Network analysis of smoking-related sleep characteristics in Chinese adults

The associations between multiple sleep characteristics and smoking behavior are inconsistent, and it is unclear which sleep characteristics are most crucial for tobacco prevention. This study aimed to explore the associations between smoking status/intensity and multiple sleep characteristics and to identify the potential core domain of smoking-related sleep using network analysis. Data were obtained from a survey of cancer-related risk factors among Chinese adults. Logistic regression models were used to quantify the associations between sleep characteristics and smoking status/intensity. Network analyses were employed to identify the core sleep characteristics. A total of 5,228 participants with a median age of 44 years old were included in the study. Current smoking was significantly positively associated with long nap time, difficulty falling asleep, late bedtime, getting up after 7 am, and waking up earlier than expected. There was significant positive association between current smoking and short sleep duration in young adults under 45 years old. Late bedtime and getting up after 7 am were only associated with current heavy smoking, but not current light smoking. Network analyses showed that multiple smoking-related sleep characteristics were interconnected, with difficulty falling asleep and late bedtime as central characteristics in the network. The study found that the associations between sleep characteristics and smoking varied by age and smoking intensity and highlights the potential benefits of sleep health promotion in smoking cessation, with a particular focus on difficulty falling asleep and late bedtime.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

Exploring research on ecotechnology through artificial intelligence and bibliometric maps

Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.

Anxious and depressive symptoms and health-related quality of life in a cohort of people who recently attempted suicide: A network analysis

BACKGROUND

Suicide is an international health concern with immeasurable impact from the perspective of human and social suffering. Prior suicide attempts, anxious and depressive symptoms, and relatively lower health-related quality of life (HRQoL) are among the most replicated risk factors for suicide. Our goal was to visualize the distribution of these features and their interconnections with use of a network analysis approach in individuals who recently attempted suicide.

METHODS

Individuals with a recent suicide attempt were recruited from nine University Hospitals across Spain as part of the SURVIVE cohort study. Anxious and depressive symptoms, and perceived HRQoL were included in the network analysis. Network structures were estimated with the EBICglasso model. Centrality measures and bridge symptoms connecting communities were explored. Subnetworks comparing younger and older individuals, and women and men were analyzed.

RESULTS

A total of 1106 individuals with a recent suicide attempt were included. Depressed mood was the symptom with the greatest influence in the overall network, followed by anxiety symptoms such as feeling nervous, worrying, restless, and having difficulties to relax. Perceived general health was associated with increased suicidal ideation in the whole sample. Older people showed a specific connection between perceived general health and depressed mood.

LIMITATIONS

The cross-sectional design does not allow determination of established causality.

CONCLUSIONS

Depressed mood was the core network's symptom and, therefore, an important target in the management and prevention of suicide. HRQoL had more influence on the network of older populations, in which it should be a primary focus.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

BACKGROUND

Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression.

METHODS

Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test.

RESULTS

Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients.

CONCLUSION

The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study

BACKGROUND

Previous research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated.

METHODS

A total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety.

RESULTS

Four distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances.

CONCLUSION

To the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

Gaze-based attention network analysis in a virtual reality classroom

This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and interconnectedness of the networks were calculated to describe the network structure. The measures, derived from graph theory, allowed for statistical inference testing and the interpretation of participants' visual attention in 3D VR environments. Our method provides useful insights when analyzing students' learning in a VR classroom, as reported in a corresponding evaluation article with N = 274 participants. •Guidelines on implementing gaze-ray casting in VR using the Unreal Engine and the HTC VIVE Pro Eye.•Creating gaze-based attention networks and analyzing their network structure.•Implementation tutorials and the Open Source software code are provided via OSF: https://osf.io/pxjrc/?view_only=1b6da45eb93e4f9eb7a138697b941198.

Network structure of complex interactions of premenstrual syndrome and influencing factors in young adult women

BACKGROUND

In the transition phase from adolescence to adulthood, premenstrual syndrome (PMS) occurs more commonly, with a variety of symptoms. The occurrence of PMS may be the result of a combination of demographic, physiological, psychological and sociological factors. This study aimed to identify the central symptoms of PMS, and explored the complex influencing factors especially the one-to-one inter-relationships factors with specific symptoms.

METHOD

This is a cross-sectional study conducted in mainland China. 3458 young adult women were assessed. Using the Premenstrual Syndrome Scale (PSS) to assess the PMS, and PSS score was over 6 divided into PMS group, and vice versa. Influencing factors were assessed by a set of self-reported questionnaire. Network analysis was used to examined the interplay of PMS, whilst also considering the influencing factors of PMS.

RESULTS

In summary, 1479 participants were in PMS group. Anxiety had the highest strength centrality (1.12/1.09), shown higher centrality in the both network. Swelling of the hands or feet also shown higher strength centrality (0.89) in PMS group. PMS is associated with a higher history of dysmenorrhea, and neurotic personality. Neurotic personality - depressed mood/nervousness (0.27/0.23), history of dysmenorrhea - abdominal distension (0.21), had significantly higher weight than other edges in PMS group.

CONCLUSION

Anxiety was the most central symptom in the network, and was closely associated with other symptoms like depressed mood, which provided additional evidence for the centrality of emotional features in PMS. Moreover, the influencing factors of PMS combined demographic, physiological, psychological, and sociological factors. According to the central symptoms and factors affecting the specific PMS symptoms in young adult women, targeted intervention is helpful to prevent and alleviate PMS.

LIMITATION

Cross-sectional design cannot infer the directionality of the associations between variables. All data is self-reported with recall bias and the edge weights across the constructs of influencing factors and PMS were fairly small.

Collective activities of the Thai Coordinating Unit for One Health (CUOH): Past activities and future directions

In Thailand, One Health concepts have been implemented among government agencies, academic institutions, intergovernment, and civil society organizations. The Thai Coordinating Unit for One Health (CUOH) was established as a collaborating body for One Health-related activities in the country in 2014. To better understand what activities CUOH has completed thus far and to assess future activities, we conducted a network analysis to identify and visualize linkages between organizations and activities from 2015 to 2021. Activities were divided into four categories: organizing meetings, developing products, providing funds, and managing resources. Most of the 114 CUOH-managed meeting participants were representatives from 72 government and 20 academic institutions. The Thai Ministry of Public Health's Department of Disease Control participated in 148 meetings, the highest attendance among all organizations working with CUOH. The first CUOH guideline or manual was published in 2020, and 11 were published in 2021. In funding management, the CUOH worked with 25 organizations to carry out 71 projects from 2015 to 2021. Additionally, the CUOH played an important role in allocating COVID-19 vaccines during the COVID-19 pandemic. The CUOH has connected organizations working in different health sectors to collaborate jointly through meetings and projects that use a One Health approach, which can holistically improve health management in Thailand. Diverse funding sources are needed to ensure the sustainability of the unit in the future.

Interpersonal relationships and suicidal ideation among Chinese youths: A network analysis

BACKGROUND

Suicidal ideation is a noteworthy health problem that requires further study in the field of interpersonal relationships. The main scope of relationships include family, teachers, peers, and the Internet. However, few studies have considered the effects of interpersonal interactions. Based on the Interpersonal Theory of Suicide, this study explores the relation between different interpersonal relationships and suicidal ideation.

METHODS

A network analysis approach was used to test the relationships. Cross-sectional data (N = 1694; 52.1 % boys; Mage = 11.86 years, SD = 1.59) were collected from primary and secondary students in October-November 2022. Participants completed a survey including demographic part and questionnaires: Positive and Negative Suicide Ideation Inventory (PANSI), Interpersonal Needs Questionnaire (INQ), Family Adaptability and Cohesion Evaluation Scale (FACES II), Inventory of Parent and Peer Attachment (IPPA), Student-Teacher Relationship Scale (STRS) and Facebook Intensity Scale (FIS).

