Detection and Differentiation of Coccidian Oocysts by Real-Time PCR and Melting Curve Analysis
Bibliographic record
Abstract
Rapid and reliable detection and identification of coccidian oocysts are essential for animal health and foodborne disease outbreak investigations. Traditional microscopy and morphological techniques can identify large and unique oocysts, but they are often subjective and require parasitological expertise. The objective of this study was to develop a real-time quantitative PCR (qPCR) assay using melting curve analysis (MCA) to detect, differentiate, and identify DNA from coccidian species of animal health, zoonotic, and food safety concern. A universal coccidia primer cocktail was designed and employed to amplify DNA from Cryptosporidium parvum, Toxoplasma gondii, Cyclospora cayetanensis, and several species of Eimeria, Sarcocystis, and Isospora using qPCR with SYBR Green detection. MCA was performed following amplification, and melting temperatures (T(m)) were determined for each species based on multiple replicates. A standard curve was constructed from DNA of serial dilutions of T. gondii oocysts to estimate assay sensitivity. The qPCR assay consistently detected DNA from as few as 10 T. gondii oocysts. T(m) data analysis showed that C. cayetanensis, C. parvum, Cryptosporidium muris, T. gondii, Eimeria bovis, Eimeria acervulina, Isospora suis, and Sarcocystis cruzi could each be identified by unique melting curves and could be differentiated based on T(m). DNA of coccidian oocysts in fecal, food, or clinical diagnostic samples could be sensitively detected, reliably differentiated, and identified using qPCR with MCA. This assay may also be used to detect other life-cycle stages of coccidia in tissues, fluids, and other matrices. MCA studies on multiple isolates of each species will further validate the assay and support its application as a routine parasitology screening tool.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".