Microfluidic Capillaric Circuit for Rapid and Facile Bacteria Detection
Bibliographic record
Abstract
Abstract Urinary tract infections (UTI) are one of the most common bacterial infections and would greatly benefit from a rapid point-of-care diagnostic test. Although significant progress has been made in developing microfluidic systems for nucleic acid and whole bacteria immunoassay tests, their practical application is limited by complex protocols, bulky peripherals, and slow operation. Here we present a microfluidic capillaric circuit (CC) optimized for rapid and automated detection of bacteria in urine. Molds for CCs were constructed using previously established design rules, then 3D-printed and replicated into poly(dimethylsiloxane). CCs autonomously and sequentially performed all liquid delivery steps required for the assay. For efficient bacteria capture, on-the-spot packing of antibody-functionalized microbeads was completed in <20 s followed by autonomous sequential delivery of 100 μL of bacteria sample, biotinylated detection antibodies, fluorescent streptavidin conjugate, and wash buffer for a total volume ≈115 μL. The assay was completed in <7 min. Fluorescence images of the microbead column revealed captured bacteria as bright spots that were easily counted manually or using an automated script for user-independent assay readout. The limit of detection of E. coli in synthetic urine was 1.2 × 102 colony-forming-units per mL (CFU/mL), which is well below the clinical diagnostic criterion (>105 CFU/mL) for UTI. The self-powered, peripheral-free CC presented here has potential for use in rapid point-of-care UTI screening.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".