Validation of an Automated High-Throughput Multiplex Real-Time PCR Assay for Detection of Enteric Protozoa
Bibliographic record
Abstract
Background: Microscopy is the conventional method for the identification of gastrointestinal parasitic pathogens in fecal specimens; however, it presents numerous challenges, including high technical expertise burden, multiple staining procedures, and prolonged turnaround time. Molecular methods provide higher throughput and potentially higher sensitivity and specificity. Methods: We validated a commercial, automated DNA extraction platform and multiplex parasitic real-time PCR panel (Seegene AllplexTM GI-Parasite Assay) detecting six protozoal pathogens: Blastocystis hominis (Bh), Cryptosporidium spp., Cyclospora cayetanensis (Cc), Dientamoeba fragilis (Df), Entamoeba histolytica (Eh), and Giardia lamblia (Gl) in unpreserved fecal specimens submitted for diagnostic parasitology. Microscopy was the reference standard for all organisms, with stool ELISA as an additional reference assay for Eh. Results: Among 461 unpreserved fecal specimens, sensitivity, specificity, positive predictive and negative predictive values of the enteric multiplex for fresh specimens were as follows: 93%, 98.3%, 85.1%, 99.3% for Bh; 100% for all measures in Cryptosporidium and Cc; 100%, 99.3%, 88.5%, 100% for Df; 33.3%, 100%, 100%, 99.6% for Eh; and 100%, 98.9%, 68.8%, 100% for Gl, respectively. With the addition of 17 frozen specimens, the sensitivity for Eh increased to 75%. On a per-batch basis, the molecular platform reduced pre-analytical and analytical testing turnaround time by 7 h. Conclusions: The enteric multiplex platform provides a useful diagnostic tool for clinically relevant enteric protozoa, including Cryptosporidium spp., Cyclospora cayetanensis, Dientamoeba fragilis, and Giardia lamblia. Further evaluation of the assay is required for Entamoeba histolytica prior to clinical use; however, given the widespread availability of confirmatory serology and stool antigen testing for E. histolytica, such performance limitations are of lesser concern.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".