Exploring viral respiratory coinfections: Shedding light on pathogen interactions
Bibliographic record
Abstract
Respiratory infections pose a significant global health burden, being one of the leading causes of illness and death worldwide [1,2].In 2019, there were an estimated 17.2 billion cases of upper respiratory infections, accounting for over 40% of all illnesses globally [3].The primary viruses responsible for these infections include respiratory syncytial viruses (RSVs), influenza viruses, and human rhinoviruses (hRVs), all of which can cause a range of symptoms that themselves vary from mild to severe [4].Additionally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2020, has significantly impacted public health and now cocirculates with other respiratory viruses.Several respiratory viruses can infect the same individual during a respiratory infection, as evidenced by an increased documentation of viral codetection.Multiplex panels, which detect multiple viral pathogens at once, have revealed that respiratory coinfections are more common than previously thought; these are found in 3% to 26% of patients hospitalised for respiratory infection [5,6].Coinfections are particularly prevalent in children, the elderly, and immunocompromised individuals, but the impact on the severity of infection or hospitalisation risk is still debated [7,8].Consequently, comprehensive research into the interactions among various respiratory viruses is crucial for advancing our knowledge and management of these infections.This Pearl will cover our current understanding of the interactions between respiratory viruses at the host cell level.It will also discuss the challenges in this field, such as the limitations of experimental models, the complexities of analysing epidemiological data, and the need for new approaches to understand deeply these complex coinfections.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".