, and Latin America rely on traditional medicine for primary health care. In recent decades, endophytic microorganisms living within plants have gained attention for their ability to produce bioactive compounds with significant therapeutic potential. This review explores the diversity of medicinal plant endophytes, focusing on their pharmacological significance, including antimicrobial, anticancer, antidiabetic, and antioxidant properties. Additionally, we discuss the application of nanotechnology and computational tools in enhancing the potency and screening of endophyte-derived metabolites. Despite the promising potential, challenges such as scalability, safety, and commercial viability remain. Future research should prioritize optimizing production, elucidating biosynthetic pathways, and integrating advanced technologies to effectively harness these bioactive compounds for novel drug development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".