Flow regime specific regulation shapes microbial-mediated nitrogen cycling of plain tidal river network.

Integrated metabolomics and proteomics analysis elucidated the therapeutic effect of Huangkui Capsule on tacrolimus-induced chronic nephrotoxicity in rats.

Sishen Wan suppresses colon cancer through dual inhibition of PI3K/AKT/mTOR and STAT3-mitophagy pathways: Network pharmacology and experimental validation.

Danhong injection alleviates sepsis-induced acute lung injury by regulating AGE/RAGE/AKT pathway.

ABSTRACT The concept of the Twin Transition (TT) – the integration of green and digital transitions – has evolved from a loosely linked policy idea – especially in the EU’s post-COVID-19 recovery agenda – to an emerging topic in academic research. TT research has grown rapidly, focusing on the enabling role of digital technologies in addressing climate challenges and on the environmental impacts of digitalization. Recently, academic interest has turned toward an underexplored aspect: TT’s regional geography. Emerging evidence indicates that the development and adoption of green and digital technologies are shaped by regional characteristics, including local capabilities, institutional settings, and path dependencies. However, systematic understanding of these geographical dimensions remains limited. This paper addresses that gap through a bibliometric analysis of TT-related research with spatial relevance. Using network analysis and bibliographic coupling, the study identifies key research trends, clusters, and gaps. Findings show a fragmented but fast-growing field, with both qualitative and quantitative approaches offering insights across spatial scales. Patterns of scientific output suggest a ‘success-breeds-success’ dynamic, particularly in ICT-strong regions. The paper underscores the importance of place-sensitive policies and localized knowledge in shaping the TT and offers a roadmap for future research into how regional factors mediate this dual transformation.

Adverse childhood experiences and loneliness trajectory in middle-aged and older adults: A network analysis approach.

Recent approaches in psychosis research have emphasized the importance of the combined analysis of risk and resilience factors. A resilience-integrated approach offers a more comprehensive understanding of the mechanisms underlying symptom associations and progression in individuals at clinical high-risk (CHR) for psychosis. Network analysis allows the examination of relationships among mental health conditions together with both risk and resilience factors. To our knowledge, this is the first study that investigated associations between resilience, risk factors, and psychosis-risk using network analysis. This secondary analysis of data from the ZInEP study (Zurich Early Recognition Program) examined 116 individuals at CHR for psychosis aged 18-35years. We investigated the relationships between two candidate resilience factors (self-efficacy and self-esteem), daily hassles as a candidate risk factor, functioning level, and psychosis risk symptoms using network analysis. Self-esteem and self-efficacy were negatively associated with general symptoms, negative symptoms, and daily hassles. Resilience factors and daily hassles were not associated with positive and disorganized symptoms, nor with functioning level. Centrality analyses further revealed that self-esteem had stronger node strength and bridge strength than self-efficacy. This study emphasizes the utility of a resilience-oriented approach in psychosis risk. Self-esteem and self-efficacy may play a crucial role in negative and general symptoms. Resilience factors may enrich our theoretical understanding of psychosis risk and help pave the way for resilience-based interventions.

The associations between sleep problems and physical exercise among Chinese university students: A network analysis of gender differences.

Unique associations between different motivations to play video games and anxiety: Evidence from network analysis.

Core insomnia symptoms associated with cognitive flexibility in insomnia disorder: A network analysis.

