Dextromethorphan attenuated the higher vulnerability to inflammatory thermal hyperalgesia caused by prenatal morphine exposure in rat offspring

<p>Abstract</p> <p>Background</p> <p>Co-administration of dextromethorphan (DM) with morphine during pregnancy and throughout lactation has been found to reduce morphine physical dependence and tolerance in rat offspring. No evidence was presented, however, for the effect of DM co-administered with morphine during pregnancy on inflammatory hyperalgesia in morphine-exposed offspring. Therefore, we attempt to investigate the possible effect of prenatal morphine exposure on the vulnerability to hyperalgesia and the possible therapeutic effect of DM in the present study.</p> <p>Methods</p> <p>Fifty μl of carrageenan (20 mg/ml) was injected subcutaneously into the plantar surface of the right hind paw in p18 rats to induce hyperalgesia. Mean paw withdrawal latency was measured in the plantar test to index the severity of hyperalgesia. Using Western blotting and RT-PCR, the quantitative analyses of NMDA receptor NR1 and NR2B subunits were performed in spinal cords from different groups of animals.</p> <p>Results</p> <p>In the carrageenan-induced hyperalgesia model, rat offspring passively exposed to morphine developed a severe hyperalgesia on postnatal day 18 (p18), which also had a more rapid time course than those in the controls. Co-administration of DM with morphine in the dams prevented this adverse effect of morphine in the offspring rats. Western blot and RT-PCR analysis showed that the levels of protein and mRNA of NMDA receptor NR1 and NR2B subunits were significantly higher in the lumbar spinal cords of rats (p14) exposed to prenatal morphine; the co-administration of DM could reverse the effect of morphine on NR1 and attenuate the effect on NR2B.</p> <p>Conclusions</p> <p>Thus, DM may have a great potential in the prevention of higher vulnerability to inflammatory thermal hyperalgesia in the offspring of morphine-addicted mothers.</p

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours