Investigating «Cryptosporidium parvum» surface proteins as candidates for a vaccine against bovine cryptosporidiosis
Bibliographic record
Abstract
Bovine cryptosporidiosis caused by Cryptosporidium parvum is frequent in dairy farms worldwide.C. parvum-related severe diarrhea and mortality in newborn dairy calves account for serious impairment of health and important economic losses.No vaccine is available to prevent oocyst shedding in field conditions and no treatment is available to cure the infection in calves.Calves can shed enormous quantities of infectious oocysts, leading to significant contamination of their environment.Catchment contamination is a concern because C. parvum is a zoonotic pathogen.Large waterborne outbreaks of cryptosporidiosis have been reported worldwide due to contamination of drinking and recreational water with oocysts.Also, people in contact with infected calves such as veterinary students are particularly at risk of infection.Human cryptosporidiosis caused by C. hominis or C. parvum is life-threatening in young children in sub-Saharan Africa and south Asia where cryptosporidiosis is the second most frequent cause of moderate-to-severe diarrhea in infants and the third most frequent cause in toddlers.Again, no vaccine is available to prevent human infection and therapeutic drugs are of limited value for immunocompromised individuals.There is an urgent need to develop a vaccine to protect against C. parvum infection.The ultimate goal of this project was to develop a vaccine that could be used to immunize pregnant cows to passively transfer immunity to newborn dairy calves in colostrum.First, I cloned 4 cDNA encoding portions of 4 C. parvum surface proteins (CP2, p23, gp45 and gp900) and expressed the proteins in E. coli.Second, mother interferon gamma receptor knock-out (IFNR-KO) mice immunized with these 4 proteins transferred partial protection to their progeny.Moreover, adoptive transfer of splenocytes from wild-type
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".