Anxiety and depression are highly comorbid mental health disorders with heterogeneous symptom patterns and poorly understood transdiagnostic mechanisms. This study aims to characterize latent subgroups, risk factors, and symptom-level interactions underlying depression-anxiety comorbidity across adolescents and adults in multi-ethnic Southwest China. The study included a total of 41,394 adolescents (aged 9-19) and 17,345 adults (aged 18-80). Adolescents were recruited using multistage stratified cluster sampling, whereas adults were recruited by convenience sampling. All participants completed a self-designed sociodemographic questionnaire, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Latent profile analysis identified subgroups, logistic regression analyzed risk/protective factors, and network analysis mapped symptom interactions and bridge nodes. This study found that three adolescent profiles emerged: high (11.66%), moderate (31.95%), and low/no depression-anxiety (56.39%). Adults were classified into low/no comorbidity (90.63%) and comorbid depression-anxiety (9.37%). Risk factors for adolescents included female gender (OR=2.77, 95%CI: 2.55-3.00; OR=1.59, 95%CI: 1.52-1.67), higher grade levels (OR=3.45, 95%CI: 3.10-3.84; OR=3.56, 95%CI: 3.33-3.80), smoking (OR=1.72, 95%CI: 1.51-1.96; OR=1.28, 95%CI: 1.17-1.41),drinking (OR=2.45, 95%CI: 2.23-2.70; OR=1.66, 95%CI: 1.55-1.77), family instability (OR=1.16, 95%CI: 1.02-1.31; OR=1.33, 95%CI: 1.14-1.56) and "other" ethnic minority (OR=1.15, 95%CI: 1.04-1.26). For adults, female gender(OR=1.68; 95%CI: 1.44-1.97), living alone(OR=1.37; 95%CI: 1.14-1.65), poor self-rated health (OR=0.13, 95%CI: 0.11-0.15), and Dai ethnicity (OR=0.70, 95%CI: 0.49-0.96) predicted comorbidity. Network analysis revealed distinct bridge symptoms: adolescents in the high depression-anxiety group had five symptoms: depressed or sad mood (phq2), psychomotor agitation/retardation (phq8), nervousness or anxiety (gad1), restlessness (gad5), and irritable (gad6); however, adults with comorbidity had one symptom: afraid something will happen (gad7). This study identified three patterns of depression-anxiety comorbidity in adolescents and two in adults. Efforts should prioritize adolescents from "other" ethnic minorities, strengthening family and peer support, as well as smoking and drinking interventions for adolescents, and addressing social isolation, physical health, and catastrophizing cognition in adults may mitigate the comorbidity burden.

Mapping the interconnections among non-suicidal self-injury, depressive symptoms, and reward sensitivity: A network analysis.

Hippocampus in the symptom architecture of schizophrenia spectrum disorders: A network analysis approach.

Traditional assessments of depressive symptoms often rely on retrospective self-reports, which may be affected by cognitive and memory biases. Few studies have compared retrospective and dynamic (real-time) assessments to examine the consistency and structure of depressive symptom reporting. This study aimed to compare retrospective and dynamic assessments of depressive symptoms in youth using network analysis to explore symptom-level associations and clustering. Ninety Brazilian adolescents and young adults (mean age=18years), with and without depression, completed the Short Mood and Feelings Questionnaire (SMFQ) every other day for 14days via a smartphone-based chatbot (dynamic assessment). At the end of the 2-week period, they completed the same questionnaire retrospectively. Network analyses were conducted using Exploratory Graph Analysis (EGA) and Dynamic Exploratory Analysis (DEA) to identify symptom communities and compare network structures across both assessment methods. Both retrospective and dynamic assessments revealed three symptom communities; however, the composition and structure of these communities differed. Retrospective assessments showed stronger connections among cognitive symptoms, while dynamic assessments displayed a more balanced distribution, with stronger associations between somatic and affective symptoms. Findings highlight significant differences in depressive symptom networks between retrospective and dynamic assessments. Dynamic methods may offer less biased and more ecologically valid insights into youth depression, underscoring the importance of real-time data collection in clinical assessment and research.

Interrelationships of family health with depression and self-efficacy among Chinese adults: A latent profile analysis and network analysis.

