Mucus‐Inspired Self‐Healing Hydrogels: A Protective Barrier for Cells against Viral Infection
Bibliographic record
Abstract
Mucus is a dynamic biological hydrogel, composed primarily of the glycoprotein mucin, exhibits unique biophysical properties and forms a barrier protecting cells against a broad-spectrum of viruses. Here, this work develops a polyglycerol sulfate-based dendronized mucin-inspired copolymer (MICP-1) with ≈10% repeating units of activated disulfide as cross-linking sites. Cryo-electron microscopy (Cryo-EM) analysis of MICP-1 reveals an elongated single-chain fiber morphology. MICP-1 shows potential inhibitory activity against many viruses such as herpes simplex virus 1 (HSV-1) and SARS-CoV-2 (including variants such as Delta and Omicron). MICP-1 produces hydrogels with viscoelastic properties similar to healthy human sputum and with tuneable microstructures using linear and branched polyethylene glycol-thiol (PEG-thiol) as cross-linkers. Single particle tracking microrheology, electron paramagnetic resonance (EPR) and cryo-scanning electron microscopy (Cryo-SEM) are used to characterize the network structures. The synthesized hydrogels exhibit self-healing properties, along with viscoelastic properties that are tuneable through reduction. A transwell assay is used to investigate the hydrogel's protective properties against viral infection against HSV-1. Live-cell microscopy confirms that these hydrogels can protect underlying cells from infection by trapping the virus, due to both network morphology and anionic multivalent effects. Overall, this novel mucin-inspired copolymer generates mucus-mimetic hydrogels on a multi-gram scale. These hydrogels can be used as models for disulfide-rich airway mucus research, and as biomaterials.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".