RESULTS

All variables showed a significant correlation in this inferred network. Family, teacher-student, and peer relationships are protective against suicidal ideation. The closeness of the teacher-student relationship showed the highest strength centrality. However, online interpersonal relationship is a risk factor for suicide. Thwarted belongingness and perceived burdensomeness are proximal factors of suicidal ideation that influenced all interpersonal relationships.

LIMITATIONS

The network analysis as a data-driven and exploratory method may be limited to provide exact cause and effect relationship.

CONCLUSIONS

This study shows that teacher-student connections may be more important than other relationships of young people. Improper peer and online interpersonal relationship could be risky for suicide. Further studies are needed to examine the role of these relationships elaborately.

Inter-relationships between suicidality and depressive symptoms among children and adolescents experiencing crisis: A network perspective

OBJECTIVES

Suicidality and depressive symptoms have emerged as major mental health issues reported among children and adolescents experiencing crises. In light of these concerns, this study was conducted to elucidate interrelationships between depressive symptoms and suicidality at an item level within this population during the COVID-19 pandemic, a worldwide public health crisis affecting children and adolescents.

METHOD

A cross-sectional study design was used. Primary and secondary school students completed the Children's Depression Inventory - Short Version (CDI-S) and two standard suicidality questions tapping suicidal ideation and suicide plans, respectively. A network analysis was performed to examine inter-connections between depressive symptoms and suicidality.

RESULTS

A total of 5380 students participated in the study. Prevalence of suicidal ideation and suicide plans were 12.8 % (95 % CI = 11.9 %-13.7 %) and 9.9 % (95 % CI = 9.2 %-10.8 %), respectively; the prevalence of depressive symptoms was 41.2 % (95%CI = 39.8 %-42.5 %). The network analysis identified CDI4 (self-hatred) as the most influential node with the highest centrality, followed by CDI8 (loneliness), CDI5 (crying), and CDI1 (sadness). Additionally, CDI5 (crying), CDI1 (sadness), CDI4 (self-hatred), and CDI10 (feeling unloved) were the most meaningful nodes linking depressive symptoms with suicidality.

CONCLUSIONS

Critical depressive symptoms linked with suicidality among children and adolescents living through the COVID-19 pandemic included self-hatred, loneliness, crying, and sadness. Interventions that target these depressive symptoms may have increased utility in reducing the risk of suicidality within this population.

A network analysis of mechanisms of change during exposures over the course of intensive OCD treatment

Exposure and response prevention (ERP) is an evidence-based treatment for obsessive-compulsive disorder (OCD). Theories for how it works vary in their emphasis on active mechanisms of change. The current study aimed to clarify mechanisms of change in ERP for OCD using network analysis, comparing ERP networks at the start and end of intensive treatment (partial hospital and residential). In our sample of 182 patients, the most central node in both networks was engagement with exposure, which was consistently related to greater understanding of ERP rationale, higher willingness, and less ritualization, accounting for all other variables in the network. There were no significant differences in networks between the start and end of treatment. These results suggest that nonspecific parameters like facilitating engagement in exposures without ritualizing and providing a clear rationale to clients may be key to effective treatment. As such, it may be useful for clinicians to spend adequate time underscoring the need to eliminate rituals to fully engage in exposure tasks and explaining the rationale for ERP prior to doing exposures, regardless of theoretical orientation. Nonetheless, findings represent group-level statistics and more fine-grained idiographic analyses may reveal individual-level differences with respect to central mechanisms of change. Other limitations include demographic homogeneity of our sample.

Intrarelationships between suboptimal health status and anxiety symptoms: A network analysis

BACKGROUND

Suboptimal health status is a global public health concern of worldwide academic interest, which is an intermediate health status between health and illness. The purpose of the survey is to investigate the relationship between anxiety statuses and suboptimal health status and to identify the central symptoms and bridge symptoms.

METHODS

This study recruited 26,010 participants aged <60 from a cross-sectional study in China in 2022. General Anxiety Disorder-7 (GAD-7) and suboptimal health status short form (SHSQ-9) were used to quantify the levels of anxiety and suboptimal health symptoms, respectively. The network analysis method by the R program was used to judge the central and bridge symptoms. The Network Comparison Test (NCT) was used to investigate the network differences by gender, place of residence, and age in the population.

RESULTS

In this survey, the prevalence of anxiety symptoms, SHS, and comorbidities was 50.7 %, 54.8 %, and 38.5 %, respectively. "Decreased responsiveness", "Shortness of breath", "Uncontrollable worry" were the nodes with the highest expected influence. "Irritable", "Exhausted" were the two symptom nodes with the highest expected bridge influence in the network. There were significant differences in network structure among different subgroup networks.

LIMITATIONS

Unable to study the causal relationship and dynamic changes among variables. Anxiety and sub-health were self-rated and may be limited by memory bias.

CONCLUSIONS

Interventions targeting central symptoms and bridge nodes may be expected to improve suboptimal health status and anxiety in Chinese residents. Researchers can build symptom networks for different populations to capture symptom relationships.

How do men and women differ in their depressive symptomatology? A gendered network analysis of depressive symptoms in a French population-based cohort

BACKGROUND

The experience of depressive manifestations and the presentation of symptoms in clinical settings may differ in men and women. Despite the extensive literature, it remains unclear how depressive manifestations interact at symptom levels in men and women. First, we aimed to describe and compare depressive networks by sex. Second, we examined symptom connections to Clinical depression and Functional Limitations as a proxy of self-recognition of a depressive episode.

METHODS

We estimated networks from the 20 CES-D items in men and women from a large population-based French cohort. We computed centrality measures and ran comparisons. Then, we re-estimated two networks in men and women separately, adding, on the one hand, Clinical Depression and, on the other hand, Limitations due to a depressive episode.

RESULTS

Over 200,000 participants were included in this study. Women were twice as likely to have a previous diagnosis of depression. Sex-ratio was less pronounced (1,7:1) for Limitations due to depression. Centrality measures revealed similar symptom patterns. However, network structures differed between men and women. We found some symptom connections to Clinical depression and Limitations to be non-invariant according to sex.

LIMITATIONS

Cross-sectional data does not capture the direction of the connections between symptoms and an eventual diagnosis. We lacked data about the diagnosis's context and could not account for other factors influencing depressive symptomatology.

CONCLUSIONS

Network structures differed, suggesting gender-specific mechanisms in activating symptoms and depressive states. Addressing central symptoms evoking depressed moods with tailored interventions may serve to tackle depressive states in men and women.

Plant growth-promoting bacteria improve the Cd phytoremediation efficiency of soils contaminated with PE-Cd complex pollution by influencing the rhizosphere microbiome of sorghum

Composite pollution by microplastics and heavy metals poses a potential threat to the soilplant system and has received increasing attention. Plant growth-promoting bacteria (PGPB) have good application potential for the remediation of combined microplastic and heavy metal pollution, but few related studies exist. The present study employed a pot experiment to investigate the effects of inoculation with the PGPB Bacillus sp. SL-413 and Enterobacter sp. VY-1 on sorghum growth and Cd accumulation under conditions of combined cadmium (Cd) and polyethylene (PE) pollution. Cd+PE composite contamination led to a significant reduction in sorghum length and biomass due to increased toxicity. Inoculation with Bacillus sp. SL-413 and Enterobacter sp. VY-1 alleviated the stress caused by Cd+PE complex pollution, and the dry weight of sorghum increased by 25.7% to 46.1% aboveground and by 12.3% to 45.3% belowground. Bacillus sp. SL-413 and Enterobacter sp. VY-1 inoculation increased the Cd content and accumulation in sorghum and improved the phytoremediation efficiency of Cd. The inoculation treatment effectively alleviated the nutrient stress caused by the reduction in soil mineral nutrients due to Cd+PE composite pollution. The composition of the soil bacterial communities was also affected by the Cd, Cd+PE and bacterial inoculation treatments, which affected the diversity of the soil bacterial communities. Network analyses indicated that bacterial inoculation regulated the interaction of rhizospheric microorganisms and increased the stability of soil bacterial communities. The Mantel test showed that the changes in the soil bacterial community and function due to inoculation with Bacillus sp. SL-413 and Enterobacter sp. VY-1 were important factors influencing sorghum growth and Cd remediation efficiency. The results of this study will provide new evidence for the research on joint plantmicrobe remediation of heavy metal and microplastic composite pollution.

