Development and Application of a Novel Conserved Signature Protein/Gene-Based qPCR Strategy for Improved Cryptosporidium Surveillance in Recreational Waters
Bibliographic record
Abstract
Cryptosporidium is a major waterborne parasite that causes gastrointestinal illness. Conventional assays, including microscopy and immunological identification, often suffer from false positives or negatives due to non-specific binding or morphological differences between Cryptosporidium species. We developed a novel qPCR assay based on a Cryptosporidium-specific Conserved Signature Protein (CSP) to address the limitations of testing complex samples, including those from recreational waters. The CSP (hypothetical protein (cgd2_3830)) was identified as taxonomically unique to Cryptosporidium species. The CSP sequence and designed qPCR assay primers/probe demonstrated high specificity for the targeted Cryptosporidium species when tested against NCBI RefSeq databases. qPCR assay efficiency was determined as 95% and an R2 value of 0.99, with a slope and intercept of −3.4 and 40.1, respectively. Additionally, the Lower Limit of Detection (ALLOD) was determined as three gene copies, suggesting the potential to detect even a single oocyst. No non-specific amplification products or primer dimers were observed when the qPCR assay was evaluated using recreational water, fecal solution, and wastewater, while spike-in-control tests indicated minimal interference with the sensitivity of the assay, highlighting application for testing complex environmental DNA extracts. These findings highlight the application of the novel CSP-based qPCR assay for the rapid and sensitive detection of Cryptosporidium sp., thereby circumventing the sequence variability and multi-copy limitations associated with existing molecular markers. This proof-of-concept study presents a diagnostic framework utilizing CSP-based markers for developing water quality monitoring strategies, with scope for expansion to other microbial pathogens and potential applications in clinical and food safety settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".