Real-Time PCR for Quantification of <i>Giardia</i> and <i>Cryptosporidium</i> in Environmental Water Samples and Sewage
Bibliographic record
Abstract
The protozoan pathogens Giardia lamblia and Cryptosporidium parvum are major causes of waterborne enteric disease throughout the world. Improved detection methods that are very sensitive and rapid are urgently needed. This is especially the case for analysis of environmental water samples in which the densities of Giardia and Cryptosporidium are very low. Primers and TaqMan probes based on the beta-giardin gene of G. lamblia and the COWP gene of C. parvum were developed and used to detect DNA concentrations over a range of 7 orders of magnitude. It was possible to detect DNA to the equivalent of a single cyst of G. lamblia and one oocyst of C. parvum. A multiplex real-time PCR (qPCR) assay for simultaneous detection of G. lamblia and C. parvum resulted in comparable levels of detection. Comparison of DNA extraction methodologies to maximize DNA yield from cysts and oocysts determined that a combination of freeze-thaw, sonication, and purification using the DNeasy kit (Qiagen) provided a highly efficient method. Sampling of four environmental water bodies revealed variation in qPCR inhibitors in 2-liter concentrates. A methodology for dealing with qPCR inhibitors that involved the use of Chelex 100 and PVP 360 was developed. It was possible to detect and quantify G. lamblia in sewage using qPCR when applying the procedure for extraction of DNA from 1-liter sewage samples. Numbers obtained from the qPCR assay were comparable to those obtained with immunofluorescence microscopy. The qPCR analysis revealed both assemblage A and assemblage B genotypes of G. lamblia in the sewage. No Cryptosporidium was detected in these samples by either method.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".