Depressive symptomatology in older adults treated with behavioral activation: A network perspective

BACKGROUND

Late-life depression is a serious mental health problem. Behavioral Activation (BA) is an effective, accessible psychotherapeutic treatment for older adults. However, little is known about which symptoms decrease and how associations between depressive symptoms change during BA treatment.

METHODS

Using data from a cluster-randomized trial for older adults with late-life depression, we estimated a partial correlation network and a relative importance network of depressive symptoms before and after 8 weeks of BA treatment in primary care (n = 96). Networks were examined with measures of network structure, connectivity, centrality as well as stability.

RESULTS

The most central symptoms at baseline and post-treatment were anhedonia, fatigue, and feeling depressed. In contrast, sleeping problems had the lowest centrality. The post-treatment network was significantly more interconnected than at baseline. Moreover, all symptoms were significantly more central at post-treatment.

CONCLUSION

Our findings highlight the utility of the network approach to better understand symptom networks of depressed older adults before and after BA treatment. Results show that network connectivity and centrality of all symptoms increased after treatment. Future studies should investigate longitudinal idiographic networks to explore symptom dynamics within individuals over time.

Burnout and depression in college students

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1)

Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.

The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen

BACKGROUND

This study aimed to investigate the interplay between anxiety and depressive symptoms in Chinese college freshmen using the causal system perspective (CSP), which differs from the traditional common cause perspective (CCP) by providing an alternative explanation by attributing comorbidity to direct interactions among symptoms.

METHODS

A convenience sample of 2,082 Chinese college freshmen (39.51% male, Mage = 18.61) from a normal university completed the Generalized Anxiety Disorder 7-Item Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). Network analysis was conducted and evaluated as to centrality, stability, node predictability, and bridging features. Moreover, the moderated network model (MNM) was utilized to detect the moderation effects of gender in the comorbidity network.

RESULTS

The network of anxiety and depressive symptoms exhibited stability, characterized by the core symptoms of "restlessness", "lack of energy", and "excessive worry about control", as well as the bridging symptoms of "fearfulness", "sad mood", and "irritability". Notably, the nodes representing "uncontrollable worry" and "difficulty in relaxation" demonstrated the highest predictive power. Gender did not exert any moderating effects on the anxiety and depressive symptom network.

CONCLUSION

These results reinforce that certain anxiety or depressive symptoms are more central than others, and thus play a more vital role in the comorbid network. These findings highlight underlying potential targeting symptoms to consider in future interventions.

Relationships among anxiety, depression, and health-related quality of life in adult epilepsy: A network analysis

OBJECTIVE

Comorbid depression and anxiety in patients with epilepsy (PWE) are common and frequently under-treated, thus, causing poor health-related quality of life (HRQoL). However, little is known regarding the interconnections between anxious/depressive symptoms and the dimensions of HRQoL. Therefore, we conducted a network analysis to explore these relationships in detail among Chinese adult PWE.

METHODS

A cohort of adult PWE was consecutively recruited from the First Affiliated Hospital of Chongqing Medical University. HRQoL, depression, and anxiety were measured with Quality of Life in Epilepsy Inventory-31, Neurological Disorders Depression Inventory for Epilepsy, and Generalized Anxiety Disorder 7-Item Scale, respectively. A regularized partial correlation network was constructed to investigate the interconnections between symptoms of anxiety/depression and the dimensions of HRQoL. We calculated expected influence (EI) and bridge expected influence (BEI) values to identify the most influential nodes.

RESULTS

A total of 396 PWE were enrolled in this study, 78.1% of whom had focal onset epilepsy. The prevalence of anxiety and depression was 30.3% and 28.8%, respectively. The symptoms "frustrated" and "uncontrollable worry" had the highest EI values, whereas "emotional well-being", "seizure worry", "difficulty finding pleasure", and "nervousness or anxiety" had the highest BEI values.

CONCLUSION

This study provides new insights into the relationships among anxiety, depression, and HRQoL. Critical central symptoms and bridge symptoms identified in the network might help to quickly identify PWE comorbid anxiety and depression in busy outpatient settings, thereby enabling early intervention and enhancing quality of life.

Hazardous drinking in young adults with co-occurring PTSD and psychosis symptoms: A network analysis

BACKGROUND

Existing literature suggests the co-occurrence of post-traumatic stress disorder (PTSD) and psychosis among young adults is related to hazardous drinking. However, the influencing mechanisms among these co-occurrences are inconclusive. Thus, this study aimed to investigate the symptomatic associations between PTSD, psychosis, and hazardous drinking.

METHODS

This study included 96,218 young Chinese adults, divided into three groups (PTSD, Psychosis, and co-occurring PTSD-Psychosis). PTSD, psychosis, and hazardous drinking were measured by the ten-item Trauma Screening Questionnaire, the seven-item Psychosis Screener Scale, and the four-item Alcohol Use Disorders Identification Test, respectively. Network analysis was utilized to explore and compare the symptomatic correlation between PTSD, psychosis, and hazardous drinking.

RESULTS

In this study, the most crucial symptom (both central and bridge) was "delusion of control" among the three networks. Hazardous drinking was another main bridge symptom. Compared to the Psychosis group and the co-occurring PTSD-Psychosis group, "Delusion of reference or persecution" to "Grandiose delusion" was the strongest edge in "the network structure of the PTSD group".

LIMITATIONS

The cross-sectional study cannot determine the causal relationship. Applying self-reporting questionnaires may cause inherent bias. Young adult participants limited the generalization of the results to other groups.

CONCLUSIONS

Among the three network structures, delusion of control was the most crucial symptom, and hazardous drinking was another bridge symptom; the edge of delusion of reference or persecution and grandiose delusion was strongest in the PTSD group's network. Efforts should be taken to develop diverse targeted interventions for these core symptoms to relieve PTSD, psychosis, and hazardous drinking in young adults.

Comparison of the EPDS and PHQ-9 in the assessment of depression among pregnant women: Similarities and differences

BACKGROUND

Perinatal depression has attracted increasing attention. However, a detailed investigation of the network structure of depression is still lacking. We aim to examine the similarities and differences between the Edinburgh Postnatal Depression Scale (EPDS) and Patient Health Questionnaire (PHQ-9) from a network perspective.

METHODS

A cross-sectional study was conducted from August 2020 to March 2022. We followed the STROBE checklist to report our research. Pregnant women (n = 2484) were recruited. All participants completed the EPDS and PHQ-9. We mainly used network analyses for statistical analysis and constructed two network models: the EPDS and PHQ-9 models.

RESULTS

The detection rates of prenatal depression measured by the EPDS and PHQ-9 were 30.2 % and 28.2 %, respectively. In the EPDS network, the EPDS8 'sad or miserable' node (strength = 1.2161) was the most central node, and the EPDS10 'self-harming' node (strength = 0.4360) was the least central node. In the PHQ-9 network, the PHQ4 'fatigue' node (strength = 0.9815) was the most central node, and PHQ9 'suicide' was the least central symptom (strength = 0.5667). For both models, 'sad' acted as an important central symptom.

CONCLUSIONS

Psychological symptoms may be more important in assessing depression using the EPDS, while physical symptoms may be more influential in assessing depression using the PHQ-9. For both the EPDS and PHQ-9, "sad" was an important central symptom, suggesting that it may be the most important target for further maternal depression interventions in the future.

