Decoding the RNA Viromes in Rodent Lungs Provides New Insight Into the Origin and Evolutionary Patterns of Rodent-borne Pathogens in Mainland Southeast Asia
Bibliographic record
Abstract
Abstract BACKGROUND: As the largest group of mammalian species, which are also widely distributed all over the world, rodents are the natural reservoirs for many diverse zoonotic viruses. A comprehensive understanding of the core virome of diverse rodents should therefore assist in efforts to reduce the risk of future emergence or re-emergence of rodent-borne zoonotic pathogens. RESULTS: This study aimed to describe the viral range that could be detected in the lungs of rodents from Mainland Southeast Asia. Lung samples were collected from 3,284 rodents and insectivores of the orders Rodentia, Scandentia, and Eulipotyphla in eighteen provinces of Thailand, Lao PDR, and Cambodia throughout 2006-2018. Meta-transcriptomic analysis was used to outline the unique spectral characteristics of the mammalian viruses within these lungs and the ecological and genetic imprints of the novel viruses. Many mammalian- or arthropod-related viruses from distinct evolutionary lineages were reported for the first time in these species, and viruses related to known pathogens were characterized for their genomic and evolutionary characteristics, host species, and locations. CONCLUSIONS: These results expand our understanding of the core viromes of rodents and insectivores from Mainland Southeast Asia and suggest that a high diversity of viruses remains to be found in rodent species of this area. These findings, combined with our previous virome data from China, increase our knowledge of the viral community in wildlife and arthropod vectors in emerging disease hotspots of East and Southeast Asia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".