Isolation and identification of promising antibiotic-producing bacteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Multiple stresses in waste dumpsite soils can drive antibiotic production as one of the strategies for survival. Bacteria are the most prolific producers of antibiotics. This study investigated the antibiotic production potential of bacteria isolated from Bahir Dar city municipal solid waste dumpsite (MSWDS). Bacteria were isolated from soil collected from the dumpsite on starch casein or nutrient agar. The isolates were carefully screened for antimicrobial activity against six pathogenic bacterial test strains. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were also determined from cell-free metabolites of the most promising isolates. Isolates showing antimicrobial activity were identified using cultural and biochemical methods. A total of 143 distinctive colonies were obtained and tentatively identified to 13 bacterial genera. Twenty-six (18.18%) of the isolates (six Bacillus and 20 actinobacteria related) demonstrated antimicrobial activities at least against one of the tested bacterial strains. These isolates were related to two actinobacterial and 11 other bacterial genera. Seven out of 26 isolates showed a broad-spectrum of antibiotic activities. Two isolates, which showed a wide spectrum, were selected for the MIC and MBC tests against Escherichia coli and Staphylococcus aureus . The MIC and MBC of the isolates were recorded to be 250–500 µg/mL against the test strains. Bahir Dar city MSWDS contained a high incidence of antibiotic-producing bacteria. Strain level identification of the isolates and detailed characterization of the metabolites will give a good insight into the antimicrobial production potential in the waste dumpsite.
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.
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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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