Comparative assessment of growth media and incubation conditions for enhanced recovery and isolation of Acinetobacter baumannii from aquatic matrices
Bibliographic record
Abstract
Acinetobacter baumannii causes serious multidrug resistant nosocomial infections around the world. This comprehensive comparative study was designed to assess the effect of temperature (30, 37 and 42 °C), incubation (aerobic and microaerobic) condition and selective [CHROMagar Acinetobacter (CHR) and Leeds Acinetobacter Medium (LAM)] and non-selective [Modified Karmali Agar (MKA)] growth media on the enhanced recovery of A. baumannii from a variety of water (agricultural, recreational, raw drinking intake source, pre-chlorinated and post-chlorinated wastewater effluent) samples spiked with a known number of A. baumannii cells. After spiking each water type with a known number of cells in 10 mL volume, the sample was passed through a membrane filter (pore size 0.45 μm) and filters were placed on different selective media plates and subjected to incubate at various incubation conditions. The results reported in this study show that for all water types tested (except post-chlorinated wastewater effluent), LAM was the most effective selective growth medium in combination with variable temperature and incubation conditions for yielding high recovery rates of A. baumannii cells. Overall, A. baumannii showed that it has a high adaptive capacity to grow on selective and non-selective growth media at different temperature and incubation conditions. The data described in this study suggest that no single incubation condition and growth media would efficiently recover A. baumannii from all environmental water types tested. This data also indicate that selective growth media and incubation condition can significantly affect the recovery of A. baumannii. Differences in recovery of A. baumannii observed in this study which appeared to be dependent on the temperature and environmental characteristics of incubation as well as the sample type, suggest the need for caution when comparing recovery using different protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".