Knitting Thread Devices: Detecting <i>Candida albicans</i> Using Napkins and Tampons
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reproducible and in situ microbial detection, particularly of microbes significant in urinary tract infections (UTIs) such as Candida albicans, provides a unique opportunity to bring equity in the healthcare outcomes of disenfranchised groups like women in low-resource settings. Here, we demonstrate a system to potentially detect vulvovaginal candidiasis by leveraging the properties of multifilament cotton threads in the form of microfluidic-thread-based analytical devices (μTADs) to develop a frugal microbial identification assay. A facile mercerization method using heptane wash to boost reagent absorption and penetration is also performed and is shown to be robust compared to other existing conventional mercerization methods. Furthermore, the twisted mercerized fibers are drop-cast with media consisting of l -proline β-naphthylamide, which undergoes hydrolysis by the enzyme l -proline aminopeptidase secreted by C. albicans, hence signaling the presence of the pathogen via simple color change with a limit of detection of 0.58 × 10 6 cfu/mL. The flexible and easily disposable thread-based detection device when integrated with menstrual hygiene products showed a detection time of 10 min using spiked vaginal discharge. The developed method boasts a long shelf life and high stability, making it a discreet detection device for testing, which provides new vistas for self-testing multiple diseases that are considered taboo in certain societies.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".