Hirofumi Sawa

This study shows favipiravir suppresses OROV replication and disease in hamsters, preventing neuroinvasion and pathogenic host responses through transcriptomic analysis.

This paper shows that WNV capsid protein causes nuclear deformation via PKCα-dependent lamin phosphorylation, which supports viral replication and disease.

This study evaluates AddaS03 as an adjuvant for a WNV subunit vaccine, showing it elicits higher neutralizing antibody titers and provides full protection in mice compared to Alhydrogel.

This paper develops a CPER-based reverse genetics system for rabies virus, enabling the generation of recombinant viruses with various genetic modifications.

This study establishes a lethal mouse model of Oz virus infection, revealing hepatic involvement and demonstrating the efficacy of antiviral drugs and vaccines against the virus.

This study develops a luciferase-based biosensor to detect and quantify dsRNA from various viral infections, showing broad-spectrum reactivity and utility in assessing antiviral compounds.

This study identifies the non-spike R204P mutation in the SARS-CoV-2 Omicron XEC variant as a driver of increased inflammation and pathogenicity through NF-κB activation.

This paper describes the discovery of S-892216, a potent SARS-CoV-2 3CLpro inhibitor with antiviral activity against multiple variants and low cross-resistance to existing drugs.

This study identifies and characterizes two nearly complete genomes of pigeon gammacoronaviruses, supporting their classification as a new species within the Igacovirus subgenus.

This paper introduces CoVFit, a protein language model that predicts SARS-CoV-2 variant fitness using spike protein sequences, identifying fitness-enhancing mutations and predicting emerging variants.

The paper identifies site-specific glycan compositions of SARS-CoV-2 spike protein using mass spectrometry and lectin microarray, revealing key glycan structures relevant to vaccine design.

LIGHTHOUSE identifies novel therapeutics for diseases including cancer, bacterial infections, and COVID-19 using a graph-based deep learning approach without requiring 3D structural data.