Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

BACKGROUND: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Engineering of Halide Methyltransferase BxHMT through Dynamic Cross-correlation Network Analysis.

Halide methyltransferases (HMTs) provide an effective way to regenerate S-adenosyl methionine (SAM) from S-adenosyl homocysteine and reactive electrophiles such as methyl iodide (MeI) or methyl toluene sulfonate (MeOTs). Compared with MeI, the cost-effective unnatural substrate MeOTs can be accessible directly from cheap and abundant alcohols, but shows only limited reactivity in SAM production. Herein, we developed a dynamic cross-correlation network analysis (DCCNA) strategy for quickly identifying hot spots influencing the enzyme's catalytic efficiency, and applied it to the evolution of HMT from Paraburkholderia xenovorans. Finally, the optimal mutant, M4 (V55T/C125S/L127T/L129P), exhibited remarkable improvements, with a specific activity of 4.08 U/mg towards MeOTs, representing an 82-fold increase compared to the wild-type (WT). Notably, M4 also demonstrated a positive impact on the catalytic ability with other methyl donors. The structural mechanism behind the enhanced enzyme activity was uncovered by molecular dynamics simulations. Our work not only contributes promising biocatalyst for the regeneration of SAM, but also offers a strategy for efficient enzyme engineering.

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.

Construction and evolutionary factors of spatial correlation network of China's provincial tourism resource conversion efficiency.

Research objective: To explore the spatial correlation network characteristics and formation mechanisms of tourism resource conversion efficiency, and provide reference for the collaborative improvement of tourism resource conversion efficiency at the provincial level in China. Research methods: Non parametric SBM efficiency measurement method and social network analysis method. Research hypothesis: The spatial network correlation characteristics of tourism resource conversion efficiency are obvious, and regional connections are close. Research findings: (i) during the research period, the spatial connection strength of China's tourism resource conversion efficiency continued to increase and the spatial network structure of tourism resource conversion efficiency tended to become more complex and significantly more stable. (ii) A spatially linked network with a stable tourism resource conversion efficiency structure formed in China. The number of network relations and density of the network fluctuated and increased, while the network efficiency continued to decrease; however, a strong small-world nature was observed. (iii) The economic development level difference matrix, tourism industry agglomeration difference matrix, human capital difference matrix, and marketization degree difference matrix significantly and positively affected spatial association relationship establishment, while the provincial adjacency matrix significantly and negatively affected such relationships.

Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post-COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post-COVID-19 condition on this demographic. OBJECTIVE: With a social media analysis of the discourse surrounding the prevalence of post-COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post-COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post-COVID-19 condition and identify critical areas and future directions for researchers and policy makers. METHODS: From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post-COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs. RESULTS: We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post-COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences. CONCLUSIONS: The perceptions described on Twitter by HCWs concerning the presence of the post-COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post-COVID-19 condition.

Emotion dysregulation links pathological eating styles and psychopathological traits in bariatric surgery candidates.

Objectives: Approximately one-third of bariatric surgery patients experience weight regain or suboptimal weight loss within five years post-surgery. Pathological eating styles and psychopathological traits (e.g., emotion dysregulation) are recognized as potential hindrances to sustain weight loss efforts and are implicated in obesity development. A comprehensive understanding of these variables and their interplays is still lacking, despite their potential significance in developing more effective clinical interventions for bariatric patients. We investigate the prevalence of and interactions between pathological eating styles and psychopathological traits in this population. Materials and methods: 110 bariatric surgery candidates were characterized using the Binge Eating Scale (BES), Hamilton Depression/Anxiety Scales (HAM-D/A), Barratt Impulsiveness Scale (BIS-11), Experiences in Close Relationships (ECR), Difficulties in Emotion Regulation Scale (DERS). We analyzed these variables with multiple logistic regression analyses and network analysis. Results: Patients with pathological eating styles showed more pronounced anxiety/depressive symptoms and emotion dysregulation. Network analysis revealed strong connections between BES and DERS, with DERS also displaying robust connections with HAM-A/D and ECR scales. DERS and attention impulsivity (BIS-11-A) emerged as the strongest nodes in the network. Discussion: Our findings demonstrate the mediating role of emotion dysregulation between pathological eating styles and psychopathological traits, supporting existing literature on the association between psychopathological traits, insecure attachment styles, and pathological eating behaviors. This research emphasizes the significance of emotion regulation in the complex network of variables contributing to obesity, and its potential impact on bariatric surgery outcomes. Interventions focusing on emotion regulation may thus lead to improved clinical outcomes for bariatric patients.

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.

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.

Characterizing Food Policy Councils' Network Partnerships and COVID-19 Responses.

