Improved Risk Analysis by Dual Direct Detection of Total and Infectious <i>Cryptosporidium</i> Oocysts on Cell Culture in Combination with Immunofluorescence Assay
Bibliographic record
Abstract
The inactivation of Cryptosporidium oocysts is a main driver in the selection of water treatment disinfection strategies, and microbial risk analysis provides a sound basis for optimizing water treatment processes. U.S. Environmental Protection Agency method 1622/23 provides an estimate of the total oocyst count; however, it cannot be used directly for risk assessment, as it does not determine the fraction of infectious oocysts. Improved assessment of the risk for designated sources or in treated water requires evaluation of the total number of oocysts and an estimate of their infectivity. We developed a dual direct detection method using differential immunofluorescent staining that allows detection of both oocysts and cell culture infection foci for each sample. Using Cryptosporidium parvum oocysts, various pH levels, proteases, and gastroenteric compounds and substrates were assessed to determine their abilities to enhance the number of infection foci. The results showed that the key trigger for oocyst stimulation was acidification. Addition of a low concentration of D-glucose (50 mM) to the infection media increased rates of infectivity, while a higher dose (300 mM) was inhibitory. The total number of oocysts in each sample was determined by counting the oocysts remaining on a cell monolayer and the oocysts recovered from cell monolayer washes during processing using a simple filtration technique. With the dual direct detection on cell culture with immunofluorescence assay method, it is now possible to determine the numbers of total and infectious oocysts for a given sample in a single analysis. Direct percentages of infectivity are then calculated, which allows more accurate assessments of risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".