Detection and Genotyping of Cryptosporidium Spp. in Biofilms and Cattle Feces From the Black River Watershed, Jamaica
Bibliographic record
Abstract
Cryptosporidium is a protozoan parasite that causes the gastrointestinal disease cryptosporidiosis. The disease is endemic in most tropical countries including Jamaica; yet underreported from environmental sources. This is of concern because the primary source of anthropocentric cryptosporidiosis is surface water contaminated by human effluent and animal waste in runoff. This dissertation therefore, focuses on three main areas of research: 1) optimization and assessment of effective methods for detecting Cryptosporidium from environmental samples; 2) application of methods to analyze biofilm and cattle feces collected from the Black River watershed, located in the rural parish of St. Elizabeth, Jamaica; 3) molecular characterization of PCR-positive detections to identify Cryptosporidium species and genotypes, thereby provide inference to waterborne transmission, mitigation, and zoonotic potential within the region. First, foundational work of this dissertation focused on the development of in situ biofilm sampling for Cryptosporidium detection. Application was then performed in the Black River network for initial screening of oocysts from biofilms collected from 5 sites, and 119 cattle fecal specimens collected from 10 farms. Multiple techniques were employed to confirm the absence or presence of Cryptosporidium, including Immunofluorescence Assay (IFA) and Modified Acid-Fast (MAF) microscopy, Enzyme-Linked Immunosorbent Assay (ELISA), and Polymerase Chain Reaction (PCR). Results show that oocysts were widely dispersed in biofilms and cattle specimens. Highest prevalence was observed amoung dairy cattle compared to beef cattle, presumably due to confined space. Results also highlight inconsistencies between detection methods, confirming that from environmental sources—where inhibitors are abundant and oocysts concentrations are naturally low—a single technique may be ineffective for understanding transmission dynamics. The second part of this dissertation focused on molecular characterization. Fourteen PCR-positive biofilm and cattle samples were subjected to gene sequencing and phylogeny. A low species diversity consisting of C. parvum and C. hominis were identified; the two most common species involved in anthropocentric infections. A specific 18S rRNA isolate of C. parvum was found in both biofilm and cattle samples (with 99% identity), indicating that a geographically distinct, clonal genotype of C. parvum potentially exists within the region. Further subtyping analysis of the gp60 locus identified one C. hominis subtype (IbA9G2), formally identified in human populations worldwide. Moreover, the IbA9G2 subtype was recently linked to calf infections in France, as well as a waterborne outbreak in Germany. In conclusion, this dissertation is the first to detect and characterize Cryptosporidium species and genotypes from surface water biofilms and cattle feces in Jamaica, providing informative data pertaining to public health and animal agriculture. Moreover, this research advocates the importance of utilizing multiple detection methods and sources for effective screening of Cryptosporidium throughout the environment. Whilst meaningful interpretations of Cryptosporidium population structures are developed, useful databases can form through analyzing a well-planned set of environmental samples.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".