Selective Uropathogenic E. coli Detection Using Crossed Surface-Relief Gratings
Bibliographic record
Abstract
Urinary tract infections (UTIs) are one of the major burdens on public healthcare worldwide. One of the primary causes of UTIs is the invasion of the urinary tract by uropathogenic Escherichia coli (UPEC). Improper treatment of bacterial infections like UTIs with broad-spectrum antibiotics has contributed to the rise of antimicrobial resistance, necessitating the development of an inexpensive, rapid and accurate detection of UPEC. Here, we present real-time, selective and label-free detection of UPEC using crossed surface-relief gratings (CSRGs) as nanometallic sensors incorporated into an optical sensing platform. CSRGs enable real-time sensing due to their unique surface plasmon resonance (SPR)-based light energy exchange, resulting in detection of a very-narrow-bandwidth SPR signal after the elimination of residual incident light. The platform’s sensing ability is experimentally demonstrated by the detection of bulk refractive index (RI) changes, with a bulk sensitivity of 382.2 nm/RIU and a resolution in the order of 10−6 RIU. We also demonstrate, for the first time, CSRG-based real-time selective capture and detection of UPEC in phosphate-buffered saline (PBS) solution, in clinically relevant concentrations, as opposed to other UTI-causing Gram-negative bacteria. The platform’s detection limit is calculated to be 105 CFU/mL (concentration on par with the clinical threshold for UTI diagnosis), with a dynamic range spanning four orders of magnitude. This work paves the way for the development of inexpensive point-of-care diagnosis devices focusing on effective treatment of UTIs, which are a burden on public healthcare due to the rise in the number of cases and their recurrences in the recent past.
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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) | 0.000 | 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 | 0.001 | 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".