The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

OBJECTIVE: Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.
METHODS: This scoping review followed the five-stage framework of Arksey and O\'Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases.
RESULTS: Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies.
CONCLUSIONS: Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota\'s impact on human health.

BACKGROUND: The worldwide spread of monkeypox (mpox) has witnessed a significant increase, particularly in nonendemic countries.
OBJECTIVE: We aimed to investigate the changing clinical symptoms associated with mpox from 1970 to 2023 and explore their interrelations.
METHODS: In this systematic review and meta-analysis, 3 electronic databases were searched for English peer-reviewed studies conducted from January 1970 to April 2023 that reported any symptoms among confirmed mpox cases. We categorized the mpox epidemics into 3 periods: 1970-2002 (period 1, within the African region), 2003-2021(period 2, epidemics outside Africa), and 2022-2023 (period 3, worldwide outbreak). Following PRISMA guidelines, a meta-analysis was performed to estimate the pooled prevalence for each symptom. The correlation among symptoms was analyzed and visualized using network analysis.
RESULTS: The meta-analysis included 61 studies that reported 21 symptoms in 720 patients from period 1, 39 symptoms in 1756 patients from period 2, and 37 symptoms in 12,277 patients from period 3. The most common symptom among patients from all 3 periods was rash (period 1: 92.6%, 95% CI 78.2%-100%; period 2: 100%, 95% CI 99.9%-100%; and period 3: 94.8%, 95% CI 90.9%-98.8%), followed by lymphadenopathy (period 1: 59.8%, 95% CI 50.3%-69.2%; period 2: 74.1%, 95% CI 64.2%-84.1%; and period 3: 61.1%, 95% CI 54.2%-68.1%). Fever (99%, 95% CI 97%-100%), enlarged lymph nodes (80.5%, 95% CI 75.4%-85.0%), and headache (69.1%, 95% CI 4%-100%) were the main symptoms in period 1, with a significant decrease in period 3: 37.9%, 31.2%, and 28.7%, respectively. Chills/rigors (73.3%, 95% CI 60.9%-85.7%), fatigue (68.2%, 95% CI 51.6%-84.8%), and dysphagia/swallowing difficulty (61.2%, 95% CI 10.5%-100%) emerged as primary new symptoms in period 2 and decreased significantly in period 3. Most other symptoms remained unchanged or decreased in period 3 compared to the former 2 periods. Nausea/vomiting had the highest degree of correlation (with 13 symptoms) and was highly positively correlated with lymphadenopathy (r=0.908) and conjunctivitis (r=0.900) in period 2. In contrast, rash and headache were 2 symptoms with the highest degree of correlation (with 21 and 21 symptoms, respectively) in period 3 and were highly positively correlated with fever (r=0.918 and 0.789, respectively).
CONCLUSIONS: The manifestation of symptoms in patients with mpox has become more diverse, leading to an increase in their correlation. Although the prevalence of rash remains steady, other symptoms have decreased. It is necessary to surveil the evolving nature of mpox and the consequential changes in clinical characteristics. Epidemic countries may shift their focus on the potential association among symptoms and the high synergy risk.
BACKGROUND: PROSPERO Registration: CRD42023403282; http://tinyurl.com/yruuas5n.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

