SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID

Synthesis, Crystal Structure, and Spectral Study of a NovelIndenoimidazole Carboxylic Acid Amide

3a,8a-Dihydroxy-2,8-dioxo-1,3a,8,8a-tetrahydro-2H-indeno[1,2-d]imidazole-3-carboxamide has been synthesized in 85% yield fromninhydrin and biuret via a green facile procedure that is free of workup andcolumn chromatography. The product structure has been established based onFT-IR, NMR, and mass spectra and finally confirmed by single crystal X-rayanalysis. The compound crystalized as monoclinic colorless plates with P21/c space group

Time-critical fog computing for vehicular networks

Moving applications from the local infrastructure to a central data center using cloud computing has been the highlight of the current distributed computing era. This paradigm shift has enabled various new use cases such as computing and storage everywhere and on-demand. A prominent example for cloud computing is off-loading computation and data from smartphones to data centers. The Internet of Things (IoT) aims to connect people and objects such as vehicles, machines, and products with people through the Internet and the cloud. The number of IoT devices significantly has grown in recent years resulting in massive volume of data transferred to the cloud for analysis. Connecting vehicles as a smart thing is required for enhancing the vehicle’s perception of its surroundings and increasing road safety as well as traffic efficiency. Some applications such as navigation may be delay-tolerant and can be still further distributed among the vehicles and the cloud. Other applications such as collision warnings are delay-critical and accordingly short-lived. For example, detecting and identifying an immediate obstacle on the road requires a quick processing of onboard sensor data (e.g. camera, radar, and LIDAR) in a few tens of milliseconds. With this unprecedented evolution, fog computing has been proposed to support delay-critical computational demand, security issues, communication latency and improve quality of service of vehicular applications. Fog computing is complementary to cloud computing. The interplay of both concepts is now considered as the real enabling architecture for virtually all vehicular networked applications. In this chapter, we first present various scenarios of time-critical applications and their timeliness requirement. We define our application model based on a set of middleware building blocks in order to reach the timeliness guarantees. The key building blocks are resource monitoring, task scheduling, real-time computation, and real-time communication. Then we show how the perturbations can interrupt the communication and the computation between vehicles and/or infrastructures, which is a serious problem in such critical scenarios. Next, we critically review existing research efforts to cope with failures, threats, and constraints of the network, computation, and data management so as to efficiently meet the timeliness requirements despite the perturbation. In particular, we provide a taxonomy of fog computing according to the research topics. Throughout the chapter, we identify research gaps and sketch future research directions

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery