Development and Evaluation of Three Real-Time PCR Assays for Genotyping and Source Tracking Cryptosporidium spp. in Water
Bibliographic record
Abstract
The occurrence of Cryptosporidium oocysts in drinking source water can present a serious public health risk. To rapidly and effectively assess the source and human-infective potential of Cryptosporidium oocysts in water, sensitive detection and correct identification of oocysts to the species level (genotyping) are essential. In this study, we developed three real-time PCR genotyping assays, two targeting the small-subunit (SSU) rRNA gene (18S-LC1 and 18S-LC2 assays) and one targeting the 90-kDa heat shock protein (hsp90) gene (hsp90 assay), and evaluated the sensitivity and Cryptosporidium species detection range of these assays. Using fluorescence resonance energy transfer probes and melt curve analysis, the 18S-LC1 and hsp90 assays could differentiate common human-pathogenic species (C. parvum, C. hominis, and C. meleagridis), while the 18S-LC2 assay was able to differentiate nonpathogenic species (such as C. andersoni) from human-pathogenic ones commonly found in source water. In sensitivity evaluations, the 18S-LC2 and hsp90 genotyping assays could detect as few as 1 Cryptosporidium oocyst per sample. Thus, the 18S-LC2 and hsp90 genotyping assays might be used in environmental monitoring, whereas the 18S-LC1 genotyping assay could be useful for genotyping Cryptosporidium spp. in clinical specimens or wastewater samples.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".