Multiplexed detection and differentiation of bacterial enzymes and bacteria by color-encoded sensor hydrogels
Bibliographic record
Abstract
We report on the fabrication and characterization of color-encoded chitosan hydrogels for the rapid, sensitive and specific detection of bacterial enzymes as well as the selective detection of a set of tested bacteria through characteristic enzyme reactions. These patterned sensor hydrogels are functionalized with three different colorimetric enzyme substrates affording the multiplexed detection and differentiation of α-glucosidase, β-galactosidase and β-glucuronidase. The limits of detection of the hydrogels for an observation time of 60 min using a conventional microplate reader correspond to concentrations of 0.2, 3.4 and 4.5 nM of these enzymes, respectively. Based on their different enzyme expression patterns, Staphylococcus aureus strain RN4220, methicillin-resistant S. aureus (MRSA) strain N315, both producing α-glucosidase, but not β-glucuronidase and β-galactosidase, Escherichia coli strain DH5α, producing β-glucuronidase and α-glucosidase, but not β-galactosidase, and the enterohemorrhagic E. coli (EHEC) strain E32511, producing β-galactosidase, but none of the other two enzymes, can be reliably and rapidly distinguished from each other. These results confirm the applicability of enzyme sensing hydrogels for the detection and discrimination of specific enzymes to facilitate differentiation of bacterial strains. Patterned hydrogels thus possess the potential to be further refined as detection units of a multiplexed format to identify certain bacteria for future application in point-of-care microbiological diagnostics in food safety and medical settings.
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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 | 0.000 |
| 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 | 0.000 | 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 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".