Drosophila as a Model for Human Viral Neuroinfections
Bibliographic record
Abstract
The study of human neurological infection faces many technical and ethical challenges. While not as common as mammalian models, the use of Drosophila (fruit fly) in the investigation of virus–host dynamics is a powerful research tool. In this review, we focus on the benefits and caveats of using Drosophila as a model for neurological infections and neuroimmunity. Through the examination of in vitro, in vivo and transgenic systems, we highlight select examples to illustrate the use of flies for the study of exogenous and endogenous viruses associated with neurological disease. In each case, phenotypes in Drosophila are compared to those in human conditions. In addition, we discuss antiviral drug screening in flies and how investigating virus–host interactions may lead to novel antiviral drug targets. Together, we highlight standardized and reproducible readouts of fly behaviour, motor function and neurodegeneration that permit an accurate assessment of neurological outcomes for the study of viral infection in fly models. Adoption of Drosophila as a valuable model system for neurological infections has and will continue to guide the discovery of many novel virus–host interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".