Central nervous system transcriptome of Biomphalaria alexandrina, an intermediate host for schistosomiasis
Bibliographic record
Abstract
OBJECTIVE: Globally, more than 200 million people live at risk of the neglected tropical disease schistosomiasis (or snail fever). Larval schistosomes require the presence of specific snail species that act as intermediate hosts, supporting their multiplication and transformation into forms that can infect humans. This project was designed to generate a transcriptome from the central nervous system (CNS) of Biomphalaria alexandrina, the major intermediate host for Schistosoma mansoni in Egypt. RESULTS: A transcriptome was generated from five pooled central nervous systems dissected from uninfected specimens of B. alexandrina. Raw Illumina RNA-seq data (~ 20.3 million paired end reads of 150 base pairs length each) generated a transcriptome consisting of 144,213 transcript elements with an N50 contig size of 716 base pairs. Orthologs of 15,246 transcripts and homologs for an additional 16,810 transcripts were identified in the UniProtKB/Swiss-Prot database. The B. alexandrina CNS transcriptome provides a resource for future research exploring parasite-host interactions in a simpler nervous system. Moreover, increased understanding of the neural signaling mechanisms involved in the response of B. alexandrina to infection by S. mansoni larvae could lead to novel and highly specific strategies for the control of snail populations.
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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 | 0.000 |
| 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".