Detection and Resolution of Cryptosporidium Species and Species Mixtures by Genus-Specific Nested PCR-Restriction Fragment Length Polymorphism Analysis, Direct Sequencing, and Cloning
Bibliographic record
Abstract
Molecular methods incorporating nested PCR-restriction fragment length polymorphism (RFLP) analysis of the 18S rRNA gene of Cryptosporidium species were validated to assess performance based on limit of detection (LoD) and for detecting and resolving mixtures of species and genotypes within a single sample. The 95% LoD was determined for seven species (Cryptosporidium hominis, C. parvum, C. felis, C. meleagridis, C. ubiquitum, C. muris, and C. andersoni) and ranged from 7 to 11 plasmid template copies with overlapping 95% confidence limits. The LoD values for genomic DNA from oocysts on microscope slides were 7 and 10 template copies for C. andersoni and C. parvum, respectively. The repetitive nested PCR-RFLP slide protocol had an LoD of 4 oocysts per slide. When templates of two species were mixed in equal ratios in the nested PCR-RFLP reaction mixture, there was no amplification bias toward one species over another. At high ratios of template mixtures (>1:10), there was a reduction or loss of detection of the less abundant species by RFLP analysis, most likely due to heteroduplex formation in the later cycles of the PCR. Replicate nested PCR was successful at resolving many mixtures of Cryptosporidium at template concentrations near or below the LoD. The cloning of nested PCR products resulted in 17% of the cloned sequences being recombinants of the two original templates. Limiting-dilution nested PCR followed by the sequencing of PCR products resulted in no sequence anomalies, suggesting that this method is an effective and accurate way to study the species diversity of Cryptosporidium, particularly for environmental water samples, in which mixtures of parasites are common.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".