BACKGROUND: Antimicrobial resistance (AMR) is a multifactorial challenge driven by a complex interplay of proximal drivers, such as the overuse and misuse of antimicrobials and the high burden of infectious diseases, and distal factors, encompassing broader societal conditions such as poverty, inadequate sanitation, and healthcare system deficiencies. However, distinguishing between proximal and distal drivers remains a conceptual challenge.
OBJECTIVE: We conducted an umbrella review, aiming to systematically map current evidence about proximal and distal drivers of AMR and to investigate their relationships.
METHODS: Forty-seven reviews were analysed, and unique causal links were retained to construct a causality network of AMR. To distinguish between proximal and distal drivers, we calculated a \'driver distalness index (Di)\', defined as an average relative position of a driver in its causal pathways to AMR.
RESULTS: The primary emphasis of the literature remained on proximal drivers, with fragmented existing evidence about distal drivers. The network analysis showed that proximal drivers of AMR are associated with risks of resistance transmission (Di = 0.49, SD = 0.14) and antibiotic use (Di = 0.58, SD = 0.2), which are worsened by intermediate drivers linked with challenges of antibiotic discovery (Di = 0.62, SD = 0.07), infection prevention (Di = 0.67, SD = 0.14) and surveillance (Di = 0.69, SD = 0.16). Distal drivers, such as living conditions, access to sanitation infrastructure, population growth and urbanisation, and gaps in policy implementation were development and governance challenges, acting as deep leverage points in the system in addressing AMR.
CONCLUSIONS: Comprehensive AMR strategies aiming to address multiple chronic AMR challenges must take advantage of opportunities for upstream interventions that specifically address distal drivers.

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

UNASSIGNED: With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.
UNASSIGNED: The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.
UNASSIGNED: Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.
UNASSIGNED: We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.

Spatial heterogeneity is a fundamental research topic in the field of geography, and Geodetector is a widely used tool for studying this phenomenon. To understand the research advancements and knowledge diffusion trends surrounding Geodetector, we constructed an author evolutionary tree structure fusing its 847 core citations in the Web of Science database and Geotree model for the first time. The results of our literature statistics indicated that Geodetector has garnered the attention of 3123 authors from 48 countries since its publication in the Ecological Indicators journal in 2010, who have published core papers concerning ten important topics. The majority of these studies focused on spatial heterogeneity and its influencing factors. Our analysis of Geotree data revealed a significant correlation between the publication rate of scholars in large teams and their academic activities. Our analysis of the knowledge diffusion chain shown that only 2% of the total number of authors have contributed to over 20% of the scientific collaborations and knowledge diffusion, and they were recognized as experts in Geodetector research. To provide a comprehensive reference for future scholars, we have summarized the citing countries, five classical articles, main scientific domains, and core teams of Geodetector research.

This study analyzes the most relevant authors, sources, cooccurrence of keywords, thematic map, and trend topics of the most recent and most cited research papers on semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA). Also, the content of the 25 most cited papers is summarized. A total of 2995 results appeared in an online electronic search performed on 14 August 2023 in the Scopus database using the term semaglutide. The most recently published 500 articles and most cited 200 documents were selected for bibliometric analysis. Network analysis visualization was conducted with the help of the VOSviewer software (version 1.6.18) (Centre for Science and Technology Studies, Leiden, the Netherlands) and Biblioshiny (it is a shiny application providing a web interface for bibliometrix) (Department of Economics and Statistics, University of Naples Federico II, Naples, Italy). After excluding duplicates and editorials, the data analysis found that 495 most recent documents were published in 279 journals by 2461 authors, and 200 most cited papers were published in 103 sources by 1241 authors. There is an increasing trend in the number of research papers from 2014 to 2022, with a peak in 2022. The most relevant authors in the most recent semaglutide research papers are Chen and Zhang. The pertinent authors of the most cited research papers on semaglutide are Lingvay and Khunti. The most common keywords used in the most recent and most cited research papers are semaglutide, obesity, diabetes mellitus type 2, glucagon-like peptide-1, glucagon-like peptide-1 receptor agonist, antidiabetic agent, liraglutide, and cardiovascular disease (CVD). The most relevant source is \"Diabetes, Obesity and Metabolism\" for the research papers on semaglutide. Trend topic analysis suggests that most of the research between 2020 and 2022 on semaglutide was done on non-insulin-dependent diabetes mellitus. The most cited papers provide essential insights into using semaglutide in managing type 2 diabetes mellitus (T2DM), obesity, and related conditions, along with their potential benefits, side effects, and possible mechanisms of action. This analysis highlights that the pharmacological effects of semaglutide extend beyond its role as a glycemic regulator.