Survival and virulence of Acinetobacter baumannii in microbial mixtures
Bibliographic record
Abstract
Acinetobacter species such as A. venetianus and A. guillouiae have been studied for various biotechnology applications, including bioremediation of recalcitrant and harmful environmental contaminants, as well as bioengineering of enzymes and diagnostic materials. Bacteria used in biotechnology are often combined with other microorganisms in mixtures to formulate efficacious commercial products. However, if the mixture contained a closely related Acinetobacter pathogen such as A. baumannii (Ab), it remains unclear whether the survival and virulence of Ab would be masked or augmented. This uncertainty poses a challenge in ensuring the safety of such biotechnology products, since Ab is one of the most significant pathogens for both hospital and community -acquired infections. This research aimed to investigate the growth and virulence of Ab within a mixture of 11 bacterial species formulated as a mock microbial mixture (MM). Growth challenges with environmental stressors (i.e., temperature, pH, sodium, iron, and antibiotics) revealed that Ab could thrive under diverse conditions except in the presence of ciprofloxacin. When cultured alone, Ab exhibited significantly more growth in the presence of almost all the environmental stressors than when it was co-incubated with the MM. During the exposure of A549 lung epithelial cells to the MM, Ab growth was stimulated compared to that in standard mammalian culture media. Cytotoxicity caused by Ab was suppressed in the presence of the MM. Lymphocytes were significantly reduced in mice exposed to Ab with or without MM via intravenous injection. The levels of the splenic cytokines IL-1α, IL-1β, MCP-1, and MIP-1α were significantly reduced 24 h after exposure to Ab + MM. This study demonstrated that the presence of the MM marginally but significantly reduced the growth and virulence of Ab, which has implications for the safety of mixtures of microorganisms for biotechnological applications. Furthermore, these findings expand our understanding of the virulence of Ab during host-pathogen interactions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".