Use of Personal Glucose Meters for the Detection of E. Coli in Water
Bibliographic record
Abstract
Contamination of potable water with pathogenic bacteria such as Escherichia coli ( E.coli ) is a major concern in the developing countries. Often water is consumed without any prior treatment leading to several water borne ailments such as diarrhea. According to United States Environmental Protection Agency (USEPA), the total concentration of E.coli in potable water should be restricted to 0 Colony Forming Units (CFU) per 100 ml of water for potable water and 126 CFU /100 ml for recreational water. The conventional methods of pathogen detection generally involve transporting the contaminated water samples to centralized laboratories where detection and quantification techniques are based on instant culturing of bacteria, enzymatic reactions, and molecular (immunological or genetic) methods of detection. These processes often take 24 – 48 hours to produce results and are often expensive due to the additional requirements of resources and qualified personnel. Such a system for water monitoring is not suitable for low-resource settings. Rapid, easy to use and inexpensive detection systems need to be developed in order to empower individual households to regularly monitor their bacteriological water quality. One of the best examples of such rapid and simple detection systems is the Personal Glucose meter (PGM), which is used by patients with diabetes to regularly monitor their blood glucose levels. This system is based on an electrochemical reaction where in the glucose present in the sample is made to react with an enzyme electrode containing glucose oxidase and the resulting change in the current is measured, which in turn can be correlated to the concentration of the glucose. We have developed a simple method for the detection of E.coli and total coliform using PGMs by monitoring the consumption of glucose as a carbon source by E.coli during their growth cycle. Contaminated water samples with bacterial concentrations in the range of 2-2x10 8 CFU/mL were supplied with glucose solutions with known concentrations and Lauryl Tryptose (LT) broth as the growth medium in order to induce the consumption of glucose by E.coli . The drop in glucose concentrations in these samples was measured every hour with a PGM. It was observed that samples with very high concentrations of E.coli (2x10 6 - 2x10 8 CFU/mL) showed a drop in the glucose concentrations within an hour and samples with extremely low E.coli concentrations (2 CFU/mL) showed a drop in glucose concentrations within a maximum of 8 hours. This method can provide qualitative as well as quantitative results to determine the level of contamination in potable water. The time required to produce results with this method is much lower than the conventional colony counting method and the cost involved is quite less compared to the equipment required for ELISA plate readers. The current PGMs available in the market are portable and can produce reliable quantitative results. These systems are quite robust, simple and can be used by any untrained person. PGMs have also been recently integrated with smart phones thereby resulting in a further increase in their base of users. The E.coli detection kit presented here is simple, easy to use, reliable, cost effective and does not involve any toxic reagents or end products. This method doesn’t warrant any special training and can be used by any unskilled person on site at the source of water.
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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".