Sustained release silicon from 3D bioprinting scaffold using silk/gelatin inks to promote osteogenesis.

Repairing extensive bone defects that cannot self-heal has been a clinical challenge. The construction of scaffolds with osteogenic activity through tissue engineering can provide an effective strategy for bone regeneration. This study utilized gelatin, silk fibroin, and Si3N4 as scaffold materials to prepare silicon-functionalized biomacromolecules composite scaffolds using three-dimensional printing (3DP) technology. This system delivered positive outcomes when Si3N4 levels were 1 % (1SNS). The results showed that the scaffold had a porous reticular structure with a pore size of 600-700 µm. The Si3N4 nanoparticles were distributed uniformly in the scaffold. The scaffold could release Si ions for up to 28 days. In vitro experiments showed that the scaffold had good cytocompatibility, promoting the osteogenic differentiation of mesenchymal stem cells (MSCs). In vivo experiments on bone defects in rats showed that the 1SNS group facilitated bone regeneration. Therefore, the composite scaffold system showed potential for application in bone tissue engineering.

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy.

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy.

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Rapid Determination of Puerarin by Near-infrared Spectroscopy During Percolation and Concentration Process of Puerariae Lobatae Radix.

BACKGROUND: Gegen (Puerariae Labatae Radix) is one of the important medicines in Traditional Chinese Medicine. The studies showed that Gegen and its preparation had effective actions for atherosclerosis. OBJECTIVE: Near-infrared (NIR) was used to develop a method for rapid determination of puerarin during percolation and concentration process of Gegen. MATERIALS AND METHODS: About ten batches of samples were collected with high-performance liquid chromatography analysis values as reference, calibration models are generated by partial least-squares (PLS) regression as linear regression, and artificial neural networks (ANN) as nonlinear regression. RESULTS: The root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively. CONCLUSIONS: The NIR model for the rapid analysis of puerarin can be used for on-line quality control in the percolation and concentration process. SUMMARY: Near-infrared was used to develop a method for on-line quality control in the percolation and concentration process of GegenCalibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regressionThe root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively. Abbreviations used: NIR: Near-Infrared Spectroscopy; Gegen: Puerariae Loabatae Radix; TCM: Traditional Chinese Medicine; PLS: Partial least-squares; ANN: Artificial neural networks; RMSEP: Root mean square error of validation; R2: Correlation coefficients; PAT: Process analytical technology; FDA: The Food and Drug Administration; Rcal: Calibration set; RMSECV: Root mean square errors of cross-validation; RPD: Residual predictive deviation; SLS: Straight Line Subtraction; MLP: Multi-Layer Perceptron; MSE: Mean square error.