Increased lethality in Influenza and SARS-CoV-2 co-infection is prevented by influenza immunity but not SARS-CoV-2 immunity
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause for the ongoing COVID-19 pandemic1. The continued spread of SARS-CoV-2 along with the imminent flu season increase the probability of influenza-SARS-CoV-2 dual infection which might result in a severe disease. In this study, we examined the disease outcome of influenza A virus (IAV) and SARS-CoV-2 co-infection in K18-hACE2 mice. Our data indicates that IAV-infected mice are more susceptible to develop severe disease upon co-infection with SARS-CoV-2 two days post influenza infection. This co-infection results in severe morbidity and nearly uniform fatality as compared to the non-fatal influenza disease, or the partial fatality of SARS-CoV-2 alone. Co-infection was associated with elevated influenza viral load in respiratory organs. Remarkably, prior immunity to influenza, but not to SARS-CoV-2, prevented the severe disease and mortality. These data provide an experimental support that flu intervention by prior vaccination may be valuable in reducing the risk of sever Flu - SARS-CoV-2 comorbidity, and highlight the importance of vaccination.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".