Suicide risk, related factors and the impact of COVID-19 amongst suicide prevention helpline callers: A network analysis

BACKGROUND

The COVID-19 pandemic brought along many known risk factors for suicide. It is important to map out contributing and protective factors for suicide risk and examine possible changes in these associations during pandemics such as COVID-19. The current study aimed to examine how information on risk and protective factors obtained through a suicide prevention helpline is linked to the assessed suicide risk and the possible impact of the COVID-19 pandemic.

METHODS

Data on 9474 calls registered by operators of the suicide prevention helpline of Flanders (i.e., part of Belgium) were analysed using network analysis. Using network analyses allowed for a data-driven examination of direct and indirect pathways through which risk and protective factors are associated to perceived suicide risk. The network before and during COVID-19 were compared to examine the possible impact of the pandemic.

RESULTS

Our findings suggest that different vulnerability and protective factors contribute to perceived suicide risk. Experiencing a break-up, abuse, previous attempt(s), experienced difficulties with the healthcare system and availability of resources were directly and uniquely associated with perceived suicide risk before and during COVID-19.

LIMITATIONS

Main limitations of this study are the possible bias of operator assessment accuracy, absence of several important psychological risk factors and the use of cross-sectional data.

CONCLUSIONS

The current study provides insight in the effect of COVID-19 on suicidality and its risk and protective factors amongst suicide prevention helpline users, a population with high risk of suicide. Implications for suicide prevention helplines are discussed.

Symptom patterns in the co-occurrence of depressive and generalized anxiety symptoms: A network analysis of a Canadian nationally representative sample

BACKGROUND

This study aimed to investigate the symptom patterns of major depressive disorder (MDD) and generalized anxiety disorder (GAD) in a matched nationally representative sample of the Canadian population. We also tested whether childhood maltreatment (CM) exposures and sex would be linked with different symptom patterns.

METHODS

A total of 3296 participants from the Canadian Community Health Survey-Mental Health with complete information on MDD and GAD symptoms and being matched on the studied sociodemographic characteristics were included in the current study. Network analysis was performed to examine the MDD-GAD symptom network, network stability and centrality indices were also estimated. Finally, network comparison in connectivity patterns was conducted to explore the impact of maltreatment experience and sex differences in the MDD-GAD symptom networks.

RESULTS

The CM group had stronger network connections and showed differences in the network structures from the non-CM group. In the CM group, depressed mood and diminished interest were central symptoms and strongly connected with other symptoms. Additionally, females had stronger connections in the MDD-GAD symptom network than males, and sleep disturbance was a central symptom for females, alongside depressed mood and diminished interest.

LIMITATIONS

The cross-sectional design restricts our capacity to establish longitudinal or causal connections between symptoms.

CONCLUSIONS

Depressed mood was the most central node that was strongly connected with other symptoms in the network. Distinct MDD-GAD symptom networks were discovered in the CM and the female group when compared to their counterparts. Noteworthy, individuals with CM had a stronger correlation between worry and suicidal ideation. Clinical management and intervention efforts should pay close attention to these core symptoms to yield optimal treatment effects, particularly for females and individuals with CM.

Personality traits and depressive symptoms among Chinese older people: A network approach

BACKGROUND

The present study aims to investigate the network structure of depressive symptoms, the interrelationships between individual personality traits and depressive symptoms, and gender differences among Chinese older people aged 60 and above.

METHOD

We performed network analyses with a regularized Graphical Gaussian Model and a case-dropping bootstrap approach. A sample of 4876 older Chinese people aged 60+ was included in the analyses. We investigated the central symptoms in the depression network and the bridge nodes that connect personality facets and depressive symptoms. Gender differences were investigated by testing the global strength, network invariance, and edge weights.

RESULTS

Sadness and depressed mood were the most central depressive symptoms, while somatic symptoms such as restless sleep were the least central. Neurotic facets, particularly "worry a lot" and "get nervous easily", played significant bridging roles in the web of personality traits and depressive symptoms. Gender differences were observed in three edges among different personality traits (rude-worried, original-worried, and forgiving-nervous).

LIMITATION

The study adopts a cross-sectional dataset, and therefore, cannot track the network changes over time or conclude a causal relationship.

DISCUSSION

The study calls for more focus and prioritization on sadness, depressed mood and neurotic traits in the identification of depression among older Chinese people. Future researchers and practitioners should better understand of older Chinese adults' worry and nervousness to develop appropriate practices and policies.

The function and keystone microbiota in typical habitats under the influence of anthropogenic activities in Baiyangdian Lake

Microbe is an essential driver in regulating the biochemical cycles of carbon, nitrogen, and sulfur. In freshwater lake, microbial communities and functions are influenced by multiple factors, especially anthropogenic activities. Baiyangdian Lake consisted of various habitats, and was frequently interfered with human activities. In this study, 16 S rRNA sequencing and metagenomic sequencing were performed to characterize the microbial communities, determine keystone taxa and reveal dominated metabolic functions in typical habitats in Baiyangdian Lake. The results showed that the diversity of microbial community was significantly higher in sediment compared with corresponding water sample. Microbial community showed strong spatial heterogeneity in sediment, and temporal heterogeneity in water. As for different habitats, significantly higher alpha diversity was observed in ecotone, where the interference of human activities was relatively weak. The shared OTUs were distinguished from the keystone taxa, which indicated the uniqueness of microbiota in different ecological habitat. Moreover, the interactions of microbial in ecological restoration area (abandoned fish pond) were relatively simple, suggesting that this ecosystem was relatively fragile compared with others. Based on the metagenomic sequencing, we recognized that the canal, open water, and abandoned fish pond were beneficial for methanogenic and the ecotone might be a hot zone for the oxidation of methane. Notably, most of the microbes that participated in these predominant metabolisms were unclassified, which indicated the hug potential for exploring functional microorganisms in Baiyangdian Lake. This study provided a comprehensive understanding of the ecology characteristics of microbiota in habitats undergoing various human interference in Baiyangdian Lake.

The global patent landscape of emerging infectious disease monkeypox

BACKGROUND

Monkeypox is an emerging infectious disease with confirmed cases and deaths in several parts of the world. In light of this crisis, this study aims to analyze the global knowledge pattern of monkeypox-related patents and explore current trends and future technical directions in the medical development of monkeypox to inform research and policy.

METHODS

A comprehensive study of 1,791 monkeypox-related patents worldwide was conducted using the Derwent patent database by descriptive statistics, social network method and linear regression analysis.

RESULTS

Since the 21st century, the number of monkeypox-related patents has increased rapidly, accompanied by increases in collaboration between commercial and academic patentees. Enterprises contributed the most in patent quantity, whereas the initial milestone patent was filed by academia. The core developments of technology related to the monkeypox include biological and chemical medicine. The innovations of vaccines and virus testing lack sufficient patent support in portfolios.

CONCLUSIONS

Monkeypox-related therapeutic innovation is geographically limited with strong international intellectual property right barriers though it has increased rapidly in recent years. The transparent licensing of patent knowledge is driven by the merger and acquisition model, and the venture capital, intellectual property and contract research organization model. Currently, the patent thicket phenomenon in the monkeypox field may slow the progress of efforts to combat monkeypox. Enterprises should pay more attention to the sharing of technical knowledge, make full use of drug repurposing strategies, and promote innovation of monkeypox-related technology in hotspots of antivirals (such as tecovirimat, cidofovir, brincidofovir), vaccines (JYNNEOS, ACAM2000), herbal medicine and gene therapy.

