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.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

BACKGROUND

Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

METHODS

We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

FINDINGS

Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.

INTERPRETATION

Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

FUNDING

National Institute for Health and Care Research.

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.

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

Mechanical communication within the microtubule through network-based analysis of tubulin dynamics

The identification of the mechanisms underlying the transfer of mechanical vibrations in protein complexes is crucial to understand how these super-assemblies are stabilized to perform specific functions within the cell. In this context, the study of the structural communication and the propagation of mechanical stimuli within the microtubule (MT) is important given the pivotal role of the latter in cell viability. In this study, we employed molecular modelling and the dynamical network analysis approaches to analyse the MT. The results highlight that β -tubulin drives the transfer of mechanical information between protofilaments (PFs), which is altered at the seam due to a different interaction pattern. Moreover, while the key residues involved in the structural communication along the PF are generally conserved, a higher diversity was observed for amino acids mediating the lateral communication. Taken together, these results might explain why MTs with different PF numbers are formed in different organisms or with different β -tubulin isotypes.

Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder

Facial emotion recognition (FER), including sadness, is altered in bipolar disorder (BD). However, the relationship between this impairment and the brain structure in BD is relatively unexplored. Furthermore, its association with clinical variables and with the subtypes of BD remains to be clarified. Twenty euthymic patients with BD type I (BD-I), 28 BD type II (BD-II), and 45 healthy controls completed a FER test and a 3D-T1-weighted magnetic resonance imaging. Gray matter volume (GMV) of the cortico-limbic regions implicated in emotional processing was estimated and their relationship with FER performance was investigated using network analysis. Patients with BD-I had worse total and sadness-related FER performance relative to the other groups. Total FER performance was significantly negatively associated with illness duration and positively associated with global functioning in patients with BD-I. Sadness-related FER performance was also significantly negatively associated with the number of previous manic episodes. Network analysis showed a reduced association of the GMV of the frontal-insular-occipital areas in patients with BD-I, with a greater edge strength between sadness-related FER performance and amygdala GMV relative to controls. Our results suggest that FER performance, particularly for facial sadness, may be distinctively impaired in patients with BD-I. The pattern of reduced interrelationship in the frontal-insular-occipital regions and a stronger positive relationship between facial sadness recognition and the amygdala GMV in BD may reflect altered cortical modulation of limbic structures that ultimately predisposes to emotional dysregulation. Future longitudinal studies investigating the effect of mood state on FER performance in BD are warranted.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BACKGROUND

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS

This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS

Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS

Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS

Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Interplay between childhood trauma, bodily self-disturbances, and clinical phenomena in schizophrenia spectrum disorders: A network analysis

BACKGROUND

Bodily self-disturbances have long been considered central to schizophrenia. Exposure to childhood trauma has been linked to the development of both psychosis and bodily self-disturbances, yet little work has examined the role of bodily self-disturbances in the relationship between childhood trauma and schizophrenia symptomatology. This study uses network analysis to bridge this gap.

METHODS

Networks were constructed to examine relationships between schizophrenia symptoms (Positive and Negative Symptom Scale; PANSS), bodily self-disturbances (Perceptual Aberration Scale; PAS), and self-reported exposure to childhood trauma (Childhood Trauma Questionnaire, Short-Form; CTQ-SF) in 152 people with a schizophrenia-spectrum disorder. Shortest path and bridge analyses were conducted to assess the role of bodily self-disturbances in linking childhood trauma to schizophrenia symptomatology. Three networks were constructed: 1) PAS, childhood trauma, and PANSS sub-scale composites (positive, negative, general); 2) PAS, childhood trauma, and positive symptoms, 3) PAS, childhood trauma, and distress symptoms.

RESULTS

Shortest path analysis revealed that bodily self-disturbances were on the shortest path between childhood trauma and positive and general symptoms (Network 1), between trauma and hallucinations (Network 2), and between trauma and depression (Network 3). Bodily self-disturbances were also found to serve as a bridge between childhood trauma and positive symptoms of schizophrenia, particularly delusions and hallucinations.

CONCLUSIONS

Using a novel, data-driven approach, we showed that bodily self-disturbances play a key role in linking childhood trauma to positive and co-morbid affective symptoms of schizophrenia. Threat experiences (i.e., abuse) specifically relate to bodily self-disturbances and psychotic symptoms.

