Aiming at the shortcomings of high cost and time-consumption in traditional liquid chromatography, an effective surface enhanced Raman scattering (SERS)-based trace detection method has been proposed to quantitatively identify the active component of traditional Chinese medicine. In this paper, a high-performance and versatile SERS platform based on Ag nanoparticles (NPs) self-assembly Ag-anodized aluminium (Ag NPs-Ag-AAO) nanoarray was fabricated by controllable physico-chemical preparation technology. The results indicated that the electromagnetic field enhancement effect was sharply strengthened as Ag NPs assembled, and the experimental enhancement factor (EEF) value was calculated to be 1.0083 × 10⁶. This novel Ag NPs-Ag-AAO nanoarray with substantial “hot spots” exhibited high SERS signal reproducibility, with the relative standard deviation (RSD) value at less than 2.23%. More importantly, this SERS platform was applied to detect active component Baicalein in Scutellaria baicalensis, and the limit of detection (LOD) was located at 10 fg/mL. Therefore, this Ag NPs-Ag-AAO nanoarray with high sensitivity, strong Raman signal reproducibility and reliable practicability has broad application prospects in the rapid detection of trace substances in the active components of traditional Chinese medicine and is expected to be popularized.

TPMS porous structures have adjustable stiffness, a smooth surface, and highly connected pores, which help avoid stress concentration within the dot-matrix structure and promote cell adhesion and proliferation. A cervical interbody cage based on this type of porous structure was designed and fabricated, and its mechanical properties and biocompatibility were evaluated. TPMS porous structures have adjustable stiffness, a smooth surface, and highly connected pores, which help avoid stress concentration within the dot-matrix structure and promote cell adhesion and proliferation. A cervical interbody cage based on this type of porous structure was designed and fabricated, and its mechanical properties and biocompatibility were evaluated. The volume fraction of the 3D-printed TC4-based Tubular-G structure was linearly related to compressive strength. Adjusting the volume fraction resulted in a Tubular-G structure with a modulus and yield strength similar to human bone, without stress concentration within the structure. The designed and fabricated TC4-based Tubular-G porous cervical interbody cage demonstrated excellent anti-sagging properties and biocompatibility. The volume fraction of the 3D-printed TC4-based Tubular-G structure was linearly related to compressive strength. Adjusting the volume fraction resulted in a Tubular-G structure with a modulus and yield strength similar to human bone, without stress concentration within the structure. The designed and fabricated TC4-based Tubular-G porous cervical interbody cage demonstrated excellent anti-sagging properties and biocompatibility.

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 10⁷ and the limit of detection (LOD) was as low as 1 × 10⁻¹⁰ M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.

Preparation and in situ mineralization process of bone tissue engineering scaffold.

An effective SERS-based detection method assisted by machine learning algorithm has been developed to intelligently identify the pharmacodynamic substances in traditional Chinese medicine (TCM) which aided in the quality control and identification of TCM. In this work, the ultrasensitive grating-like core-shell Ag@SiO ₂ @Cu@moth wing (Ag@SiO ₂ @Cu@MW) SERS platform was explored which can precisely control the intergap distances by adjusting the thickness of the SiO ₂ layer. This SERS platform demonstrated reproducibility and reliability, with a low relative standard deviation (RSD) of 7.38% and an enhancement factor (EF) of 2.49 × 10 ⁷ . The optimized Ag ₃₀ @SiO ₂ (4)@Cu ₂₀ @MW substrate were then applied in the accurately analyzing pharmacodynamic substances. Specifically, the label-free SERS analysis showed the distinct spectral features for Jujuboside A, Saikosaponin A and Timosaponin A-III. Machine learning algorithms, such as principal component analysis (PCA), decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN) were employed and further in differentiating with the three pharmacodynamic substances Raman spectrum groups. These results indicate that SERS technology in combination with machine learning algorithms can not only achieve rapid and accurate detection of different types of pharmacodynamic substances, but also promote the modernization and international application of TCM.