Effect of rainfall in shaping microbial community during Microcystis bloom in Nakdong River, Korea

Various environmental factors play a role in the formation and collapse of Microcystis blooms. This study investigates the impact of heavy rainfall on cyanobacterial abundance, microbial community composition, and functional dynamics in the Nakdong River, South Korea, during typical and exceptionally rainy years. The results reveal distinct responses to rainfall variations, particularly in cyanobacterial dominance and physicochemical characteristics. In 2020, characterized by unprecedented rainfall from mid-July to August, Microcystis blooms were interrupted significantly, exhibiting lower cell densities and decreased water temperature, compared to normal bloom patterns in 2019. Moreover, microbial community composition varied, with increases in Gammaproteobacteria and notably in genera of Limnohabitans and Fluviicola. These alterations in environmental conditions and bacterial community were similar to those of the post-bloom period in late September 2019. It shows that heavy rainfall during summer leads to changes in environmental factors, consequently causing shifts in bacterial communities akin to those observed during the autumn-specific post-bloom period in typical years. These changes also accompany shifts in bacterial functions, primarily involved in the degradation of organic matter such as amino acids, fatty acids, and terpenoids, which are assumed to have been released due to the significant collapse of cyanobacteria. Our results demonstrate that heavy rainfall in early summer induces changes in the environmental factors and subsequently microbial communities and their functions, similar to those of the post-bloom period in autumn, leading to the earlier breakdown of Microcystis blooms.

Network Structure and Clustering Analysis Relating to Individual Symptoms of Problematic Internet Use in a Community Adolescent Population

INTRODUCTION

Problematic internet use (PIU) is a psychopathology that includes multiple symptoms and psychological constructs. Because no studies have considered both network structures and clusters among individual symptoms in the context of PIU in a Korean adolescent population, this study aimed to investigate network structures and clustering in relation to PIU symptoms in adolescents.

METHODS

Overall, 73,238 adolescents were included. PIU severity was assessed using a self-rating scale comprising 20 items and 6 subscales, namely, the Internet Addiction Proneness Scale for Youth-Short Form; KS scale. Network structures and clusters among symptoms were analyzed using a Gaussian graphical model and exploratory graph analysis, respectively. Centrality of strength, closeness, and betweenness scores was also calculated.

RESULTS

Our study identified four clusters: disturbance in adaptive functioning, virtual interpersonal relationships, withdrawal, and tolerance. The symptom of confidence served as a node bridging the cluster of virtual interpersonal relationships and other clusters of withdrawal and disturbances of adaptive function. The symptom of craving served as a bridge between the clusters of withdrawal and tolerance with high betweenness centrality.

CONCLUSION

This study identified network structures and clustering among PIU symptoms in adolescents and revealed that positive experiences derived from online interpersonal relationships were an important mechanism underlying PIU. These are novel insights concerning the interconnection among multiple symptoms and related clustering for the mechanism of adolescent PIU in terms of KS-scale PIU assessment.

Network analysis on psychopathological symptoms, psychological measures, quality of life and COVID-19 related factors in Chinese psychiatric patients in Hong Kong

BACKGROUND

Psychiatric patients are susceptible to adverse mental health impacts during COVID-19, but complex interplays between psychopathology and pandemic-related variables remain elusive. This study aimed to investigate concomitant associations between psychopathological symptoms, psychological measures and COVID-19 related variables in Chinese psychiatric patients during the peak of fifth pandemic wave in Hong Kong.

METHODS

We employed network analysis to investigate inter-relationships among psychopathological symptoms (including depression, anxiety, post-traumatic stress disorder-like [PTSD-like] symptoms, insomnia, psychotic symptoms), cognitive complaints, health-related quality of life, loneliness, resilience and selected pandemic-related factors in 415 psychiatric outpatients between 28 March and 8 April, 2022. Network comparisons between genders, diagnosis (common mental disorders [CMD] vs. severe mental disorders [SMD]), and history of contracting COVID-19 at fifth wave were performed as exploratory analyses.

RESULTS

Our results showed that anxiety represented the most central node in the network, as indicated by its highest node strength and expected influence, followed by depression and quality of life. Three comparatively strong connections between COVID-19 and psychopathological variables were observed including: fear of contagion and PTSD-like symptoms, COVID-19 stressor burden and PTSD-like symptoms, and COVID-19 stressor burden and insomnia. Network comparison tests revealed significant network structural difference between participants with history of contracting COVID-19 and those without, but showed no significant difference between genders as well as between CMD and SMD patients.

CONCLUSIONS

Our findings suggest the pivotal role of anxiety in psychopathology network of psychiatric patients amidst COVID-19. Pandemic-related variables are critically associated with trauma/stress and insomnia symptoms. Future research is required to elucidate potential network structural changes between pandemic and post-COVID periods.

The network structures of depressive and insomnia symptoms among cancer patients using propensity score matching: Findings from the Health and Retirement Study (HRS)

OBJECTIVE

Both depression and insomnia are found to be more prevalent in cancer patients compared to the general population. This study compared the network structures of depression and insomnia among cancer patients versus cancer-free participants (controls hereafter).

METHOD

The 8-item Center for Epidemiological Studies Depression Scale (CESD-8) and the 4-item Jenkins Sleep Scale (JSS-4) were used to measure depressive and insomnia symptoms, respectively. Propensity score matching (PSM) was used to construct the control group using data from the Health and Retirement Study (HRS). In total, a sample consisting of 2216 cancer patients and 2216 controls was constructed. Central (influential) and bridge symptoms were estimated using the expected influence (EI) and bridge expected influence (bridge EI), respectively. Network stability was assessed using the case-dropping bootstrap method.

RESULT

The prevalence of depression (CESD-8 total score ≥ 4) in cancer patients was significantly higher compared to the control group (28.56 % vs. 24.73 %; P = 0.004). Cancer patients also had more severe depressive symptoms relative to controls, but there was no significant group difference for insomnia symptoms. The network structures of depressive and insomnia symptoms were comparable between cancer patients and controls. "Felt sadness" (EI: 6.866 in cancer patients; EI: 5.861 in controls), "Felt unhappy" (EI: 6.371 in cancer patients; EI: 5.720 in controls) and "Felt depressed" (EI: 6.003 in cancer patients; EI: 5.880 in controls) emerged as the key central symptoms, and "Felt tired in morning" (bridge EI: 1.870 in cancer patients; EI: 1.266 in controls) and "Everything was an effort" (bridge EI: 1.046 in cancer patients; EI: 0.921 in controls) were the key bridge symptoms across both groups.

CONCLUSION

Although cancer patients had more frequent and severe depressive symptoms compared to controls, no significant difference was observed in the network structure or strength of the depressive and insomnia symptoms. Consequently, psychosocial interventions for treating depression and insomnia in the general population could be equally applicable for cancer patients who experience depression and insomnia.

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

Inter-relationships of depression and insomnia symptoms with life satisfaction in stroke and stroke-free older adults: Findings from the Health and Retirement Study based on network analysis and propensity score matching

BACKGROUND

Depression and insomnia are common co-occurring psychiatric problems among older adults who have had strokes. Nevertheless, symptom-level relationships between these disorders remain unclear.

OBJECTIVES

In this study, we compared inter-relationships of depression and insomnia symptoms with life satisfaction among older stroke patients and stroke-free peers in the United States.

METHODS

The study included 1026 older adults with a history of stroke and 3074 matched controls. Data were derived from the US Health and Retirement Study. Depression, insomnia and life satisfaction were assessed. Propensity score matching was employed to identify demographically-similar groups of stroke patients and controls. Central and bridge symptoms were assessed using Expected influence (EI) and bridge EI, respectively.

RESULTS

The prevalence of depression in the stroke group (25.0 %) was higher than that of controls (14.3 %, P < 0.001). In stroke group, "Feeling depressed" (CESD1; EI: 5.80), "Feeling sad" (CESD7; EI: 4.67) and "Not enjoying life" (CESD6; EI: 4.51) were the most central symptoms, while "Not feel rested in the morning" (JSS4; BEI: 1.60), "Everything was an effort" (CESD2; BEI: 1.21) and "Waking up during the night" (JSS2; BEI: 0.98) were key bridge symptoms. In controls, the most central symptoms were "Lack of happiness" (CESD4; EI: 6.45), "Feeling depressed" (CESD1; EI: 6.17), and "Feeling sad" (CESD7; EI: 6.12). Furthermore, "Feeling tired in the morning" (JSS4; BEI: 1.93), "Everything was an effort" (CESD2; BEI: 1.30), and "Waking up too early" (JSS3; BEI: 1.12) were key bridge symptoms. Life satisfaction had the most direct associations with "Not enjoying life" (CESD6) and "Feeling lonely" (CESD5) in the two groups, respectively.