Identifying central symptom clusters and correlates in patients with lung cancer post-chemotherapy: A network analysis

Objective

This study aims to investigate the network structures of symptoms and symptom clusters in patients with lung cancer post-chemotherapy, with a focus on identifying the central symptom cluster. Understanding the central cluster is crucial for targeted and effective symptom management.

Methods

Symptom occurrence and severity were assessed using the Memorial Symptom Assessment Scale (MSAS). Principal component analysis (PCA) was employed to explore symptom clusters, while network analysis unveiled the network structure and pinpointed the central symptom cluster.

Results

The study included 512 patients with lung cancer. Four distinct symptom clusters emerged: sickness behavior, psychological, lung cancer-specific, and epithelial. The sickness behavior symptom cluster was identified as the central symptom cluster.

Conclusions

This research designates the sickness behavior symptom cluster as central in post-chemotherapy patients with lung cancer, offering valuable insights for clinical nurses in devising more effective symptom management strategies.

Trial registration

ChiCTR2300070944 (Chinese Clinical Trial Register).

Network Analysis Revealed the Role of Helplessness as a Central Feature Among Late-Life Depressive Symptoms in Patients With Mild Cognitive Impairment and Early Stage Dementia

OBJECTIVE

It has been reported that depressive symptoms in older adults are different from those in younger adults, especially when accompanied by cognitive decline. However, few studies have investigated the network structure of depressive symptoms in this population.

METHODS

The participants consisted of 627 older adults (>60 yr) who were diagnosed with mild cognitive impairment (MCI) or early stage dementia. Among them, 36.7% were male and the mean age was 76.20±7.71 years. The Korean form of Geriatric Depression Scale (KGDS) was used to evaluate their depressive symptoms and network analyses were performed using bootnet R-package to identify the central features among depressive symptoms.

RESULTS

Of all the KGDS items, we found that KGDS 2 (often feel helpless) had the highest node strength followed by KGDS 21 (in good spirits), KGDS 14 (not confident at all), and KGDS 15 (cheerful and happy). In terms of node betweenness, KGDS 2 also showed the highest value. The edge weights of edges connected to node KGDS 2 were strongest in KGDS 3 (restless and fidgety) and KGDS 28 (easily get tired).

CONCLUSION

In this study, we presented which symptoms are central among the elderly with MCI and early stage dementia. This result not only increases the understanding of depressive symptoms in this group but would also help determine target symptoms in the treatment program.

Unpacking the overlap between Autism and ADHD in adults: A multi-method approach

The overlap between Autism and Attention-Deficit Hyperactivity Disorder (ADHD) is widely observed in clinical settings, with growing interest in their co-occurrence in neurodiversity research. Until relatively recently, however, concurrent diagnoses of Autism and ADHD were not possible. This has limited the scope for large-scale research on their cross-condition associations, further stymied by a dearth of open science practices in the neurodiversity field. Additionally, almost all previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions. Tackling these limitations in previous research, 5504 adults - including a nationally representative sample of the UK (Study 1; n = 504) and a large pre-registered study (Study 2; n = 5000) - completed well-established self-report measures of Autism and ADHD traits. A series of network analyses unpacked the associations between Autism and ADHD at the individual trait level. Low inter-item connectivity was consistently found between conditions, supporting the distinction between Autism and ADHD as separable constructs. Subjective social enjoyment and hyperactivity-impulsivity traits were most condition-specific to Autism and ADHD, respectively. Traits related to attention control showed the greatest Bridge Expected Influence across conditions, revealing a potential transdiagnostic process underlying the overlap between Autism and ADHD. To investigate this further at the cognitive level, participants completed a large, well-powered, and pre-registered study measuring the relative contributions of Autism and ADHD traits to attention control (Study 3; n = 500). We detected age- and sex-related effects, however, attention control did not account for the covariance between Autism and ADHD traits. We situate our findings and discuss future directions in the cognitive science of Autism, ADHD, and neurodiversity, noting how our open datasets may be used in future research.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder

BACKGROUND

A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.

METHODS

A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.

RESULTS

In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.

CONCLUSIONS

This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

PURPOSE

Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.

