Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization.

SignificanceSARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus's evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtained an antibody that broadly neutralizes SARS-CoV-2 variants.

Static Magnetic Field Promotes Proliferation, Migration, Differentiation, and AKT Activation of Periodontal Ligament Stem Cells.

Periodontal ligament stem cells (PDLSCs) possess self-renewal and multilineage differentiation potential and exhibit great potential for the treatment of bone tissue defects caused by inflammation. Previous studies have indicated that static magnetic field (SMF) can enhance the proliferation and differentiation of mesenchymal stem cells (MSCs). SMF has been widely used to repair bone defects and for orthodontic and implantation treatment. In this study, we revealed that a 320 mT SMF upregulates the protein expression levels of cytokines such as MCM7 and PCNA in proliferating PDLSCs. Cell counting kit-8 results revealed that the SMF group had higher optical density values than the control group. The ratio of cells in the S phase to those in the G2/M phase was significantly increased after exposure to a 320 mT SMF. In scratch assays, the SMF-treated PDLSCs exhibited a higher migration rate than the sham-exposed group after 24 h of culture, indicating that the SMF promoted the migratory ability of PDLSCs. The activity level of the early differentiation marker alkaline phosphatase and the late marker matrix mineralization, as well as osteoblast-specific gene and protein expression, were enhanced in PDLSCs exposed to the SMF. Furthermore, AKT signaling pathway was activated by SMF. Our data demonstrated that the potential mechanism of action of SMF may enhance PDLSCs proliferation and osteogenic differentiation by activating the phosphorylated AKT pathway. The elucidation of this molecular mechanism may lead to a better understanding of bone repair responses and aid in improved stem cell-mediated regeneration.

Static magnetic field-induced IL-6 secretion in periodontal ligament stem cells accelerates orthodontic tooth movement.

Static magnetic field (SMF) promoting bone tissue remodeling is a potential non-invasive therapy technique to accelerate orthodontic tooth movement (OTM). The periodontal ligament stem cells (PDLSCs), which are mechanosensitive cells, are essential for force-induced bone remodeling and OTM. However, whether and how the PDLSCs influence the process of inflammatory bone remodeling under mechanical force stimuli in the presence of SMFs remains unclear. In this study, we found that local SMF stimulation significantly enhanced the OTM distance and induced osteoclastogenesis on the compression side of a rat model of OTM. Further experiments with macrophages cultured with supernatants from force-loaded PDLSCs exposed to an SMF showed enhanced osteoclast formation. RNA-seq analysis showed that interleukin-6 (IL-6) was elevated in force-loaded PDLSCs exposed to SMFs. IL-6 expression was also elevated on the pressure side of a rat OTM model with an SMF. The OTM distance induced by an SMF was significantly decreased after injection of the IL-6 inhibitor tocilizumab. These results imply that SMF promotes osteoclastogenesis by inducing force-loaded PDLSCs to secrete the inflammatory cytokine IL-6, which accelerates OTM. This will help to reveal the mechanism of SMF accelerates tooth movement and should be evaluated for application in periodontitis patients.

Deep Point Set Resampling via Gradient Fields.

3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. They are often perturbed by noise or suffer from low density, which obstructs downstream tasks such as surface reconstruction and understanding. In this paper, we propose a novel paradigm of point set resampling for restoration, which learns continuous gradient fields of point clouds that converge points towards the underlying surface. In particular, we represent a point cloud via its gradient field-the gradient of the log-probability density function, and enforce the gradient field to be continuous, thus guaranteeing the continuity of the model for solvable optimization. Based on the continuous gradient fields estimated via a proposed neural network, resampling a point cloud amounts to performing gradient-based Markov Chain Monte Carlo (MCMC) on the input noisy or sparse point cloud. Further, we propose to introduce regularization into the gradient-based MCMC during point cloud restoration, which essentially refines the intermediate resampled point cloud iteratively and accommodates various priors in the resampling process. Extensive experimental results demonstrate that the proposed point set resampling achieves the state-of-the-art performance in representative restoration tasks including point cloud denoising and upsampling.

Role of histone deacetylase 9 in human periodontal ligament stem cells autophagy in a tumour necrosis factor α-induced inflammatory environment.

Histone deacetylases (HDACs) play important roles in the post-translational modification of histones, which can affect the biological properties of cells, thereby altering disease progression and outcomes. However, it remains unclear how HDAC9, a class II HDAC, affects the autophagy of human periodontal ligament stem cells (hPDLSCs). We aimed to identify its role in autophagy in hPDLSCs in an inflammatory environment and to explore the potential regulatory mechanisms. A rat periodontitis model was induced by ligating the molars with silk thread. Expression of autophagy-related genes and TNF-α was elevated in this model. TNF-α was used to stimulate hPDLSCs to establish an inflammatory environment. In the TNF-α-stimulated hPDLSCs, the expression of ATG7, ATG12, Beclin-1, LC3 and HDAC9 was upregulated, and that of p62 was downregulated. When HDAC9 expression was inhibited, autophagy-related genes expression was downregulated, and p62 expression was upregulated in TNF-α-treated hPDLSCs, indicating that autophagy was inhibited under these conditions. ERK pathway inhibition significantly reduced HDAC9-mediated autophagy in TNF-α-treated hPDLSCs. These findings reveal that autophagy occurred in our rat periodontitis model and that HDAC9 regulated autophagy via ERK pathways in hPDLSCs in the inflammatory environment. HDAC9 is therefore a potential target for the treatment of periodontitis.

Mutational fitness landscape of human influenza H3N2 neuraminidase.

Influenza neuraminidase (NA) has received increasing attention as an effective vaccine target. However, its mutational tolerance is not well characterized. Here, the fitness effects of >6,000 mutations in human H3N2 NA are probed using deep mutational scanning. Our result shows that while its antigenic regions have high mutational tolerance, there are solvent-exposed regions with low mutational tolerance. We also find that protein stability is a major determinant of NA mutational fitness. The deep mutational scanning result correlates well with mutational fitness inferred from natural sequences using a protein language model, substantiating the relevance of our findings to the natural evolution of circulating strains. Additional analysis further suggests that human H3N2 NA is far from running out of mutations despite already evolving for >50 years. Overall, this study advances our understanding of the evolutionary potential of NA and the underlying biophysical constraints, which in turn provide insights into NA-based vaccine design.

Validation of Material Algorithms for Femur Remodelling Using Medical Image Data.

The aim of this study is the utilization of human medical CT images to quantitatively evaluate two sorts of "error-driven" material algorithms, that is, the isotropic and orthotropic algorithms, for bone remodelling. The bone remodelling simulations were implemented by a combination of the finite element (FE) method and the material algorithms, in which the bone material properties and element axes are determined by both loading amplitudes and daily cycles with different weight factor. The simulation results showed that both algorithms produced realistic distribution in bone amount, when compared with the standard from CT data. Moreover, the simulated L-T ratios (the ratio of longitude modulus to transverse modulus) by the orthotropic algorithm were close to the reported results. This study suggests a role for "error-driven" algorithm in bone material prediction in abnormal mechanical environment and holds promise for optimizing implant design as well as developing countermeasures against bone loss due to weightlessness. Furthermore, the quantified methods used in this study can enhance bone remodelling model by optimizing model parameters to gap the discrepancy between the simulation and real data.