CONCLUSION

Older adults with stroke exhibited more severe depression and insomnia symptoms. Interventions targeting central and bridge symptoms may help to mitigate the co-occurrence of these symptoms.

Understanding heterogeneity, comorbidity, and variability in depression: Idiographic models and depression outcomes

This study uses time-intensive, item-level assessment to examine individual depressive and co-occurring symptom dynamics. Participants experiencing moderate-severe depression (N = 31) completed ecological momentary assessment (EMA) four times per day for 20 days (total observations = 2480). We estimated idiographic networks using MDD, anxiety, and ED items. ED items were most frequently included in individual networks relative to depression and anxiety items. We built ridge and logistic regression ensembles to explore how idiographic network centrality metrics performed at predicting between-subject depression outcomes (PHQ-9 change score and clinical deterioration, respectively) at 6-months follow-up. For predicting PHQ-9 change score, R2 ranged between 0.13 and 0.28. Models predicting clinical deterioration ranged from no better than chance to 80 % accuracy. This pilot study shows how co-occurring anxiety and ED symptoms may contribute to the maintenance of depressive symptoms. Future work should assess the predictive utility of psychological networks to develop understanding of how idiographic models may inform clinical decisions.

Network analysis for inter-relationships of the suboptimal health status with depression and anxiety during the COVID-19 pandemic: A perspective of predictive, preventive, and personalized health

BACKGROUND

The Coronavirus Disease-2019 (COVID-19) pandemic has had a profound impact on suboptimal health status, depression, and anxiety, necessitating a comprehensive understanding of their inter-relationships at the national level. This study aims to investigate the inter-relationships among suboptimal health status, depression, and anxiety using a network analysis approach.

METHODS

We conducted a national survey between June 20 and August 31, 2022. Three network models were constructed and analyzed to independently examine the inter-relationships among suboptimal health status, depression, and anxiety.

RESULTS

A total of 26,152 participants were included in this study. The study network analysis indicated that item 9 (i.e., Slow response) exhibited the highest node strength within the suboptimal health status questionnaire-short form (SHSQ-SF) network, followed by item 5 (i.e., Breathlessness at rest). Additionally, positive correlations were observed between depression and anxiety severity and most of the SHSO-SF items.

CONCLUSIONS

This study provided valuable insights into inter-relationships between suboptimal health status, depression, and anxiety, informing the development of comprehensive intervention strategies for the general population. These findings have important implications for promoting the well-being and mental health of individuals during and beyond the COVID-19 pandemic.

Network analysis of posttraumatic stress and posttraumatic growth symptoms among women in subsequent pregnancies following pregnancy loss

BACKGROUND

Pregnant women who have undergone pregnancy loss often display both posttraumatic stress (PTS) and posttraumatic growth (PTG). However, the precise relationship and structure of symptomatic levels of PTS and PTG have not been well understood. This study aimed to assess the associations between PTS and PTG symptoms in women during subsequent pregnancies following a previous pregnancy loss.

METHODS

A total of 406 pregnant women with a history of pregnancy loss were included in this study. The Impact of Events Scale-6 (IES-6) and the Posttraumatic Growth Inventory Short Form (PTGI-SF) were used to assess symptoms of PTS and PTG, respectively. The Graphical Gaussian Model was employed to estimate the network model. Central symptoms and bridge symptoms were identified based on "expected influence" and "bridge expected influence" indices, respectively. The stability and accuracy of the network were examined using the case-dropping procedure and nonparametric bootstrapped procedure.

RESULTS

The network analysis identified PTG3 ("Ability to do better things") as the most central symptom, followed by PTS3 ("Avoidance of thoughts") and PTG6 ("New path for life") in the sample. Additionally, PTS3 ("Avoidance of thoughts") and PTG9 ("Perception of greater personal strength") were bridge symptoms linking PTS and PTG clusters. The network structure was robust in stability and accuracy tests.

CONCLUSIONS

Interventions targeting the central symptoms identified, along with key bridge symptoms, have the potential to alleviate the severity of PTS experienced by women with a history of pregnancy loss and promote their personal growth.

Mild differences in the role of somatic symptoms in depression networks in pregnancy and postpartum: A comparison with women outside peripartum

Depression is one of the most prevalent mental ailments in pregnancy. Many authors have discussed the appropriateness of somatic symptoms for depression assessment in pregnancy and postpartum. However, no study has examined the role of somatic symptoms in networks of pregnant and postpartum women compared to women outside peripartum. Here, we show that somatic symptoms are essential to depression assessment during pregnancy and postpartum. We compared networks of the nine PHQ-9 items across pregnant women (n = 894, Mage = 29.29), women in postpartum (n = 586, Mage = 29.83) and women outside peripartum (n = 1029, Mage = 24.87). While three of the five somatic symptoms in PHQ-9 were more present in pregnant women than in those outside the peripartum, the three networks were highly similar regarding the position of the somatic symptoms and their relation to the emotional-cognitive symptoms. Most depression symptoms in pregnant women were predicted by other depression symptoms to a lesser extent than in postpartum and outside peripartum. Other external variables are therefore needed to explain these sensations in pregnancy. In addition to the incidence of somatic symptoms, practitioners should ask pregnant women about their attributions of these.

Depression, anxiety, and insomnia symptoms among Chinese college students: A network analysis across pandemic stages

BACKGROUND

As the stages of the COVID-19 pandemic evolved, the symptoms of depression, anxiety, and insomnia have increasingly manifested among Chinese college students. The aim of this study is to investigate the relationships between these symptoms through network analysis among Chinese college students during COVID-19.

METHOD

A three-wave cross-sectional survey was conducted at 22 colleges in Guangdong Province, involving 381,152 students during three specific time intervals: T1 (baseline, February 3 to 10, 2020), T2 (19 months after baseline, June 10 to 18, 2021), and T3 (37 months after baseline, March 15 to April 22, 2023). Depression (PHQ-9), anxiety (GAD-7), and insomnia (YSIS) were used separately. We analyzed two key network indices: "Expected influence" and "Bridge expected influence". Network stability was assessed through a case-dropping bootstrap program.

RESULT

The effective sample sizes for the three periods were as follows: T1 - 164,101 (103,645 females, 63.2 %), T2 - 86,767 (52,146 females, 60.1 %), and T3 - 130,284 (76,720 females, 58.9 %). Across these three periods, the key central symptoms were "Fatigue" (PHQ4), "Restlessness" (GAD5), "Uncontrollable worrying" (GAD2), "Worry too much" (GAD3) and "Sleep insufficiency" (YSIS6). Notably, "Fatigue" (PHQ4), "Restlessness" (GAD5) and "Irritability" (GAD6) consistently served as bridge symptoms. In the T1 and T2 period, "Motor" (PHQ8) acted as a bridge symptom but weakened in T3.

CONCLUSION

Throughout the three periods, the mental health issues among Chinese college students displayed characteristics of somatization within the depression-anxiety-insomnia comorbidity network. Over time, anxiety symptoms appeared to become more prominent. Consequently, this study highlights the importance of accurately identifying and promptly intervening in these core symptoms of mental health among college students, as these symptoms may evolve across different stages of a pandemic.

