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Parkinson's disease (PD) is a common neurodegenerative disease in clinical practice. Patients with PD are usually accompanied by non - motor symptoms (NMS) such as gastrointestinal dysfunction, hyposmia, sleep disorders, depression, pain and hallucinations, which seriously reduce the quality of life. Recent studies have found that PD patients were accompanied by diffuse pathological changes in non-dopaminergic system such as progressive degeneration of 5-hydroxytryptamine (5-HT) ergic neurons, and 5-HT plays an important role in the regulation of emotion, cognitive function, pain and other neural functions, and is closely related to non-motor symptoms of PD. This article reviews the correlation between 5 -HT and non -motor symptoms of PD and its research progress, in order to further clarify the pathogenesis of non-motor symptoms of PD and provide a theoretical basis for the treatment of PD.

Abstract Purpose To investigate the effectiveness of problem-based learning (PBL) and case-based learning (CBL) teaching methods in clinical practical teaching in transarterial chemoembolization (TACE) treatment in China. Materials and methods A comprehensive search of PubMed, the Chinese National Knowledge Infrastructure (CNKI) database, the Weipu database and the Wanfang database up to June 2023 was performed to collect studies that evaluate the effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment in China. Statistical analysis was performed by R software (4.2.1) calling JAGS software (4.3.1) in a Bayesian framework using the Markov chain-Monte Carlo method for direct and indirect comparisons. The R packages “gemtc”, “rjags”, “openxlsx”, and “ggplot2” were used for statistical analysis and data output. Results Finally, 7 studies (five RCTs and two observational studies) were included in the meta-analysis. The combination of PBL and CBL showed more effectiveness in clinical thinking capacity, clinical practice capacity, knowledge understanding degree, literature reading ability, method satisfaction degree, learning efficiency, learning interest, practical skills examination scores and theoretical knowledge examination scores. Conclusions Network meta-analysis revealed that the application of PBL combined with the CBL teaching mode in the teaching of liver cancer intervention therapy significantly improves the teaching effect and significantly improves the theoretical and surgical operations, meeting the requirements of clinical education.

Leukocyte cell-derived chemotaxin-2 (LECT2) is an important cytokine synthesized by liver. Significant research interest is stimulated by its crucial involvement in inflammatory response, immune regulation, disease occurrence and development. However, bibliometric study on LECT2 is lacking. In order to comprehend the function and operation of LECT2 in human illnesses, we examined pertinent studies on LECT2 investigation in the Web of Science database, followed by utilizing CiteSpace, VOSview, and Scimago Graphica for assessing the yearly quantity of papers, countries/regions involved, establishments, authors, publications, citations, and key terms. Then we summarized the current research hotspots in this field. Our study found that the literature related to LECT2 has a fluctuating upward trend. “Angiogenesis”, “ALECT2”, “diagnosis”, and “biliary atresia” are the current investigative frontiers. Our findings indicated that liver diseases (e.g. liver fibrosis and hepatic cell carcinoma), systemic inflammatory disease, and amyloidosis are the current research focus of LECT2. The current LECT2 research outcomes are not exceptional. We hope to promote the scientific research of LECT2 and exploit its potential for clinical diagnosis and treatment of related diseases through a comprehensive bibliometric review.

With the advent of large-scale data, the demand for information is increasing, which makes signal sampling technology and digital processing methods particularly important. The utilization of 1-bit compressive sensing in sparse recovery has garnered significant attention due to its cost-effectiveness in hardware implementation and storage. In this paper, we first leverage the minimax concave penalty equipped with the least squares to recover a high-dimensional true signal x∈Rp with k-sparse from n-dimensional 1-bit measurements and discuss the regularization by combing the nonconvex sparsity-inducing penalties. Moreover, we give an analysis of the complexity of the method with minimax concave penalty in certain conditions and derive the general theory for the model equipped with the family of sparsity-inducing nonconvex functions. Then, our approach employs a data-driven Newton-type method with stagewise steps to solve the proposed method. Numerical experiments on the synthesized and real data verify the competitiveness of the proposed method.

Exploring urban spatial structures through spatial coupling analysis methods is an important method to provide theoretical support for the construction of sustainable urban structures. In order to make up for the neglect of POI species differences in previous studies, information entropy was introduced to calculate POI confusion, and a comprehensive POI index was constructed by combining kernel density and the entropy weight method; impervious surface coverage was extracted based on land cover data. The spatial distribution of the Beijing–Tianjin–Hebei urban agglomeration and some typical cities was analyzed by coupling two types of data using the dual-factor mapping method. The research indicates the following: (1). The spatial distribution of the two sets of data in the Beijing–Tianjin–Hebei region is highly consistent, indicating a state of high spatial coupling; Beijing has the highest proportion of coupling in the same region at the city level (73.39%). (2). The areas with different coupling of the two types of data are mainly distributed in the urban fringe areas transitioning from the city center to the suburbs, as well as in large-scale areas with single functionality such as airports, scenic spots, and ports. This study shows that analysis combining the POI comprehensive index and impervious surface coverage can effectively characterize urban spatial structure characteristics, providing a new perspective for the study of the spatial structure of the Beijing–Tianjin–Hebei urban agglomeration. It is of great significance for a deeper understanding of the laws of urban agglomeration spatial structures and guiding the coordinated development of the Beijing–Tianjin–Hebei urban agglomeration.

For large-scale optimization that covers a wide range of optimization problems encountered frequently in machine learning and deep neural networks, stochastic optimization has become one of the most used methods thanks to its low computational complexity. In machine learning and deep learning problems, nonconvex problems are common, while convex problems are rare. How to find the global minimum for nonconvex optimization and reduce the computational complexity are challenges. Inspired by the phenomenon that the stagewise stepsize tuning strategy can empirically improve the convergence speed in deep neural networks, we incorporate the stagewise stepsize tuning strategy into the iterative framework of Nesterov’s acceleration- and variance reduction-based methods to reduce the computational complexity, i.e., the stagewise stepsize tuning strategy is incorporated into randomized stochastic accelerated gradient and stochastic variance-reduced gradient. The proposed methods are theoretically derived to reduce the complexity of the nonconvex and convex problems and improve the convergence rate of the frameworks, which have the complexity O(1/μϵ) and O(1/μϵ), respectively, where μ is the PL modulus and L is the Lipschitz constant. In the end, numerical experiments on large benchmark datasets validate well the competitiveness of the proposed methods.