METHODS

This scoping review followed the five-stage framework of Arksey and O'Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases.

RESULTS

Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies.

IMPLICATIONS FOR CANCER SURVIVORS

Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

Strategies for regulating the intensity of different cyanobacterial blooms: Insights from the dynamics and stability of bacterioplankton communities

The occurrence of cyanobacterial blooms is increasing in frequency and magnitude due to climate change and human activities, which poses a direct threat to drinking water security. The impacts of abiotic and biotic factors on the development of blooms have been well studied; however, control strategies for different bloom intensities have rarely been explored from the perspective of the dynamics and stability of bacterioplankton communities. Here, a network analysis was used to investigate the interactions and stability of microbial communities during different periods of R. raciborskii bloom in an inland freshwater lake. The abundance and diversity of rare taxa were significantly higher than that of abundant taxa throughout the bloom cycle. At the pre-bloom (PB) stage, microbial interactions among the different bacterial groups were weak but strongly negatively correlated, indicating low robustness and weak disturbance resistance within the community. However, community stability was better, and microbial interactions became more complicated at the high-bloom (HB) and low-bloom (LB) stages. Interestingly, rare taxa were significantly responsible for community stability and connectivity despite their low relative abundance. The Mantel test revealed that Secchi depth (SD), orthophosphate (PO43--P), and dissolved oxygen (DO) were significantly positively correlated with abundant taxa, rare taxa and PB. DO was significantly positively correlated with HB, intermediate taxa, and rare taxa, while water temperature (WT), N/P and total nitrogen (TN) were significantly positively correlated with LB, abundant taxa, intermediate taxa, and rare taxa. These findings suggest that reducing the PO43--P concentration at the PB stage may be an effective approach to preventing the development of R. raciborskii blooms, while regulating rare taxa at the HB and LB stages may be a key factor in controlling R. raciborskii blooms.

Contemporaneous and temporal network analysis of complex Posttraumatic stress disorder among Chinese college students with Childhood adversity: A longitudinal study

OBJECTIVE

To enhance understanding of the longitudinal progression of complex posttraumatic stress disorder (CPTSD) symptoms, this longitudinal study examined how CPTSD symptoms interact over time in Chinese college students with childhood trauma.

METHODS

From 18,933 college students who took part in two surveys 12 months apart, 4,006 participants who reported adverse childhood experiences were screened. Cross-sectional network comparisons and cross-lagged panel network (CLPN) analysis characterized interactions among CPTSD symptoms.

RESULTS

In the cross-sectional networks, feeling like a failure and avoid activities reminiscent of the trauma were the central symptoms. Takes long time to calm down and exaggerated startle are important bridge symptoms in the two networks respectively. The comparison of cross-sectional networks indicates that the global network strength was stable. The findings of the CLPN model reveal that feel worthless and feel like a failure had the highest "out" expected influence; exaggerated startle and avoid thoughts and feelings about the trauma had the highest "in" expected influence.

CONCLUSIONS

By conducting cross-sectional network analyses, the study illuminated the attributes of CPTSD networks across various time points. Additionally, the CLPN analysis uncovered the longitudinal patterns of CPTSD symptoms.

Network evolution of core symptoms after lung cancer thoracoscopic surgery:A dynamic network analysis

OBJECTIVES

To investigate relationships between various symptoms occurring 1-2 and 5-6 days following days after thoracoscopic surgery, to identify core symptoms, and to monitor changes in core symptoms over time following lung cancer thoracoscopic surgery.

METHODS

We evaluated symptoms using the Anderson Symptom Scale (Chinese version) and the Lung Cancer-Specific Symptoms Template in 214 lung cancer patients hospitalized in the Department of Thoracic Surgery of a provincial hospital in Jiangsu Province from March 2023 to September 2023. Data was collected at 1-2 days and 5-6 days postoperatively. Symptom networks were constructed for each time point, and centrality indicators were analyzed to identify core symptoms while controlling for influencing factors.

RESULTS

According to the network analysis, fatigue (rs = 26.00、rc = 0.05、rb = 1.02) had the highest strength, closeness, and betweenness in the symptom network 1-2 days after lung cancer surgery. At 5-6 days after surgery, shortness of breath (rs = 27.00) emerged as the symptom with the highest strength, fatigue (rc = 0.04) had the highest closeness, and cough (rb = 1.08) ranked highest in betweenness within the symptom network.

