The dengue-specific immune response and antibody identification with machine learning.

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response.

RWD-Cockpit: Application for Quality Assessment of Real-world Data.

Background: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences.

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design.

Synergic complement-mediated bactericidal activity of monoclonal antibodies with distinct specificity.

The classical complement pathway is triggered when antigen-bound immunoglobulins bind to C1q through their Fc region. While C1q binds to a single Fc with low affinity, a higher avidity stable binding of two or more of C1q globular heads initiates the downstream reactions of the complement cascade ultimately resulting in bacteriolysis. Synergistic bactericidal activity has been demonstrated when monoclonal antibodies recognize nonoverlapping epitopes of the same antigen.