Network analysis of social anxiety and problematic mobile phone use in Chinese adolescents: A longitudinal study

Social anxiety is a common comorbid factor of problematic mobile phone use, but the relationship between them from the inside structure has been underexplored. This two-wave longitudinal study aims to identify the central symptoms of problematic mobile phone use and social anxiety, respectively, as well as the bridge symptoms between the two disorders. Furthermore, this study examined the time and gender differences. A total of 3,294 Chinese adolescents were recruited during two waves (T1: January 2023; T2: August 2023), and completed the questionnaires of social anxiety and problematic mobile phone use. Through network analysis, the results showed that 1) the central symptom of problematic mobile phone use was "loss of control" both at T1 and T2, 2) the central symptom of social anxiety was "fear of negative evaluation" both at T1 and T2, and 3) the bridge symptom between social anxiety and problematic mobile phone use was "withdrawal" both at T1 and T2. Moreover, the network comparison test indicated that the nodes of social anxiety were more strongly associated with "craving", "loss of control", and "negative life consequences" of problematic mobile phone use at T2 than at T1. The nodes of social anxiety were more strongly associated with "negative life consequences" of problematic mobile phone use for boys than for girls, while the "fear of negative evaluation" was more strongly associated with "loss of control" for girls than for boys. These findings provide new scientific basis for interventions targeting adolescents who are susceptible to social anxiety and problematic mobile phone use.

Trauma behind the keyboard: Exploring disparities in child sexual abuse materials exposure and mental health factors among investigators and forensic examiners - A network analysis

BACKGROUND

Despite acknowledging the detrimental impact of child sexual abuse material (CSAM) exposure on the mental and physical well-being of investigators and forensic examiners, there is a need for comprehensive exploration into the complex relationship between CSAM exposure, its various dimensions, mental health (i.e., anxiety, depression, and PTSD), and burnout, as well as the presence of positive attitudes towards the job.

OBJECTIVE

To understand how CSAM exposure, mental health and burnout interconnect and cluster within distinct networks of police investigators and forensic examiners.

PARTICIPANTS AND SETTING

Police investigators and forensic examiners from across the United States who were exposed to CSAM as part of their professions (N = 470).

METHODS

Participants, recruited through connections with the National Criminal Justice Training Center, completed an anonymous online survey.

RESULTS

The network analysis revealed differences in centrality between investigators and forensic examiners, particularly in their associations with exposure factors and mental health variables. Edges invariance tests showed differences in the strength of these associations, with some factors being more strongly linked to PTSD avoidance symptoms among investigators and others among forensic examiners. Stability analyses suggested potentially greater heterogeneity among investigators, while both groups displayed high stability in other centrality indices.

CONCLUSIONS

This study contributes to our comprehension of the distinct experiences and challenges faced by CSAM investigators and forensic examiners, and specifically the nuanced disparities between CSAM investigators and forensic examiners in terms of their exposure to CSAM content and the associated mental health factors. These insights highlight the imperative need for tailored support mechanisms and interventions that can effectively address the unique challenges encountered by individuals working tirelessly in this critical field.

Network analysis of anxiety and depressive symptoms during the COVID-19 pandemic in older adults in the United Kingdom

The health crisis caused by COVID-19 in the United Kingdom and the confinement measures that were subsequently implemented had unprecedented effects on the mental health of older adults, leading to the emergence and exacerbation of different comorbid symptoms including depression and anxiety. This study examined and compared depression and anxiety symptom networks in two specific quarantine periods (June-July and November-December) in the older adult population in the United Kingdom. We used the database of the English Longitudinal Study of Aging COVID-19 Substudy, consisting of 5797 participants in the first stage (54% women) and 6512 participants in the second stage (56% women), all over 50 years of age. The symptoms with the highest centrality in both times were: "Nervousness (A1)" and "Inability to relax (A4)" in expected influence and predictability, and "depressed mood (D1"; bridging expected influence). The latter measure along with "Irritability (A6)" overlapped in both depression and anxiety clusters in both networks. In addition, a the cross-lagged panel network model was examined in which a more significant influence on the direction of the symptom "Nervousness (A1)" by the depressive symptoms of "Anhedonia (D6)", "Hopelessness (D7)", and "Sleep problems (D3)" was observed; the latter measure has the highest predictive capability of the network. The results report which symptoms had a higher degree of centrality and transdiagnostic overlap in the cross-sectional networks (invariants) and the cross-lagged panel network model of anxious and depressive symptomatology.

Treatment patterns and persistence on disease modifying therapies for multiple sclerosis and its associated factors

BACKGROUND

Effective interventions for Multiple Sclerosis require timely treatment optimization which usually involves switching disease modifying therapies. The patterns of prescription and the reasons for changing treatment in people with MS, especially in low prevalence populations, are unknown.

OBJECTIVES

To describe the persistence, reasons of DMT switches and prescription patterns in a cohort of Colombian people with MS.

METHODS

We conducted a retrospective observational study including patients with confirmed MS with at least one visit at our centre. We estimated the overall incidence rate of medication changes and assessed the persistence on medication with Kaplan-Meier survival estimates for individual medications and according to efficacy and mode of administration. The factors associated with changing medications were assessed using adjusted Cox proportional-hazards models. The reasons for switching medication changes were described, and the prescription patterns were assessed using network analysis, with measures of centrality.

RESULTS

Seven hundred one patients with MS were included. Mean age was 44.3 years, and 67.9% were female. Mean disease duration was 11.3 years and 84.5% had relapsing MS at onset, with median EDSS of 1.0. Treatment was started in 659 (94%) of the patients after a mean of 3 years after MS symptom onset. Among them, 39.5% maintained their initial DMT, 29.9% experienced a single DMT change, while 18.7% went through two, and 11.9% had three or more DMT changes until the final follow-up. The total number of treatment modifications reached 720, resulting in an incidence rate of 1.09 (95% confidence interval: 1.01-1.17) per patient per year The median time to change after the first DMT was 3.75 years, and was not different according to the mode of administration or efficacy classification. The main reasons for changing DMT were MS activity (relapses, 56.7%; MRI activity, 18.6%), followed by non-serious adverse events (15.3%) and disability (11.1%). Younger age at MS onset, care under our centre and insurer status were the main determinants of treatment change. Network analysis showed that interferons and fingolimod were the most influential DMTs.

CONCLUSIONS

A majority of patients switch medications, mostly due to disease activity, and in association with age and insurer status.

Health communication on social media at the early stage of the pandemic: Examining health professionals' COVID-19 related tweets

Focusing on health professionals' tweets regarding COVID-19, this study examines whether and how those tweets are unique based on their identity as health experts. The data revealed that the infusion of health communication with political opinions, whether pro- or against certain political parties or health policies, reflects values and may deviate from the original purpose of health communication. In addition, sentiment analysis countered the intuitive thought that health experts merely fulfill their role as neutral encyclopedias without excessively carrying sentiment. We conclude by reflecting on the meaning of health communication in relation to the political stances of professionals.

Systemic sclerosis associated respiratory involvement: Scopus-based analysis of articles in 2013-2022

BACKGROUND

Systemic sclerosis (SSc), a complex autoimmune disorder, manifests as a convergence of rheumatologic, dermatologic, and pulmonary challenges. Among the severe complications contributing to morbidity and mortality are SSc Associated Interstitial Lung Disease (SSc-ILD) and pulmonary hypertension. Over the past decade, research on pulmonary involvement in SSc has intensified, leading to a heightened understanding of its pathogenesis, diagnostic methods, and therapeutic strategies.

AIM

This study aims to provide a data-driven overview of the current state of systemic sclerosis research, identifying emerging trends and fostering informed decisions regarding resource allocation and research priorities.

METHODS

A literature search was conducted in the Scopus database, using MESH keywords such as "systemic sclerosis" AND "lungs" OR "pulmonary hypertension" OR "interstitial lung disease". After applying exclusion criteria, a thorough analysis was performed, considering factors such as document category, authorship, journal source, citation frequency, country of publication, language, and keywords. The bibliometric analysis utilized Scopus as the preferred database, leveraging its extensive coverage, user-friendly interface, and commitment to data accuracy. Visual networks were constructed using VOSviewer software to map the relationships between keywords, countries, and authors. Altmetric Attention Scores (AAS) were employed to assess the social impact of articles.