CONCLUSION

Fatigue stands out as the most core symptom in the network 1-2 days after lung cancer surgery. Shortness of breath, fatigue and cough are the most core symptoms in the symptom network 5-6 days after surgery. Therefore, clinical staff can improve the postoperative symptom experience of lung cancer patients by developing symptom management programmes tailored to these core symptoms.

Deciphering the interplay between psychopathological symptoms, sensorimotor, cognitive and global functioning: a transdiagnostic network analysis

BACKGROUND

Understanding the relationship between psychopathology and major domains of human neurobehavioral functioning may identify new transdiagnostic treatment targets. However, studies examining the interrelationship between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample are lacking. We hypothesized a close relationship between sensorimotor and cognitive functioning in a transdiagnostic patient sample.

METHODS

We applied network analysis and community detection methods to examine the interplay and centrality [expected influence (EI) and strength] between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample consisting of 174 schizophrenia spectrum (SSD) and 38 mood disorder (MOD) patients. All patients (n = 212) were examined with the Positive and Negative Syndrome Scale (PANSS), the Heidelberg Neurological Soft Signs Scale (NSS), the Global Assessment of Functioning (GAF), and the Brief Cognitive Assessment Tool for Schizophrenia consisted of trail making test B (TMT-B), category fluency (CF) and digit symbol substitution test (DSST).

RESULTS

NSS showed closer connections with TMT-B, CF, and DSST than with GAF and PANSS. DSST, PANSS general, and NSS motor coordination scores showed the highest EI. Sensory integration, DSST, and CF showed the highest strength.

CONCLUSIONS

The close connection between sensorimotor and cognitive impairment as well as the high centrality of sensorimotor symptoms suggests that both domains share aspects of SSD and MOD pathophysiology. But, because the majority of the study population was diagnosed with SSD, the question as to whether sensorimotor symptoms are really a transdiagnostic therapeutic target needs to be examined in future studies including more balanced diagnostic groups.

Global abundance patterns, diversity, and ecology of Patescibacteria in wastewater treatment plants

BACKGROUND

Microorganisms are responsible for nutrient removal and resource recovery in wastewater treatment plants (WWTPs), and their diversity is often studied by 16S rRNA gene amplicon sequencing. However, this approach underestimates the abundance and diversity of Patescibacteria due to the low coverage of commonly used PCR primers for this highly divergent bacterial phylum. Therefore, our current understanding of the global diversity, distribution, and ecological role of Patescibacteria in WWTPs is very incomplete. This is particularly relevant as Patescibacteria are considered to be associated with microbial host cells and can therefore influence the abundance and temporal variability of other microbial groups that are important for WWTP functioning.

RESULTS

Here, we evaluated the in silico coverage of widely used 16S rRNA gene-targeted primer pairs and redesigned a primer pair targeting the V4 region of bacterial and archaeal 16S rRNA genes to expand its coverage for Patescibacteria. We then experimentally evaluated and compared the performance of the original and modified V4-targeted primers on 565 WWTP samples from the MiDAS global sample collection. Using the modified primer pair, the percentage of ASVs classified as Patescibacteria increased from 5.9 to 23.8%, and the number of detected patescibacterial genera increased from 560 to 1576, while the detected diversity of the remaining microbial community remained similar. Due to this significantly improved coverage of Patescibacteria, we identified 23 core genera of Patescibacteria in WWTPs and described the global distribution pattern of these unusual microbes in these systems. Finally, correlation network analysis revealed potential host organisms that might be associated with Patescibacteria in WWTPs. Interestingly, strong indications were found for an association between Patescibacteria of the Saccharimonadia and globally abundant polyphosphate-accumulating organisms of the genus Ca. Phosphoribacter.

CONCLUSIONS

Our study (i) provides an improved 16S rRNA gene V4 region-targeted amplicon primer pair inclusive of Patescibacteria with little impact on the detection of other taxa, (ii) reveals the diversity and distribution patterns of Patescibacteria in WWTPs on a global scale, and (iii) provides new insights into the ecological role and potential hosts of Patescibacteria in WWTPs. Video Abstract.

