Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 × 10<sup>2</sup> colony-forming-units per mL (CFU/mL), which is well below the clinical diagnostic criterion (>10<sup>5</sup> CFU/mL) for UTI. The self-powered, peripheral-free CC presented here has potential for use in rapid point-of-care UTI screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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 it