Adverse childhood experiences have been identified as important risk factors for addictive behaviors, particularly when cumulatively occurring. The present cross-sectional study aimed to investigate the differences and interrelationships of addictive behaviors (both substance and behavior-related, such as gaming and overeating) according to adverse childhood experiences. A total of 802 participants recruited from the Italian general population completed the brief Screener for Substance and Behavioral Addiction and Childhood Traumatic Events Scale. Based on reported adverse childhood experiences, participants were divided into three groups: no adverse experience, single adverse experience, and multiple adverse experiences. The interrelationships among addictive behaviors in the groups were assessed using a network analysis approach. The multiple adverse experiences group reported significantly higher levels of addictive behaviors related to tobacco, overeating, and sex. Network analysis showed that in the multiple adverse experiences group, addictive behaviors were more interrelated, displaying a greater number of associations among each other than in the no adverse experience and single adverse experience groups. Particularly, in the multiple adverse experiences group, substance-related addictive behaviors (alcohol, tobacco, cannabis, cocaine) showed strong interrelationships, suggesting a pattern of polysubstance use. Moreover, in the multiple adverse experiences group, overeating showed associations with the other types of addictive behaviors. Lastly, in each group, compulsive sex was associated with most of the other types of addictive behaviors, and, specifically, in the multiple adverse experiences group, it showed connections with shopping and overworking, while in the no adverse experience group, with cannabis, videogaming, and overeating. The main findings of the study showed that individuals with cumulative adverse childhood experiences not only reported higher severity of single addictive behaviors, but also greater interrelationships among each other, highlighting in these individuals a potential pattern of mutual reinforcement and links between several addictive behaviors.

Network of job demands-resources and depressive symptoms in clinical nursing interns: A cross-sectional study.

Research has underscored the importance of parental responses to children's negative emotions (PRCNE) in adolescents' socioemotional development. However, many existing studies relying on regression or structural equation modelling tend to examine different types of PRCNE separately, rather than modelling their interrelations as parts of an interconnected system. To address this, this study utilised the network approach to examine the roles of various types of PRCNE in Chinese adolescents' socioemotional development with data from 477 Chinese adolescents (Mage = 13.62 years, 50.73% male). Analysis of PRNCE networks revealed that both maternal and paternal networks exhibited connections among different types of responses. In both maternal and paternal networks, punitive responses had the highest centrality. Analysis of bridge networks connecting PRCEN and adolescent emotional/social outcomes showed that parental responses related to adolescents' emotional and behavioural development differed based on the parent's gender. Bridge analysis showed both paternal and maternal person-minimisation were most central for emotional outcomes, while paternal problem-focussed and maternal emotion-focussed responses were key for adolescent social outcomes. In conclusion, this study revealed gender-specific patterns in how various parental responses are interconnected and associated with adolescents' emotional and social outcomes, underscoring the importance of culturally informed, parent-specific approaches in research and practice.

To further investigate the heterogeneity of adolescents' social anxiety, a cross-sectional survey was conducted among 6540 Chinese adolescents aged 11 to 19 years (3294 boys). Latent profile analysis and network analysis were adopted to identify subgroups based on social anxiety symptoms and further reveal core features of each group. Results showed that four subgroups were identified: "Profile 1 (n = 1,768, 26.8%): the low group with diffuse social anxiety"; "Profile 2 (n = 1,202, 18.7%): the moderate group with cognitive disturbance"; "Profile 3 (n = 1995, 30.6%): the moderate group with difficulties in new situations"; and "Profile 4: high group with diffuse social anxiety". Multiple logistic regression analyses showed significant age and gender differences across the four profiles. Network analyses revealed that, with regard to standardized strength indices of SAS-A symptoms, generally, symptoms F4 "worry that others don't like me", and F6 "feel that others make fun of me" emerged as central symptoms across the four networks. Network 4 showed a higher average predictability of all nodes, indicating a greater resistance to intervention. Network comparison tests indicated that there were significant differences regarding network structures in all pairs of networks except for Network 1 versus 2. All pairs of networks differed significantly in network strengths except for Network 2 versus 3, particularly revealing the "essence" and "manifestation" of adolescents' social anxiety. The findings help understand adolescents' social anxiety symptoms and the interactions of symptoms, potentially providing novel perspectives and approaches for the prevention and treatment of adolescents' social anxiety.

Integrated transcriptomics and miRNA-mRNA network analysis reveals Kisspeptin-10 mediated regulation of EMT and apoptosis in glioblastoma.