Comparing the outcome between multicentric/multifocal breast cancer and unifocal breast cancer: A systematic review and meta-analysis.

Objective: This systematic review and meta-analysis compares the outcome between MMBC and unifocal breast cancer (UFBC), in order to provide a theoretical basis for the design of an appropriate clinical therapeutic strategy of MMBC patients. Methods: PubMed, Embase, The Cochrane Library, Web of science, CNKI, WanFang Data, CBM and VIP database were searched from inception to July 2021, and observational studies reporting the outcome of patients with MMBC and UFBC were included. We extracted or calculated the mortality rates of MMBC and UFBC patients; and obtained the hazard ratios; odds ratios; relative risks; and the corresponding 95% confidence intervals from the eligible studies. All the meta-analyses were conducted by using the Stata 15.0 software. Results: 31 eligible studies comprising a total of 15,703 individuals were included. The meta-analysis revealed that MMBC did not have a significant association with poor overall survival (HR=1.04, 95% CI=0.96-1.12), disease-free survival (HR= 1.07, 95% CI= 0.84-1.36), breast cancer-specific survival (HR=1.42, 95% CI= 0.89-2.27), recurrence-free survival (HR= 0.878, 95% CI= 0.652-1.182), local recurrence-free survival (HR= 0.90, 95% CI= 0.57-1.42), and contralateral breast cancer risk (RR= 0.908, 95% CI= 0.667-1.234). However, MMBC appeared to have a correlation with a slightly higher risk of death (OR=1.31, 95% CI=1.18-1.45). Conclusion: Patients with MMBC appeared to have a higher risk of death, however, it may not be independently associated with poorer outcomes. Considering the inter-study heterogeneity and other limitations, our results need to be validated by further multicenter prospective studies with a large sample size in the future.

A Weighted Symmetric Graph Embedding Approach for Link Prediction in Undirected Graphs.

Link prediction is an important task in social network analysis and mining because of its various applications. A large number of link prediction methods have been proposed. Among them, the deep learning-based embedding methods exhibit excellent performance, which encodes each node and edge as an embedding vector, enabling easy integration with traditional machine learning algorithms. However, there still remain some unsolved problems for this kind of methods, especially in the steps of node embedding and edge embedding. First, they either share exactly the same weight among all neighbors or assign a completely different weight to each node to obtain the node embedding. Second, they can hardly keep the symmetry of edge embeddings obtained from node representations by direct concatenation or other binary operations such as averaging and Hadamard product. In order to solve these problems, we propose a weighted symmetric graph embedding approach for link prediction. In node embedding, the proposed approach aggregates neighbors in different orders with different aggregating weights. In edge embedding, the proposed approach bidirectionally concatenates node pairs both forwardly and backwardly to guarantee the symmetry of edge representations while preserving local structural information. The experimental results show that our proposed approach can better predict network links, outperforming the state-of-the-art methods. The appropriate aggregating weight assignment and the bidirectional concatenation enable us to learn more accurate and symmetric edge representations for link prediction.

Activation of the TLR2-mediated downstream signaling pathways NF-κB and MAPK is responsible for B7-H3-augmented inflammatory response during S. pneumoniae infection.

It has been reported that B7-H3, a costimulatory protein, participates in the development and progression of experimental pneumococcal meningitis by amplifying the TLR2-mediated inflammatory response. This study attempted to clarify the pathway(s) of TLR2 signaling involved in B7-H3-augmented inflammatory response during S. pneumoniae infection. Murine microglial cell line N9 cells and primary murine microglial cells were infected with S. pneumoniae alone or in combination with B7-H3. Although B7-H3 stimulation failed to further enhance S. pneumoniae-upregulated mRNA and protein expression of TLR2, it strongly augmented S. pneumoniae-induced phosphorylation of NF-κB p65, MAPK p38, and ERK1/2 in both N9 cells and primary microglial cells. Notably, B7-H3 itself did not activate NF-κB p65, MAPK p38, and ERK1/2. Furthermore, deactivation of NF-κB p65, MAPK p38, and ERK1/2 with their specific inhibitors significantly attenuated B7-H3-amplified proinflammatory cytokine and chemokine release from S. pneumoniae-infected microglial cells. Importantly, blockage of NF-κB p65, MAPK p38, or ERK1/2 in vivo substantially diminished B7-H3-augmented TNF-α levels in the brain of S. pneumoniae-infected mice. These results indicate that the activation of both NF-κB and MAPKs is predominantly responsible for B7-H3-augmented inflammatory response during S. pneumoniae infection.

Study of the interaction between bovine serum albumin and analogs of Biphenyldicarboxylate by spectrofluorimetry.

This work was designed to study the interaction between bovine serum albumin (BSA) and Biphenyldicarboxylate (DDB) or analogs (I, II and III) of DDB by UV-visible and fluorescence spectroscopic methods for the first time. Results showed that both DDB and analogs had a strong ability to quench the intrinsic fluorescence of BSA through a static quenching procedure. The binding constants (K) were calculated according to the relevant fluorescence data. The thermodynamic parameters (ΔH, ΔS and ΔG) showed that the hydrophobic force played a major role in the binding interaction between BSA and DDB or analogs. Synchronous fluorescence spectra of BSA were investigated in the presence of DDB or analogs. It was showed that DDB was the strongest quencher and bound to BSA with the highest affinity among four compounds. The influence of molecular structure on the binding aspects was reported.