A protocol to count Cryptosporidium oocysts by flow cytometry without antibody staining
Bibliographic record
Abstract
Cryptosporidiosis caused by the protozoan parasites Cryptosporidium hominis and C. parvum, threatens the lives of young children in developing countries.In veterinary medicine, C. parvum causes life-threatening diarrhea and dehydration in newborn dairy calves.Protocols to detect Cryptosporidium spp.oocysts using flow cytometry have been reported; however, these protocols use antibodies against the parasite and typically focus on detection of oocysts, not quantification.These techniques are not well-suited for studies that generate large variations in oocyst burdens because the amount of antibody required is proportional to the number of oocysts expected in samples.Also, oocysts are lost in washes in the staining protocol, reducing accuracy of oocyst counts.Moreover, these protocols require costly fluorochrome-conjugated monoclonal antibodies and are not optimal for studies involving large numbers of samples.Here we present an optimized protocol for purifying oocysts from mouse stool and intestine samples combined with a reliable method to quantify oocysts in a relatively pure population without the need for antibody staining.We used morphology (SSC-A vs FSC-A) and the innate characteristics of C. parvum oocysts compared to fecal and intestinal contaminants to develop a two-step gating strategy that can differentiate oocysts from debris.This method is a fast, reliable, and high-throughput technique to promote research projects on C. parvum infections in mice and potentially other animal hosts. Author summaryDiarrheal diseases are the second leading cause of death in children < 5 years old.Cryptosporidiosis caused by the unicellular parasite Cryptosporidium spp. is one of these diarrheal diseases.C. hominis and C. parvum cause moderate-to-severe diarrhea and dehydration that threaten the lives of young children in developing countries.Flow cytometry is a state-of-the-art technique to detect Cryptosporidium spp.oocysts, the infectious form of the parasite.Reported protocols typically focus on detection of oocysts using
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".