Double synergic chitosan-coated poly (lactic-co-glycolic) acid nanospheres loaded with nucleic acids as an intranasally administered vaccine delivery system to control the infection of foot-and-mouth disease virus.

BACKGROUND & AIMS: The spread of foot-and-mouth disease virus (FMDV) through aerosol droplets among cloven-hoofed ungulates in close contact is a major obstacle for successful animal husbandry. Therefore, the development of suitable mucosal vaccines, especially nasal vaccines, to block the virus at the initial site of infection is crucial. PATIENTS AND METHODS: Here, we constructed eukaryotic expression plasmids containing the T and B-cell epitopes (pTB) of FMDV in tandem with the molecular mucosal adjuvant Fms-like tyrosine kinase receptor 3 ligand (Flt3 ligand, FL) (pTB-FL). Then, the constructed plasmid was electrostatically attached to mannose-modified chitosan-coated poly(lactic-co-glycolic) acid (PLGA) nanospheres (MCS-PLGA-NPs) to obtain an active nasal vaccine targeting the mannose-receptor on the surface of antigen-presenting cells (APCs). RESULTS: The MCS-PLGA-NPs loaded with pTB-FL not only induced a local mucosal immune response, but also induced a systemic immune response in mice. More importantly, the nasal vaccine afforded an 80% protection rate against a highly virulent FMDV strain (AF72) when it was subcutaneously injected into the soles of the feet of guinea pigs. CONCLUSIONS: The nasal vaccine prepared in this study can effectively induce a cross-protective immune response against the challenge with FMDV of same serotype in animals and is promising as a potential FMDV vaccine.

[Research on wavelength variates selection methods for determination of oil yield in oil shales using near-infrared spectroscopy].

The wavelength selection is an important step in the spectra modeling analysis. In the present paper, three wavelength selection methods, including correlation coefficient (CC), moving window partial least squares (MWPLS) and uninformative variables elimination (UVE), were studied for the determination of oil yield in oil shale using near-infrared (NIR) diffuse reflection spectroscopy. The above methods were used to eliminate the redundant and irrelevant variables in spectral data for enhancing the analytic efficiency and predictive ability of calibration model. The effects of thresholds of CC, window width of MWPLS and noise matrix of UVE were studied. Partial least squares regression was used to build prediction model for predicting oil yield in oil shale, and the performance of PLS models constructed with and without the using of wavelength selection methods were compared. The results show that any of the three methods can simplify the calibration model and improve the performance of model. By using UVE, the total number of wavelength variables of spectral data, the RMSECV of calibration model and the RMSEP of prediction model were decreased by 22.8%, 9.3% and 4.5%, respectively.

[Analyzing and modeling methods of near infrared spectroscopy for in-situ prediction of oil yield from oil shale].

In order to in-situ detect the oil yield of oil shale, based on portable near infrared spectroscopy analytical technology, with 66 rock core samples from No. 2 well drilling of Fuyu oil shale base in Jilin, the modeling and analyzing methods for in-situ detection were researched. By the developed portable spectrometer, 3 data formats (reflectance, absorbance and K-M function) spectra were acquired. With 4 different modeling data optimization methods: principal component-mahalanobis distance (PCA-MD) for eliminating abnormal samples, uninformative variables elimination (UVE) for wavelength selection and their combina- tions: PCA-MD + UVE and UVE + PCA-MD, 2 modeling methods: partial least square (PLS) and back propagation artificial neural network (BPANN), and the same data pre-processing, the modeling and analyzing experiment were performed to determine the optimum analysis model and method. The results show that the data format, modeling data optimization method and modeling method all affect the analysis precision of model. Results show that whether or not using the optimization method, reflectance or K-M function is the proper spectrum format of the modeling database for two modeling methods. Using two different modeling methods and four different data optimization methods, the model precisions of the same modeling database are different. For PLS modeling method, the PCA-MD and UVE + PCA-MD data optimization methods can improve the modeling precision of database using K-M function spectrum data format. For BPANN modeling method, UVE, UVE + PCA-MD and PCA- MD + UVE data optimization methods can improve the modeling precision of database using any of the 3 spectrum data formats. In addition to using the reflectance spectra and PCA-MD data optimization method, modeling precision by BPANN method is better than that by PLS method. And modeling with reflectance spectra, UVE optimization method and BPANN modeling method, the model gets the highest analysis precision, its correlation coefficient (Rp) is 0.92, and its standard error of prediction (SEP) is 0.69%.