Neuronal signaling in schistosomes: current status and prospects for postgenomicsThe present review is one of a series of occasional review articles that have been invited by the Editors and will feature the broad range of disciplines and expertise represented in our Editorial Advisory Board.
Bibliographic record
Abstract
Parasitic platyhelminths of the genus Schistosoma Weinland, 1858 (Trematoda, Digenea) are the etiological agents of human schistosomiasis, one of the most prevalent and debilitating parasitic diseases worldwide. Praziquantel is the only drug treatment available in most parts of the world and the effectiveness of the drug is threatened by the prospect of drug resistance. There is a pressing need to learn more about the basic biology of this organism and to identify molecular targets for new therapeutic drugs. The nervous system of schistosomes coordinates many activities that are essential for parasite survival, and as such is an attractive target for chemotherapeutic intervention. Until recently, very little was known about the molecular mechanisms of neuronal signaling in these organisms, but this is rapidly changing following the completion of the genome sequence and several recent developments in schistosome transgenesis and gene silencing. Here we review the current status of schistosome neurobiology and discuss prospects for future research as the field moves into a postgenomics era. One of the themes that will emerge from this discussion is that schistosomes have a rich diversity of neurotransmitters and receptors, indicating a more sophisticated system of neuronal communication than might be expected of a parasitic flatworm. Moreover, many of these transmitter receptors share little sequence homology with those of the human host, making them ideally suited for selective drug targeting. Strategies for characterization of these important parasite proteins will be discussed.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".