Analysis of Schistosoma mansoni Population Structure Using Total Fecal Egg Sampling
Bibliographic record
Abstract
Many parasite populations are difficult to sample because they are not uniformly distributed between several host species and are often not easily collected from the living host, thereby limiting sample size and possibly distorting the representation of the population. For the parasite Schistosoma mansoni, we investigated the use of eggs, in aggregate, from the stools of infected individuals as a simple and representative sample. Previously, we demonstrated that microsatellite allele frequencies can be accurately estimated from pooled DNA of cloned S. mansoni adults. Here, we show that genotyping of parasite populations from reproductively isolated laboratory strains can be used to identify these specific populations based on characteristic patterns of allele frequencies, as observed by polyacrylamide gel electrophoresis and automated sequencer analysis of fluorescently labeled PCR products. Microsatellites used to genotype aggregates of eggs collected from stools of infected individuals produced results consistent with the geographic distribution of the samples. Preferential amplification of smaller alleles, and stutter PCR products, had negligible effect on measurement of genetic differentiation. Direct analysis of total stool eggs can be an important approach to questions of population genetics for this parasite by increasing the sample size to thousands per infected individual and by reducing bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".