Targeting NOX2 with Bivalent Small-Molecule p47phox-p22phox Inhibitors.

Nicotinamide adenine dinucleotide phosphate oxidase isoform 2 (NOX2) is an enzymatic complex whose function is the regulated generation of reactive oxygen species (ROS). NOX2 activity is central to redox signaling events and antibacterial response, but excessive ROS production by NOX2 leads to oxidative stress and inflammation in a range of diseases. The protein-protein interaction between the NOX2 subunits p47phox and p22phox is essential for NOX2 activation, thus p47phox is a potential drug target. Previously, we identified 2-aminoquinoline as a fragment hit toward p47phoxSH3A-B and converted it to a bivalent small-molecule p47phox-p22phox inhibitor (Ki = 20 µM). Here, we systematically optimized the bivalent compounds by exploring linker types and positioning as well as substituents on the 2-aminoquinoline part and characterized the bivalent binding mode with biophysical methods. We identified several compounds with submicromolar binding affinities and cellular activity and thereby demonstrated that p47phox can be targeted by potent small molecules.

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study.

BACKGROUND: Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. OBJECTIVE: This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. METHODS: To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. RESULTS: With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. CONCLUSIONS: Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

RSV Vaccine Based on Rhabdoviral Vector Protects after Single Immunization.

The respiratory syncytial virus (RSV) is one major cause of lower respiratory tract infections in childhood and an effective vaccine is still not available. We previously described a new rhabdoviral vector vaccine, VSV-GP, a variant of the vesicular stomatitis virus (VSV), where the VSV glycoprotein G is exchanged by the glycoprotein GP of the lymphocytic choriomeningitis virus. Here, we evaluated VSV-GP as vaccine vector for RSV with the aim to induce RSV neutralizing antibodies. Wild-type F (Fwt) or a codon optimized version (Fsyn) were introduced at position 5 into the VSV-GP genome. Both F versions were efficiently expressed in VSV-GP-F infected cells and incorporated into VSV-GP particles. In mice, high titers of RSV neutralizing antibodies were induced already after prime and subsequently boosted by a second immunization. After challenge with RSV, viral loads in the lungs of immunized mice were reduced by 2-3 logs with no signs of an enhanced disease induced by the vaccination. Even a single intranasal immunization significantly reduced viral load by a factor of more than 100-fold. RSV neutralizing antibodies were long lasting and mice were still protected when challenged 20 weeks after the boost. Therefore, VSV-GP is a promising candidate for an effective RSV vaccine.