Autophagy and Respiratory Viruses: Mechanisms, Viral Exploitation, and Therapeutic Insights
Bibliographic record
Abstract
Respiratory viruses, such as influenza virus, rhinovirus, coronavirus, and respiratory syncytial virus (RSV), continue to impose a heavy global health burden. Despite existing vaccination programs, these infections remain leading causes of morbidity and mortality, especially among vulnerable populations like children, older adults, and immunocompromised individuals. However, the current therapeutic options for respiratory viral infections are often limited to supportive care, underscoring the need for novel treatment strategies. Autophagy, particularly macroautophagy, has emerged as a fundamental cellular process in the host response to respiratory viral infections. This process not only supports cellular homeostasis by degrading damaged organelles and pathogens but also enables xenophagy, which selectively targets viral particles for degradation and enhances cellular defense. However, viruses have evolved mechanisms to manipulate the autophagy pathways, using them to evade immune detection and promote viral replication. This review examines the dual role of autophagy in viral manipulation and host defense, focusing on the complex interplay between respiratory viruses and autophagy-related pathways. By elucidating these mechanisms, we aim to highlight the therapeutic potential of targeting autophagy to enhance antiviral responses, offering promising directions for the development of effective treatments against respiratory viral infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".