Pulmonary diseases induced by ambient ultrafine and engineered nanoparticles in twenty-first century.

Air pollution is a severe threat to public health globally, affecting everyone in developed and developing countries alike. Among different air pollutants, particulate matter (PM), particularly combustion-produced fine PM (PM2.5) has been shown to play a major role in inducing various adverse health effects. Strong associations have been demonstrated by epidemiological and toxicological studies between increases in PM2.5 concentrations and premature mortality, cardiopulmonary diseases, asthma and allergic sensitization, and lung cancer. The mechanisms of PM-induced toxicological effects are related to their size, chemical composition, lung clearance and retention, cellular oxidative stress responses and pro-inflammatory effects locally and systemically. Particles in the ultrafine range (<100 nm), although they have the highest number counts, surface area and organic chemical content, are often overlooked due to insufficient monitoring and risk assessment. Yet, ample studies have demonstrated that ambient ultrafine particles have higher toxic potential compared with PM2.5. In addition, the rapid development of nanotechnology, bringing ever-increasing production of nanomaterials, has raised concerns about the potential human exposure and health impacts. All these add to the complexity of PM-induced health effects that largely remains to be determined, and mechanistic understanding on the toxicological effects of ambient ultrafine particles and nanomaterials will be the focus of studies in the near future

Metabolism and Metabolic Inhibition of Xanthotoxol in Human Liver Microsomes

Cytochrome p450 (CYP450) enzymes are predominantly involved in Phase I metabolism of xenobiotics. In this study, the CYP450 isoforms involved in xanthotoxol metabolism were identified using recombinant CYP450s. In addition, the inhibitory effects of xanthotoxol on eight CYP450 isoforms and its pharmacokinetic parameters were determined using human liver microsomes. CYP1A2, one of CYP450s, played a key role in the metabolism of xanthotoxol compared to other CYP450s. Xanthotoxol showed stronger inhibition on CYP3A4 and CYP1A2 compared to other isoenzymes with the IC50 of 7.43 μM for CYP3A4 and 27.82 μM for CYP1A2. The values of inhibition kinetic parameters (Ki) were 21.15 μM and 2.22 μM for CYP1A2 and CYP3A4, respectively. The metabolism of xanthotoxol obeyed the typical monophasic Michaelis-Menten kinetics and Vmax, Km, and CLint values were calculated as 0.55 nmol·min−1·mg−1, 8.46 μM, and 0.06 mL·min−1·mg−1. In addition, the results of molecular docking showed that xanthotoxol was bound to CYP1A2 with hydrophobic and π-π bond and CYP3A4 with hydrogen and hydrophobic bond. We predicted the hepatic clearance (CLh) and the CLh value was 15.91 mL·min−1·kg−1 body weight. These data were significant for the application of xanthotoxol and xanthotoxol-containing herbs

An Application Study on AI Educational Robots in Spoken English Exercises of Chinese Primary Schools

China’s primary schools offer limited English courses, and the society lacks the environment for naturally acquiring English in everyday life. Students typically have weak spoken English abilities and inadequate application of English. With the aim of addressing this issue, 79 fifth-graders from China’s Hangzhou L elementary school participated in a one-semester AI-assisted English-speaking practice experiment. The control class practiced spoken English by reading English texts aloud, whereas the experimental class practiced for 30 minutes a day using the “AI educational robots + graded picture books + role play” approach. According to the results of the experiment’s post-test, Chinese primary school students regarded the experimental class’s acquisition mode to be highly appealing, this approach was well accepted by both students and parents and brought enthusiasm and good effect of spoken English exercise. The experimental class’s average daily reading time for English role-play reading grew by about 30 minutes, the amount of reading increased by five times, the amount of time spent watching cartoons and playing video games fell by nearly 28 minutes, and the spoken English score climbed by 37 points, representing an increase of 82% when compared to the control class; Additionally, the standard level of pronunciation and intonation has increased by two grades, from “poor” to “good,” and the English final exam scores have increased by roughly 8%. However, there has not been a considerable change in the aforementioned control class indicators. This AI-assisted second language practice technique is affordable, efficient, and helpful and has good implications for second language acquisition in other countries

Early Verification of Legal Compliance via Bounded Satisfiability Checking

Legal properties involve reasoning about data values and time. Metric first-order temporal logic (MFOTL) provides a rich formalism for specifying legal properties. While MFOTL has been successfully used for verifying legal properties over operational systems via runtime monitoring, no solution exists for MFOTL-based verification in early-stage system development captured by requirements. Given a legal property and system requirements, both formalized in MFOTL, the compliance of the property can be verified on the requirements via satisfiability checking. In this paper, we propose a practical, sound, and complete (within a given bound) satisfiability checking approach for MFOTL. The approach, based on satisfiability modulo theories (SMT), employs a counterexample-guided strategy to incrementally search for a satisfying solution. We implemented our approach using the Z3 SMT solver and evaluated it on five case studies spanning the healthcare, business administration, banking and aviation domains. Our results indicate that our approach can efficiently determine whether legal properties of interest are met, or generate counterexamples that lead to compliance violations