RESULTS

The analysis revealed a total of 2538 scholarly items, with 55.7% identified as open access. The USA (n = 532), Italy (n = 458), France (n = 304), Japan (n = 271), and the UK (n = 236) emerged as primary contributors, with English being the predominant language. A notable upward tendency in annual publication and citation scores indicated sustained interest and relevance in SSc-ILD research. The top journals, including Rheumatology United Kingdom, Clinical and Experimental Rheumatology, Clinical Rheumatology, Arthritis and Rheumatology, and Journal of Rheumatology, played a pivotal role in scholarly output. Original Articles (n = 1795; 70.7%) constituted the majority of publications, followed by Reviews, Letters, Notes, and Editorials. The analysis of publication impact within different scholarly formats revealed varying citation patterns, with Original Articles and Reviews leading in influence. The identification of influential research hubs and key contributors provided insights into collaborative efforts and geographic distribution. A strong correlation (rho = 0.612, p < 0.001) was observed between the quantity of Mendeley readers and the citations received by scholarly articles.

CONCLUSION

This bibliometric analysis offers a comprehensive overview of SSc-ILD research, highlighting its dynamic and interdisciplinary nature. The surge in publications, citation scores, and the identification of key contributors underscore the continued relevance and impact of this field. The nuanced relationships between social attention and scientific recognition, as revealed by Mendeley readership and AAS, contribute to a deeper understanding of the multifaceted nature of scholarly impact.

Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

BACKGROUND

Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner relationships between common syndromes, such as depression and burnout. Network analysis (NA) was a novel approach to quantified the correlations between mental variables from the perspective of mathematics. This study was to investigate the interactions between burnout and depression symptoms through NA among ICU nurses.

METHOD

A cross-sectional study with a total of 616 Chinese nurses in ICU were carried out by convenience sampling from December 19, 2022 to January19, 2023 via online survey. Burnout symptoms were measured by Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version), and depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9). NA was applied to build interactions between burnout and depression symptoms. We identified central and bridge symptoms by R package qgraph in the network model. R package bootnet was used to examined the stability of network structure.

RESULTS

The prevalence of burnout and depressive symptoms were 48.2% and 64.1%, respectively. Within depression-burnout network, PHQ4(Fatigue)-MBI2(Used up) and PHQ4(Fatigue)-MBI5(Breakdown) showed stronger associations. MBI2(Used up) had the strongest expected influence central symptoms, followed by MBI4(Stressed) and MBI7 (Less enthusiastic). For bridge symptoms. PHQ4(Fatigue), MBI5(Breakdown) and MBI2(Used up) weighed highest. Both correlation stability coefficients of central and bridge symptoms in the network structure were 0.68, showing a high excellent level of stability.

CONCLUSION

The symptom of PHQ4(Fatigue) was the bridge to connect the emotion exhaustion and depression. Targeting this symptom will be effective to detect mental disorders and relieve mental syndromes of ICU nurses at the late stage of COVID-19 pandemic.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

A network-based approach to explore comorbidity patterns among community-dwelling older adults living alone

The detailed comorbidity patterns of community-dwelling older adults have not yet been explored. This study employed a network-based approach to investigate the comorbidity patterns of community-dwelling older adults living alone. The sample comprised a cross-sectional cohort of adults 65 or older living alone in a Korean city (n = 1041; mean age = 77.7 years, 77.6% women). A comorbidity network analysis that estimates networks aggregated from measures of significant co-occurrence between pairs of diseases was employed to investigate comorbid associations between 31 chronic conditions. A cluster detection algorithm was employed to identify specific clusters of comorbidities. The association strength was expressed as the observed-to-expected ratio (OER). As a result, fifteen diseases were interconnected within the network (OER > 1, p-value < .05). While hypertension had a high prevalence, osteoporosis was the most central disease, co-occurring with numerous other diseases. The strongest associations among comorbidities were found between thyroid disease and urinary incontinence, chronic otitis media and osteoporosis, gastric duodenal ulcer/gastritis and anemia, and depression and gastric duodenal ulcer/gastritis (OER > 1.85). Three distinct clusters were identified as follows: (a) cataracts, osteoporosis, chronic otitis media, osteoarthritis/rheumatism, low back pain/sciatica, urinary incontinence, post-accident sequelae, and thyroid diseases; (b) hyperlipidemia, diabetes mellitus, and hypertension; and (c) depression, skin disease, gastric duodenal ulcer/gastritis, and anemia. The results may prove valuable in guiding the early diagnosis, management, and treatment of comorbidities in older adults living alone.

Adverse childhood experiences and crime outcomes in early adulthood: A multi-method approach in a Brazilian birth cohort

This study aimed to investigate alternative approaches to a cumulative risk score in the relationship between adverse childhood experiences (ACEs) and crime. Using data from the 1993 Pelotas (Brazil) Birth Cohort (n = 3236), we measured 12 ACEs up to 15 years, and past-year violent and non-violent crime at 22 years. We used four analytical approaches: single adversities, cumulative risk, latent class analysis, and network analysis. When examined individually, physical abuse, emotional abuse, and domestic violence were associated with both crime outcomes, whereas maternal mental illness and discrimination were associated with violent crime only, and parental divorce and poverty with non-violent crime only. There was a cumulative effect of ACEs on crime. The class with child maltreatment and household challenges was associated with both crime outcomes; exposure to household challenges and social risks was associated with violent crime only. In network models, crime showed conditional associations with physical abuse, maternal mental illness, and parental divorce. Although cumulative ACEs did associate with crime, some individual and combinations of ACEs showed particularly strong and robust effects, which were not captured by the cumulative score. Many ACEs are closely connected and/or cluster together, and the usefulness of the ACE score needs to be further evaluated.

The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective

BACKGROUND

There are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective.

METHODS

A total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested.

RESULTS

"Failure", "External control", and "Lack of activity" were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were "Failure", "Guilty", and "No future". Positive PLE "Odd looks" and negative PLE "Unable to terminate" are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network.

CONCLUSION

In this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms. Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.

Symptom networks in older adults with cancer: A network analysis

INTRODUCTION

Due to aging, older adults with cancer (OAC) may be confronted with a complex interplay of multiple age-related issues; coupled with receiving cancer treatment, OAC may experience multiple concurrent symptoms that require the identification of the core symptom for effective management. Constructing symptom networks will help in the identification of core symptoms and help achieve personalized and precise interventions. Currently, few studies have used symptom networks to identify core symptoms in OAC. Our objectives were to construct symptom networks of OAC, explore the core symptoms, and compare the differences in symptom networks among various subgroups.

MATERIALS AND METHODS

Secondary analysis was performed using data from 485 OAC collected in 2021 from a cross-sectional survey named the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory (MDASI) was used to assess the incidence and severity of cancer-related symptoms. We used the R package to construct symptom networks and identify the centrality indices. The network comparison test was used to compare network differences among the subgroups.

RESULTS

The most common and severe symptoms reported were fatigue, disturbed sleep, and difficulty remembering. The network density was 0.718. Vomiting (rs = 1.81, rb = 2.13), fatigue (rs = 1.54, rb = 1.93), and sadness (rs = 0.81, rb = 0.69) showed the highest strength values, which suggested that these symptoms were more likely to co-occur with other symptoms. The network comparison tests showed significant differences in symptom network density between the subgroups categorized as survival "< 5 years" and survival "≥ 5 years" (p = 0.002), as well as between the those with comorbidities and those without comorbidities (p = 0.037).

DISCUSSION

Our study identified symptom networks in 485 OAC. Vomiting, fatigue, and sadness were important symptoms in the symptom networks of OAC. The symptom networks differed among populations with different survival durations and comorbidities. Our network analysis provides a reference for future targeted symptom management and interventions in OAC. In the future, conducting dynamic research on symptom networks will be crucial to explore interaction mechanisms and change trends between symptoms.