Paper-Based DNA Reader for Visualized Quantification of Soil-Transmitted Helminth Infections
Bibliographic record
Abstract
Soil-transmitted helminth (STH) infections are a global health issue affecting nearly one-third of the world's population. As most endemic areas of STH are impoverished countries or regions with limited healthcare resources, the accurate diagnosis of STH requires analytical tools that are not only quantitative, but also portable, inexpensive, and with no or minimal demand for external instrument. Herein, we introduce a novel paper-based diagnostic device, termed quantitative paper-based DNA reader (qPDR), capable of quantifying STH at the molecular level by measuring distance as readout, thus eliminating the need for external readers. On the basis of the unique interfacial interaction of a DNA intercalating dye, SYBR Green I, with native cellulose on a chromatographic paper, qPDR allows the distance-based quantification of minute amounts of double-stranded DNA as short as 6 min. By integrating qPDR with polymerase chain reactions that were performed using a smartphone-controlled portable thermal cycler, we were able to quantify minute amount of genetic markers from adult worms of an STH (Trichuris trichiura) that were expelled post-treatment by infected children living in the rural areas of Honduras.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".