Mechanisms for Physicochemical Interaction between Mucus and Respiratory Viruses
Bibliographic record
Abstract
Viral infections of the respiratory tract, including those caused by respiratory syncytial virus, influenza, and coronaviruses, constitute a significant global public health burden. Central to the pathogenesis of these infections are the interactions between viruses and host mucosal barriers, particularly the complex glycoproteins known as mucins that are the primary constituents of mucus. Mucins function not only as physical barriers but also as immune modulators, with their glycan chains playing critical roles in viral recognition and binding processes. These viral-mucin interactions determine host specificity, influence transmission dynamics, and regulate immune responses. Conversely, viruses can alter mucus composition and compromise mucociliary clearance mechanisms. This review first examines the structural and functional properties of mucins, followed by a comprehensive analysis of the complex interactions between respiratory viral surface proteins and mucins, including virus-induced perturbations to airway mucus secretion. We summarize current knowledge of viral-mucin interactions to provide insights into the potential development of mucin-mimetic polymers for targeted viral engagement, with applications ranging from viral detection to infection inhibition. Additionally, we discuss biophysical methodologies for investigating interactions between viruses and glycans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".