A review of<i>Cryptosporidium</i>spp. and their detection in water
Bibliographic record
Abstract
Cryptosporidium spp. are one of the most important waterborne pathogens worldwide and a leading cause of mortality from waterborne gastrointestinal diseases. Detection of Cryptosporidium spp. in water can be very challenging due to their low numbers and the complexity of the water matrix. This review describes the biology of Cryptosporidium spp. and current methods used in their detection with a focus on C. parvum and C. hominis. Among the methods discussed and compared are microscopy, immunology-based methods using monoclonal antibodies, molecular methods including PCR (polymerase chain reaction)-based assays, and emerging aptamer-based methods. These methods have different capabilities and limitations, but one common challenge is the need for better sensitivity and specificity, particularly in the presence of contaminants. The application of DNA aptamers in the detection of Cryptosporidium spp. oocysts shows promise in overcoming these challenges, and there will likely be significant developments in aptamer-based sensors in the near future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".