Atezolizumab, an anti-programmed death-ligand 1 (PD-L1) agent, is effective and well tolerated in patients with pretreated advanced non-small-cell lung cancer (NSCLC). We assessed its efficacy and safety in Japanese patients through subgroup analyses of the phase 3 OAK study (NCT02008227). Key eligibility criteria of this randomized, controlled, open-label, international study include locally advanced/metastatic NSCLC, ≥ 1 prior platinum-based chemotherapy, age ≥ 18 years, measurable disease (Response Evaluation Criteria in Solid Tumors v1.1), and Eastern Cooperative Oncology Group performance status 0 or 1. Atezolizumab 1200 mg or docetaxel 75 mg/m² was provided intravenously every 3 weeks. Co-primary end points were overall survival (OS) in the intention-to-treat (ITT) population and those with ≥ 1% PD-L1 expression on tumor cells (TC) or tumor-infiltrating immune cells (IC; TC1/2/3 or IC1/2/3). Sixty-four ITT patients were Japanese; 19 had TC1/2/3 or IC1/2/3 status. In Japanese ITT patients, median OS in the atezolizumab arm (n = 36) was longer than the docetaxel arm (n = 28; 21.3 months [95% confidence interval (CI), 11.0-not estimable (NE)] versus 17.0 months [95% CI, 12.5-NE], respectively; hazard ratio 0.80 [95% CI, 0.41-1.57]). In the TC1/2/3 or IC1/2/3 population, median OS was 21.3 months (95% CI, 15.0-NE) and NE in the atezolizumab (n = 11) and docetaxel (n = 8) groups, respectively (hazard ratio, 0.81 [95% CI, 0.22-3.05]). Atezolizumab was generally well tolerated, with no treatment-related deaths. Atezolizumab was effective and well tolerated in pretreated Japanese patients with NSCLC. Results are consistent with the primary analysis of OAK.

Digital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability to adapt to all kinds of errors. Errors in biochips can be categorized into two types: known errors, and unknown errors. Known errors are detectable before the start of the routing process using sensors or cameras. Unknown errors, in contrast, only become apparent during the routing process and remain undetected by sensors or cameras, which can unexpectedly stop the routing process and diminish the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm, designed to manage not only known errors but also unknown errors. Our experiments demonstrated that our algorithm outperformed the previous ones in terms of the success rate of the routing, in the scenarios including both known errors and unknown errors. Additionally, our algorithm contributed to detecting unknown errors during the routing process, identifying the most efficient routing path with a high probability.

Digital Microfluidic Biochips (DMFBs), used in various kinds of fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than previous ways. However, their reliability is often compromised by their inability to adapt to all kinds of errors. All errors in biochips can be categorized into two types: known errors and unknown errors. Known errors are detectable before the start of the routing process through sensors or cameras. Unknown errors, in contrast, become apparent only during the routing process and remain undetected by sensors or cameras, which is the biggest issue to unexpectedly stop the routing process and diminishes the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm designed to manage not only known errors but also unknown errors. Our experiments demonstrate that our algorithm outperforms previous ones in terms of the success rate of the routing in the scenario including both known errors and unknown errors. Additionally, our algorithm contributes to detecting unknown errors during the routing process and identifying the most efficient routing path with high probability.