Investigation of the impact of bacterial microencapsulation on naturalproduct discovery
Bibliographic record
Abstract
Developing more effective drugs for patient care seems significantly critical, due to an alarming increase rate of antibiotic resistance and spread of cancer in society. Despite the promising potential of environmental microorganisms to produce such drugs, researchers are currently facing the problem of unveiling previously known compounds. To address this challenge, the impact of microencapsulation on natural compound production was explored on a less broadly studied strain, Kitasatospora cystarginea NRRL B-16505 to increase the likelihood of producing novel compounds. The current work postulates that the stress of microencapsulation process may induce secondary metabolism, potentially leading to the production of novel metabolites. Different microencapsulation techniques including microfluidics, co-axial air flow printing and emulsification were compared in terms of bead size, viability, metabolite profile and yields. This study has shown promising results leading to the discovery of new bioactive compounds as well as activating the silent pathways for compound production. It seems plausible to consider that nutrition deficiency, reduced motility, presence of salt, heat shock, bead uniformity and shear stress during microencapsulation are potential reasons for the production of these putative new chemicals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".