Molecular-Based Identification of Actinomycetes Species That Synthesize Antibacterial Silver Nanoparticles
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Bibliographic record
Abstract
Infectious diseases caused by antibiotic-resistant bacteria lead to a considerable increase in human morbidity and mortality globally. This requires to search potential actinomycete isolates from undiscovered habitats as a source of effective bioactive metabolites and to synthesis metabolite-mediated antibacterial silver nanoparticles (AgNPs). The main purpose of the present study was to identify actinomycetes isolated from Thika waste dump soils that produce bioactive metabolites to synthesize antibacterial AgNPs. The synthesis of metabolite-mediated AgNP was confirmed with visual detection and a UV-vis spectrophotometer, whereas the functional groups involved in AgNP synthesis were identified using a FTIR spectrophotometer. The antibacterial activity of the metabolite-mediated AgNPs was tested by a well diffusion assay. Identification of actinomycete isolates involved in the synthesis of antibacterial AgNPs was done based on 16S rRNA gene sequence analysis. The visual detection showed that dark salmon and pale golden color change was observed due to the formation of AgNPs by KDT32 and KGT32 metabolites, respectively. The synthesis was confirmed by a characteristic UV spectra peak at 415.5 nm for KDT32-AgNP and 416 nm for KGT32-AgNP. The FTIR spectra revealed that OH, C=C, and S-S functional groups were involved in the synthesis of KDT32-AgNP, whereas OH, C=C, and C-H were involved in the formation of KGT32-AgNP. The inhibition zone results revealed that KDT32-AgNP showed 22.0 ± 1.4 mm and 19.0 ± 1.4 mm against Escherichia coli and Salmonella typhi, whereas KGT32-AgNP showed 21.5 ± 0.7 mm and 17.0 ± 0.0 mm, respectively. KDT32 and KGT32 isolates were identified as genus Streptomyces and their 16S rRNA gene sequences were deposited in the GenBank database with MH301089 and MH301090 accession numbers, respectively. Due to the bactericidal activity of synthesized AgNPs, KDT32 and KGT32 isolates can be used in biomedical applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it