3D Bioprinting of a Bioactive Composite Scaffold for Cell Delivery in Periodontal Tissue Regeneration.

Hydrogels have been widely applied to the fabrication of tissue engineering scaffolds via three-dimensional (3D) bioprinting because of their extracellular matrix-like properties, capacity for living cell encapsulation, and shapeable customization depending on the defect shape. However, the current hydrogel scaffolds show limited regeneration activity, especially in the application of periodontal tissue regeneration. In this study, we attempted to develop a novel multi-component hydrogel that possesses good biological activity, can wrap living cells for 3D bioprinting and can regenerate periodontal soft and hard tissue. The multi-component hydrogel consisted of gelatin methacryloyl (GelMA), sodium alginate (SA) and bioactive glass microsphere (BGM), which was first processed into hydrogel scaffolds by cell-free 3D printing to evaluate its printability and in vitro biological performances. The cell-free 3D-printed scaffolds showed uniform porous structures and good swelling capability. The BGM-loaded scaffold exhibited good biocompatibility, enhanced osteogenic differentiation, apatite formation abilities and desired mechanical strength. The composite hydrogel was further applied as a bio-ink to load with mouse bone marrow mesenchymal stem cells (mBMSCs) and growth factors (BMP2 and PDGF) for the fabrication of a scaffold for periodontal tissue regeneration. The cell wrapped in the hydrogel still maintained good cellular vitality after 3D bioprinting and showed enhanced osteogenic differentiation and soft tissue repair capabilities in BMP2- and PDGF-loaded scaffolds. It was noted that after transplantation of the cell- and growth factor-laden scaffolds in Beagle dog periodontal defects, significant regeneration of gingival tissue, periodontal ligament, and alveolar bone was detected. Importantly, a reconstructed periodontal structure was established in the treatment group eight weeks post-transplantation of the scaffolds containing the cell and growth factors. In conclusion, we developed a bioactive composite bio-ink for the fabrication of scaffolds applicable for the reconstruction and regeneration of periodontal tissue defects.

C-reactive protein could predict the efficacy of SSRIs in clinical practice: A cohort study of large samples in the real world.

BACKGROUND: C-reactive protein (CRP) has been shown to predict antidepressant treatment outcomes in several trials, but they were limited to small-sample and strictly-restricted conditions. This study plans to verify if CRP can predict antidepressant efficacy in large samples in the real world. METHODS: 918 depressed patients who had tested CRP were included, then were followed up through their outpatient visits or by telephone to obtain information about their medication therapy (SSRIs, SNRIs, MT, NaSSA) and assess efficacy using the Clinical Global Impressions-Improvement scale (CGII). Efficacy was classified as effective and ineffective and CRP was separated into the low CRP group (CRP <1 mg/L, n = 709) and the high CRP group (CRP ≥1 mg/L, n = 209).The efficacy was compared in different groups. RESULTS: Using Kaplan-Meier survival analysis and Cox proportional regression model to analyze, it was discovered that SNRIs were more effective than SSRIs in treating patients with high CRP(HR = 1.652, p = 0.037,95 % CI:1.031-2.654), and SSRIs were more effective in treating patients with low CRP than those with high CRP (HR = 1.257, p = 0.047,95 % CI:1.003-1.574), while no difference in efficacy between the two groups was found in patients using SNRIs, MT, NaSSA. LIMITATIONS: Small amounts of MT and NaSSA were included, and some factors that may affect CRP value have not been controlled. CONCLUSION: CRP could predict the efficacy of SSRIs in the real world, depressed patients with high CRP may be more likely to respond poorly to SSRIs.

An SVM-Based NAND Flash Endurance Prediction Method.

NAND flash memory is widely used in communications, commercial servers, and cloud storage devices with a series of advantages such as high density, low cost, high speed, anti-magnetic, and anti-vibration. However, the reliability is increasingly getting worse while process improvements and technological advancements have brought higher storage densities to NAND flash memory. The degradation of reliability not only reduces the lifetime of the NAND flash memory but also causes the devices to be replaced prematurely based on the nominal value far below the minimum actual value, resulting in a great waste of lifetime. Using machine learning algorithms to accurately predict endurance levels can optimize wear-leveling strategies and warn bad memory blocks, which is of great significance for effectively extending the lifetime of NAND flash memory devices and avoiding serious losses caused by sudden failures. In this work, a multi-class endurance prediction scheme based on the SVM algorithm is proposed, which can predict the remaining P-E cycle level and the raw bit error level after various P-E cycles. Feature analysis based on endurance data is used to determine the basic elements of the model. Based on the error features, we present a variety of targeted optimization strategies, such as extracting the numerical features closely related to the endurance, and reducing the noise interference of transient faults through short-term repeated operations. Besides a high-parallel flash test platform supporting multiple protocols, a feature preprocessing module is constructed based on the ZYNQ-7030 chip. The pipelined module of SVM decision model can complete a single prediction within 37 us.