TRPtracker: a community database for monitoring praziquantel sensitivity at TRPM <sub>PZQ</sub> variants
Bibliographic record
Abstract
Abstract The anthelmintic praziquantel (PZQ) has been used for decades as the clinical therapy for schistosomiasis, and remains the only available drug. As a cheap and effective drug therapy for all human disease-causing Schistosoma species, usage of PZQ underpins mass drug administration strategies aimed at eliminating schistosomiasis as a public health problem by 2030. Concern over the potential emergence of resistance to PZQ is therefore warranted, as it would constitute a major threat to this approach. In terms of molecular adaptations conferring PZQ resistance, variation in the sequence and/or expression of the drug target is an obvious mechanism and should be a priority for surveillance efforts. The target of PZQ is a transient receptor potential ion channel, TRPM PZQ , which is established as a locus that regulates schistosome sensitivity to PZQ. Here, we describe the establishment of a community resource, ‘TRPtracker’, which coalesces data on TRPM PZQ natural variants together with measurements of individual variant sensitivity to PZQ. A compendium of laboratory-generated mutants in TRPM PZQ is also compiled in TRPtracker to map regions within TRPM PZQ critical for PZQ sensitivity. Aggregation of data from multiple research groups into TRPtracker permits rapid community-wide exchange of data, cataloguing which TRPM PZQ variants have been functionally profiled, where geographically these variants have been found, their frequency within populations and their potential impact on PZQ sensitivity.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".