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Due to the uncertainty of trauma or infection, customized bone substitutes are often required in clinic. Meanwhile, excessive use of antibiotics may lead to drug resistance. Therefore, the design of anti-infection bone tissue engineering scaffold is of very important. In this study, porous titanium alloy bone tissue engineering scaffolds were designed and fabricated by 3D printing. TiO2 nanotubes were further constructed on the scaffolds through electrochemical anodic oxidation, achieving the drug loading and anti-infection functions. The micron-level bionic pores were fabricated by the 3D printing process, and the secondary nanoscale-level nanotubes were achieved through the anodic oxidation process. Thereafter, the micro–nano structured porous bone tissue engineering scaffolds are presented. This structure features that the drug release rate can be regulated by loading the anti-infection drug minocycline and coating them with poly(lactic-co-glycolic acid) (PLGA) in the nanotubes. According to the results, the micro–nano composite porous scaffold showed uniform and controllable micro–nano pores, it may load anti-infection drugs and shown anti-infection ability. In addition, the PLGA coating may delay the drug release and maintain a sustained anti-infection function for the scaffold in a week. This study provides new ideas for designing antibacterial bone tissue engineering scaffold.

Objective: Dentatorubral-pallidoluysian atrophy (DRPLA) is an inherited neurodegenerative disease caused by CAG overexpansion (≥48 tandem copies) in ATN1. The aim of this research was to explore the genetic cause of a large Chinese DRPLA pedigree and to review the characteristics of Chinese DRPLA patients., Methods: Suspected variants were screened by high-throughput sequencing. The number of CAG repeats was assessed by polymerase chain reaction using FAM-labeled primers followed by capillary electrophoresis. Literature on previously reported DRPLA cases with overexpanded ATN1 CAG repeats in China was reviewed., Results: After contracting a lung infection, the proband suffered early-onset DRPLA symptoms and novel phenotypes, transitioning from insomnia to stupor. The numbers of CAG repeats in the proband, her grandfather, father, mother, brother, and aunt were 8/81, 17/54, 10/57, 8/10, 10/10, and 10/17, respectively. Possible incomplete penetrance was observed in this pedigree., Conclusion: We described a large Chinese DRPLA pedigree in which the proband carried the largest CAG expansion reported in China. We also reviewed the characteristics of Chinese DRPLA patients and broadened the phenotypic spectrum., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Reasoning over temporal knowledge graphs (TKGs) is a challenging task that requires models to infer future events based on past facts. Currently, subgraph-based methods have become the state-of-the-art (SOTA) techniques for this task due to their superior capability to explore local information in knowledge graphs (KGs). However, while previous methods have been effective in capturing semantic patterns in TKG, they are hard to capture more complex topological patterns. In contrast, path-based methods can efficiently capture relation paths between nodes and obtain relation patterns based on the order of relation connections. But subgraphs can retain much more information than a single path. Motivated by this observation, we propose a new subgraph-based approach to capture complex relational patterns. The method constructs candidate-oriented relational graphs to capture the local structure of TKGs and introduces a variant of a graph neural network model to learn the graph structure information between query-candidate pairs. In particular, we first design a prior directed temporal edge sampling method, which is starting from the query node and generating multiple candidate-oriented relational graphs simultaneously. Next, we propose a recursive propagation architecture that can encode all relational graphs in the local structures in parallel. Additionally, we introduce a self-attention mechanism in the propagation architecture to capture the query's preference. Finally, we design a simple scoring function to calculate the candidate nodes' scores and generate the model's predictions. To validate our approach, we conduct extensive experiments on four benchmark datasets (ICEWS14, ICEWS18, ICEWS0515, and YAGO). Experiments on four benchmark datasets demonstrate that our proposed approach possesses stronger inference and faster convergence than the SOTA methods. In addition, our method provides a relational graph for each query-candidate pair, which offers interpretable evidence for TKG prediction results.