Detection of Cryptosporidium parvum Oocysts on Fresh Produce Using DNA Aptamers
Bibliographic record
Abstract
There are currently no standard methods for the detection of Cryptosporidium spp., or other protozoan parasites, in foods, and existing methods are often inadequate, with low and variable recovery efficiencies. Food testing is difficult due to the low concentrations of parasites, the difficulty in eluting parasites from some foods, the lack of enrichment methods, and the presence of PCR inhibitors. The main objectives of the present study were to obtain DNA aptamers binding to the oocyst wall of C. parvum, and to use the aptamers to detect the presence of this parasite in foods. DNA aptamers were selected against C. parvum oocysts using SELEX (Systematic Evolution of Ligands by EXponential enrichment). Ten rounds of selection led to the discovery of 14 aptamer clones with high affinities for C. parvum oocysts. For detecting parasite-bound aptamers, a simple electrochemical sensor was employed, which used a gold nanoparticle-modified screen-printed carbon electrode. This aptasensor was fabricated by self-assembling a hybrid of a thiolated ssDNA primer and the anti- C. parvum aptamer. Square wave voltammetry was employed to quantitate C. parvum in the range of 150 to 800 oocysts, with a detection limit of approximately 100 oocysts. The high sensitivity and specificity of the developed aptasensor suggests that this novel method is very promising for the detection and identification of C. parvum oocysts on spiked fresh fruits, as compared to conventional methods such as microscopy and PCR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".