The COVID-19 pandemic pushed millions of Americans into food insecurity. Food policy councils (FPCs) across the country played a vital role in organizing coordinated food responses across multiple sectors. We used a social network analysis (SNA) approach to investigate: (1) the network of partnering organizations and agencies within FPCs; (2) how the characteristics of FPCs' network partnerships (i.e., degree, coreness, and density) related to programmatic, policy, and advocacy actions in response to the pandemic; and (3) how FPCs' use of a racial or social equity framework shifted their network partnerships and responses. Local government agencies and food supply chain actors were core in FPCs' network partnerships, while public utilities, correctional facilities, social justice groups, and others were non-core partners. Network density was more likely to be associated with any action by FPCs, and it was especially pronounced for advocacy actions taken by FPCs; trends were similar among FPCs that reported using a racial or social equity framework. The findings begin to uncover core actors in FPCs' partnerships and opportunities to establish new partnerships, particularly with social justice groups. The results also suggest that network density (interconnectedness) may be more important than other network characteristics when responding to food-related needs.

Integrated omics approach reveals the molecular pathways activated in tomato by Kocuria rhizophila, a soil plant growth-promoting bacterium.

Plant microbial biostimulants application has become a promising and eco-friendly agricultural strategy to improve crop yields, reducing chemical inputs for more sustainable cropping systems. The soil dwelling bacterium Kocuria rhizophila was previously characterized as Plant Growth Promoting Bacteria (PGPB) for its multiple PGP traits, such as indole-3-acetic acid production, phosphate solubilization capability and salt and drought stress tolerance. Here, we evaluated by a multi-omics approach, the PGP activity of K. rhizophila on tomato, revealing the molecular pathways by which it promotes plant growth. Transcriptomic analysis showed several up-regulated genes mainly related to amino acid metabolism, cell wall organization, lipid and secondary metabolism, together with a modulation in the DNA methylation profile, after PGPB inoculation. In agreement, proteins involved in photosynthesis, cell division, and plant growth were highly accumulated by K. rhizophila. Furthermore, "amino acid and peptides", "monosaccharides", and "TCA" classes of metabolites resulted the most affected by PGPB treatment, as well as dopamine, a catecholamine neurotransmitter mediating plant growth through S-adenosylmethionine decarboxylase (SAMDC), a gene enhancing the vegetative growth, up-regulated in tomato by K. rhizophila treatment. Interestingly, eight gene modules well correlated with differentially accumulated proteins (DAPs) and metabolites (DAMs), among which two modules showed the highest correlation with nine proteins, including a nucleoside diphosphate kinase, and cytosolic ascorbate peroxidase, as well as with several amino acids and metabolites involved in TCA cycle. Overall, our findings highlighted that sugars and amino acids, energy regulators, involved in tomato plant growth, were strongly modulated by the K. rhizophila-plant interaction.

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.

Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links through Daily Activity Patterns and Brain Connectivity Analysis.

BACKGROUND: Better understanding apathy in late-life depression (LLD) would help predicting poor prognosis of the disease such as dementia. Actimetry provides an objective and ecological measure of apathy from patients' daily motor activity. We aimed to determine if patterns of motor activity were associated with apathy and brain connectivity in networks underlying goal-directed behaviors. METHODS: Resting-state functional MRI and diffusion MRI were collected from 38 non-demented LLD subjects. Apathy was evaluated using the diagnostic criteria for apathy, the apathy evaluation scale (AES) and the apathy motivation index (AMI). Functional principal components (fPC) of motor activity were derived from actimetry recordings of 72 hours. Associations between fPC and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPC were identified via the threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry. RESULTS: We found two fPC associated with apathy: mean diurnal activity, negatively associated with AES, and an early chronotype, negatively associated with AMI. Mean diurnal activity was associated with increased connectivity in the default-mode, the cingulo-opercular and the frontoparietal networks, while chronotype was associated with a more heterogenous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs. CONCLUSION: Our findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in LLD, associated with modified functional connectivity of brain networks underlying goal-directed behaviors.

Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis.

Regarding neurophysiological and developmental findings, anxiety and depression are usual comorbidities of gastritis patients. However, research related to anxiety and depression among chronic gastritis patients was conducted on the disease level while ignoring symptoms. Hence, we rendered the network approach to reveal the symptoms of anxiety and depression among chronic gastritis patients. Three hundred and sixty-nine chronic gastritis patients (female = 139, Mage = 55.87 years) were asked to complete the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Three symptom networks and one directed acyclic graph (DAG) network were formed. First, in the anxiety network of chronic gastritis patients, dizziness was the most influential symptom. In the depression network of chronic gastritis patients, depressed affect and psychomotor retardation were the influential symptoms. Second, panic, easy fatiguability, weakness, palpitation, depressed affect, tachycardia, fatigue, and psychomotor agitation bridged the anxiety-depression network of chronic gastritis patients. Third, DAG networks showed that anxiousness and hopelessness could trigger other symptoms in the anxiety-depression networks of chronic gastritis patients. The current study provided insightful information on patients with chronic gastritis by examining the structures of symptoms.

