Drosophila melanogaster as a High-Throughput Model for Host–Microbiota Interactions
Bibliographic record
Abstract
We agree no model is perfect and that D. melanogaster has its limitations like any other model. We would like to stress that we are not discrediting germ-free mouse models, but that researchers could benefit from supportive in vivo evidence (rather than in vitro) of their hypotheses before embarking down this expensive avenue. The cost and time-consuming nature of germ-free mouse studies requires strong conviction of hypotheses to warrant logical further investigation. The purpose of this perspective article is to bring attention to the under-considered areas of research that D. melanogaster may be a useful model for preliminary investigations. We highlight that the inexpensive and high-throughput D. melanogaster microbiota model can enable investigators to experiment with exploratory research questions such as probiotics, prebiotics, xenobiotics, and diet-genetic interactions before verification in costlier models. D. melanogaster microbiota simplicity also enables researchers to develop predictive models of how polymicrobial interactions affect host physiology before testing in more complex hosts (a common theme in biological animal models which you have alluded to previously). Altogether, we agree that there are limitations to the D. melanogaster model. We have now explicitly pointed out the major shortcomings of this model in the Future Directions and Conclusions section and made reference to review articles addressing these limitations in more detail. We hope this will better inform readers while also abiding to the strict space limitations inherent of perspective articles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".