OBJECTIVE: To discuss the focus of the legislative protective of Chinese traditional medicine. METHODS: The key elements in the legislative protective of Chinese traditional medicine were analyzed. RESULTS & CONCLUSION: Chinese traditional medicine literature, Chinese herbal medicine resources and medicinal genetic resources should be considered as the key elements in the legislative protective of Chinese traditional medicine.

Objective To establish a rapid and convenient method for determination of genomic DNA methylation in cells.Methods Five standard substances (dC, mdC, dA, dT and dG) were separated by high-performance capillary electrophoresis.Bare fused-silica capillary was used and eletrophoresis buffer was 48 mmol/L NaHCO3 with 60 mmol/L SDS, pH 9.6.The temperature of separation was controlled at 25 ℃ and a voltage of 20 kV was applied.The separation of the mixture was performed at a wavelength of 256 nm with UV-Vis detection and injection time was 5 seconds at 0.7 psi.Under optimal condition,genomic DNA methylation in methotrexate drug-resistant A549 cells was detected.Results The optimal condition was made by adjusting SDS concentration(40, 60, 80 mmol/L), pH value of running buffer(9.4,9.6, 9.8), voltage(15, 17, 19, 20, 22 kV), injection time(5, 10, 15, 20, 30 s) and capillary temperature(15, 20, 25, 30 ℃).The method for determination of genomic DNA methylation in cells was established.Five substances were completely separated by high-performance capillary electrophoresis in 10 mins.Intra-day coefficient of variation was less than 0.2% and inter-day coefficient of variation was less than 2%.The minimal detection limit was 2 μmol/L.Percentage of mdC in A549 parent cells was (4.80 ±0.52) %.Percentage of mdC in 15, 30, 40 μmol/L methotrexate drug-resistant A549 cells were (4.20±0.44) %, ( 3.70 ± 0.36 ) %, (3.10±0.35 ) %, respectively.Conclusions Genomic DNA methylation can be quantificated by high-performance capillary electrophoresis efficiently, rapidly, conveniently and sensitively.Genomic DNA methylation in methotrexate drug resistance cells decreases significantly.

Objective To establish a method for detection folylpolyglutamate syntbetase (FPGS),explore the change of FPGS in the drug-resistant A549 cells induced by methotrexate(MTX) enantiomer,and provide new tools to further investigate drug resistant mechanism. Methods A549 cell lines induced by L-( + )-MTX and D-( - )-MTX (25 μmol/L) were chosen to raise three cell lines as compared with MTX-sensitive cell line. Then FPGS were extracted for the CEIA-LIF and western blot was performed. After validation, FPGS antibodies were labeled by fluoreacein isothiocyanate (FITC) and produced a immune response with former-extracted FPGS. CELA-LIF can separate and detect labeled proteins according ruination time of the protein with different size and detect FPGS in drug resistant cell lines induced with L-(+)-MTX and D-(- )-MTX. The accuracy was evaluated as compared with western blot assay. Results The separation time of CEIA-LIF for labeled FPGS antibody and the immune complexes were7. 1 min and 8.9 min, and the resolving power was 4. 5. The process of protein separation and detection can be accomplished in less than 10 minutes. Western blot analysis showed there was no non-specific bands appears in the extract of these three cell lines after the freeze-thaw in liquid nitrogen. The minimum detection level in sensitive cell strains was 0. 68 mg/μl. The consent of FPGS in L-(+)-MTX and D-( - )-MTX induced cells were 46. 59% and 48. 36% compared with drug sensitive cell strains with CELA-LIF. Conclusions CELA-LIF was established in this experiment. It is efficient and sensitive for detecting of FPGS, which is similar to western blot method. The level of FPGS in L-( + )-MTX and D-( - )-MTX induced drug resistant cell lines is significantly lower, indicating the expression of FPGS is damaged.

In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
Algorithms ; Exoskeleton Device ; Humans ; Lower Extremity ; Motion ; Support Vector Machine