Exploring molecular variation in Schistosoma japonicum in China
Bibliographic record
Abstract
Schistosomiasis is a neglected tropical disease that affects more than 200 million people worldwide. The main disease-causing agents, Schistosoma japonicum, S. mansoni and S. haematobium, are blood flukes that have complex life cycles involving a snail intermediate host. In Asia, S. japonicum causes hepatointestinal disease (schistosomiasis japonica) and is challenging to control due to a broad distribution of its snail hosts and range of animal reservoir hosts. In China, extensive efforts have been underway to control this parasite, but genetic variability in S. japonicum populations could represent an obstacle to eliminating schistosomiasis japonica. Although a draft genome sequence is available for S. japonicum, there has been no previous study of molecular variation in this parasite on a genome-wide scale. In this study, we conducted the first deep genomic exploration of seven S. japonicum populations from mainland China, constructed phylogenies using mitochondrial and nuclear genomic data sets, and established considerable variation between some of the populations in genes inferred to be linked to key cellular processes and/or pathogen-host interactions. Based on the findings from this study, we propose that verifying intraspecific conservation in vaccine or drug target candidates is an important first step toward developing effective vaccines and chemotherapies against schistosomiasis.
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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 | 0.002 |
| 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) | 0.001 | 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".