Rational Development of Hypervalent Glycan Shield‐Binding Nanoparticles with Broad‐Spectrum Inhibition against Fatal Viruses Including SARS‐CoV‐2 Variants
Bibliographic record
Abstract
Abstract Infectious virus diseases, particularly coronavirus disease 2019, have posed a severe threat to public health, whereas the developed therapeutic and prophylactic strategies are seriously challenged by viral evolution and mutation. Therefore, broad‐spectrum inhibitors of viruses are highly demanded. Herein, an unprecedented antiviral strategy is reported, targeting the viral glycan shields with hypervalent mannose‐binding nanoparticles. The nanoparticles exhibit a unique double‐punch mechanism, being capable of not only blocking the virus–receptor interaction but also inducing viral aggregation, thereby allowing for inhibiting the virus entry and facilitating the phagocytosis of viruses. The nanoparticles exhibit potent and broad‐spectrum antiviral efficacy to multiple pseudoviruses, including severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and its major variants (D614G, N501Y, N439K, Δ69‐70, Delta, and Omicron; lentiviruses expressing only the spike proteins), as well as other vital viruses (human immunodeficiency virus 1 and Lassa virus), with apparent EC50 values around the 10 −9 m level. Significantly, the broad‐spectrum inhibition of authentic viruses of both wild‐type SARS‐CoV‐2 and Delta variants is confirmed. Therefore, this hypervalent glycan‐shield targeting strategy opens new access to broad‐spectrum viral inhibition.
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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".