The JAK1/2 inhibitor ruxolitinib in patients with COVID-19 triggered hyperinflammation: the RuxCoFlam trial

Dysregulated hyperinflammatory response is key in the pathogenesis in patients with severe COVID-19 leading to acute respiratory distress syndrome and multiorgan failure. Whilst immunosuppression has been proven to be effective, potential biological targets and optimal timing of treatment are still conflicting. We sought to evaluate efficacy and safety of the Janus Kinase 1/2 inhibitor ruxolitinib, employing the previously developed COVID-19 Inflammation Score (CIS) in a prospective multicenter open label phase II trial (NCT04338958). Primary objective was reversal of hyperinflammation (CIS reduction of ≥25% at day 7 in ≥20% of patients). In 184 patients with a CIS of ≥10 (median 12) ruxolitinib was commenced at an initial dose of 10 mg twice daily and applied over a median of 14 days (range, 2–31). On day 7, median CIS declined to 6 (range, 1–13); 71% of patients (CI 64–77%) achieved a ≥25% CIS reduction accompanied by a reduction of markers of inflammation. Median cumulative dose was 272.5 mg/d. Treatment was well tolerated without any grade 3–5 adverse events related to ruxolitinib. Forty-four patients (23.9%) died, all without reported association to study drug. In conclusion, ruxolitinib proved to be safe and effective in a cohort of COVID-19 patients with defined hyperinflammation

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, since constructing such an exhaustive training dataset is very challenging in practice, it is desirable that a robotic system can autonomously learn and improves its grasping strategy. In this paper, we address this problem using reinforcement learning. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g., vertical pinch grasp. We present a hierarchical policy search approach for learning multiple grasping strategies. Our framework autonomously constructs a database of grasping motions and point clouds of objects to learn multiple grasping types autonomously. We formulate the problem of selecting the grasp location and grasp policy as a bandit problem, which can be interpreted as a variant of active learning. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy

Nucleotide sequence of the self-priming 3' terminus of the single-stranded DNA extracted from the parvovirus Kilham rat virus

The parvovirus genome is a linear, single-stranded DNA molecule with double-stranded hairpin termini. The 3' terminus can serve in vitro as a self-primer for the synthesis of a double-stranded viral DNA intermediate. We have sequenced the nucleotides in the 3' terminus and propose a model for the secondary structure of the terminus and the in vitro origin of replication for the complementary viral DNA strand.</jats:p