Development of a GFP Fluorescent Bacterial Biosensor for the Detection and Quantification of Silver and Copper Ions
Bibliographic record
Abstract
Abstract Ionic silver is known to be an effective antimicrobial agent widely used in the cleaning and medical industries, however, there are several concerns regarding the release of silver pollutants into the environment. Presented here are two engineered bacterial biosensors for the detection and quantification of silver. The biosensors contain a silver resistance operon and a GFP gene that is strictly regulated through silver activated regulatory regions that control expression of the sil operons. The two biosensors are responsive to a wide range of silver ion concentrations, and a correlation between silver and GFP signal is seen at select concentration ranges. The biosensors were shown to detect silver ions released from silver nanoparticles, and have the potential to become a method for monitoring ion release rates of different nanoparticles. Interestingly, the close homology of the silver resistance and copper resistance genes allowed for the biosensor to also be responsive to copper ions, implying that copper ions activate silver resistance. Further development of this biosensor could lead to commercial applications for environmental monitoring. Importance Ionic silver is known to have many harmful environmental effects. Silver pollutants have been found in various environmental settings such as natural waterways and tailings from mining operations, raising concern. In addition, persistent exposure to silver in medical and environmental settings has led to the development of silver resistant bacteria, many of which are also resistant to a wide range of antibiotics. Some of these have the potential to develop into human pathogens. It then becomes important to have standardized methods for detecting and monitoring silver concentrations in various environments so that appropriate measures can be taken to prevent further silver ion release. This research shows that bacterial biosensors engineered to detect and quantify silver ions can be developed as effective alternatives to traditional analytical techniques. Further development of such biosensors could result in a commercial system for short and long term environmental monitoring, which is important as products containing silver and other heavy metals become increasingly popular.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".