Developing insect models for the study of current and emerging human pathogens
Bibliographic record
Abstract
The study of human diseases requires the testing of microorganisms in model systems. Although mammals are typically used, we argue the validity of using insects as models in order to examine human diseases, particularly the growing number of opportunistic microorganisms. Insects can be used in large numbers, are easily manipulated, and are not subject to the same ethical concerns as mammalian systems. Insects and mammals have many parallels with respect to microbial pathogenesis, from proteinaceous integuments that require breaching before infection to similarities in their innate immune responses. Reactions of insects to Candida and Pseudomonas spp. infections show good correlation with mouse models, providing precedent-setting examples of the study of human pathogens using insects. Insects as pathogen hosts also warrant study because they may act as reservoirs for emerging human pathogens. Finally, insect models may be used to examine the evolutionary processes involved in the acquisition of virulence factors and host-jumping mechanisms indispensable to emerging pathogens. Insect models may be used in 'niche' investigations where large sample sizes can facilitate rapid, informative screening of opportunistic diseases and provide insights into pathogen evolution, while reducing the cost and ethical concerns associated with mammalian models.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".