WEB OF TRANSFORMATIONS: impact of multicomponent intervention on the relationship between sociodemographic indicators, changes in body composition, cardiorespiratory fitness and biochemical markers in adolescents with overweight and obesity.

The study aims to analyze the relationships between changes after multicomponent intervention in sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers in overweight/obese adolescents. Quasi-experimental study with 33 overweight/obese adolescents (17 in the intervention group (IG) and 16 in the control group (16)), in which the GI participated in the multicomponent intervention for 24 weeks. Sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers were evaluated. Network analysis was performed using JASP software. In GI, the reduction in %BF proved to be the variable with greater connectivity and strength in the network compared to the control network. Changes in %BF were related to changes in ACR, BMI and leptin. It is concluded that the reduction in %BF is the most important variable in network relationships after the intervention, suggesting that the greater the reduction in %BF, the greater the effect on variables such as BMI, ACR and leptina.

Bridging the relationship between physical exercise and mental health in adolescents based on network analysis.

Although physical exercise has been recommended as a useful means of enhancing the mental health of adolescents, the exact mechanisms through which physical exercise plays a role are unclear. Both physical exercise and mental health are complex concepts with multiple facets, and traditional methods may constrain the manifestations of their mapping relationships. This research aimed to find the bridging connections between physical exercise and mental health. Mental health and physical exercise behaviors were assessed using the Symptom Checklist 90 (SCL-90) and the Adolescent Physical Activity Questionnaire (PAQ-A) in 9072 Chinese adolescents, respectively. Network analysis was utilized to construct the mental health-physical exercise network and to analyze the relationships between individual physical exercise behaviors and mental health symptoms. Core and bridging nodes were identified based on expected influence (EI) and bridge expected influence (BEI). Gender differences were also examined. The results revealed specific and distinct pathways between physical exercise and mental health (e.g., winter sports-obsessive-compulsive symptoms, winter sports-phobia). For both males and females, anxiety, depression, interpersonal sensitivity, ball sports, and evening activity were the most central symptoms/behaviors, reflecting their relative significance in their respective associations. The nodes with the highest BEI were obsessive-compulsive symptoms and physical education, showing negative associations with nodes in the other community. Furthermore, in the male group, somatization and winter sports stood out as the most positive bridge nodes. Conversely, in the female group, interpersonal sensitivity and sports games were the most positive bridge nodes. These findings illuminate the pathways linking physical exercise and mental health, supporting the implementation of physical exercise in a more elaborate way.

Transcriptome and small RNA analysis unveils novel insights into the C4 gene regulation in sugarcane.

MAIN CONCLUSION: This study reveals miRNA indirect regulation of C4 genes in sugarcane through transcription factors, highlighting potential key regulators like SsHAM3a. C4 photosynthesis is crucial for the high productivity and biomass of sugarcane, however, the miRNA regulation of C4 genes in sugarcane remains elusive. We have identified 384 miRNAs along the leaf gradients, including 293 known miRNAs and 91 novel miRNAs. Among these, 86 unique miRNAs exhibited differential expression patterns, and we identified 3511 potential expressed targets of these differentially expressed miRNAs (DEmiRNAs). Analyses using Pearson correlation coefficient (PCC) and Gene Ontology (GO) enrichment revealed that targets of miRNAs with positive correlations are integral to chlorophyll-related photosynthetic processes. In contrast, negatively correlated pairs are primarily associated with metabolic functions. It is worth noting that no C4 genes were predicted as targets of DEmiRNAs. Our application of weighted gene co-expression network analysis (WGCNA) led to a gene regulatory network (GRN) suggesting miRNAs might indirectly regulate C4 genes via transcription factors (TFs). The GRAS TF SsHAM3a emerged as a potential regulator of C4 genes, targeted by miR171y and miR171am, and exhibiting a negative correlation with miRNA expression along the leaf gradient. This study sheds light on the complex involvement of miRNAs in regulating C4 genes, offering a foundation for future research into enhancing sugarcane's photosynthetic efficiency.

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.

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.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

BACKGROUND: An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. OBJECTIVE: The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. METHODS: Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. RESULTS: Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). CONCLUSIONS: Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

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.