A cloth-based hybridization array system for rapid detection of the food- and waterborne protozoan parasites Giardia duodenalis, Cryptosporidium spp. and Toxoplasma gondii
Bibliographic record
Abstract
Protozoan parasites in food or water samples are generally detected using microscopy or PCR followed by Sanger sequencing. However, microscopy is subjective, requires a high degree of expertise and has limited sensitivity, while DNA sequencing requires expensive and specialized equipment and facilities. This study describes a cloth-based hybridization array system (CHAS) that is an alternative to Sanger sequencing to confirm PCR-positive samples. CHAS is an inexpensive, rapid and reliable method for the simultaneous detection of multiple protozoan parasite species based on the colorimetric detection of PCR amplicons on a polyester cloth. PCR primers and CHAS hybridization probes were developed to detect the protozoan parasites Giardia duodenalis, Cryptosporidium spp. and Toxoplasma gondii. In addition, CHAS probes were designed for the differentiation of G. duodenalis Assemblages A and B. In artificially contaminated fresh produce (lettuce, parsley) and water samples (river water, wastewater), this CHAS assay allowed for the successful detection of G. duodenalis, Cryptosporidium spp., and T. gondii. The present study demonstrates that the CHAS detection method is a simple and inexpensive alternative to DNA sequencing for the confirmation of PCR-positive results in laboratories testing for parasites in food or water samples. This assay may also be beneficial in developing countries, where DNA sequencing facilities may not be readily available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".