A network analysis of psychopathology in young Black children: Implications for predicting outcomes in adolescence

OBJECTIVE

Network analysis may identify specific symptoms involved in the maintenance and development of psychopathology. This approach, however, has not been applied to the study of young Black children, a population facing unique challenges and developmental risks. It is also unclear whether network analysis identifies early symptoms in Black children that are linked to their longer-term difficulties and strengths in adolescence.

METHODS

We conducted a network analysis of emotional and behavioral difficulties in 1238 Black (non-Hispanic) children from the age-3 assessment in the Future of Families and Child Wellbeing Study (47 % female). We also explored whether early childhood symptoms predict subsequent caregiver-reported internalizing and externalizing problems, and youth-reported social competencies and extracurricular and community involvement, at the age-15 assessment.

RESULTS

We identified specific symptoms of externalizing and emotional reactivity as central in the network. Symptoms of emotional reactivity were also involved in comorbidity, bridging different communities of symptoms. Using elastic net models, we identified specific central and bridge symptoms, but also peripheral network symptoms, that contributed uniquely to the prediction of internalizing and externalizing problems in adolescence. Early childhood symptoms were less predictive of positive outcomes in adolescence.

CONCLUSIONS

This study identified central and bridge symptoms in young Black children, an underrepresented population in network analysis research. Some of these central and bridge symptoms, but also peripheral network symptoms, may be useful targets in early interventions to prevent long-term difficulties. Conversely, network approaches to understanding early psychopathology may have less utility for predicting Black children's subsequent strengths in adolescence.

Risk factors associated with depression and anxiety among the Chinese general population after retracting the dynamic zero-COVID policy: A network analysis

BACKGROUND

Recent studies have evidenced the negative psychological consequences of the COVID-19 pandemic and sociodemographic vulnerability among the general population, while limited information was available on which factors make the greatest contribution to psychological distress when these factors were considered concurrently. Herein, we aimed to investigate the pathways that underlie psychological distress in the context of retracting dynamic zero-COVID policy.

METHODS

We employed the mixed graphical model to construct the network of depression (PHQ-9), anxiety (GAD-7), and pandemic-related factors in a general population sample (N = 1610). Then, we re-examined the network by adding sociodemographic variables to further explore the influence of sociodemographic factors. Additionally, we repeated the analyses in the second sample (N = 620) collected in the same period to assess the replicability.

RESULTS

The relationships between the pandemic factors and anxiety and depressive symptoms exhibited a tendency to decrease after adding demographic variables, and income became the most important node and shared edge weights with all anxiety and depressive symptoms. These findings were replicable with the second sample. No significant difference in the network properties was detected between the two samples.

LIMITATIONS

The cross-sectional design limits the ability to observe longitudinal changes in these risk factors and their relationship with psychological distress.

CONCLUSIONS

Income level, rather than the pandemic-related factors, acted as a vital role in the psychological distress of the general population, implying that livelihood issues may be the critical intervention targets for mental health during the post-pandemic period.

Stressful life events exhibit complex patterns of associations with depressive symptoms in two population-based samples using network analysis

BACKGROUND

Stressful life events (SLEs) constitute key risk factors for depression. However, previous studies examining associations between SLEs and depression have been limited by focusing on single events, combining events into broad categories, and/or ignoring interrelationships between events in statistical analyses. Network analysis comprises a set of statistical methods well-suited for assessing relationships between multiple variables and can help surpass several limitations of previous studies.

METHODS

We applied network analysis using mixed graphical models combining two large-scale population-based samples and >34,600 randomly sampled adults to investigate the associations between SLEs and current depressive symptoms in the general population.

RESULTS

Numerous SLEs were uniquely associated with specific symptoms. Strong pairwise links were observed between SLEs during the past year and individual symptoms, e.g., between having experienced illness or injury and sleeping problems, having been degraded or humiliated and feeling blue, and between financial problems and hopelessness and being worried and anxious. Several SLEs, such as financial problems, sexual abuse, and having been degraded or humiliated, were associated with symptoms across more than one timepoint. More recent SLEs were generally more strongly associated with depressive symptoms. Several life events were strongly interrelated, such as multiple forms of abuse, and financial problems, unemployment, divorce, and serious illness or injury.

LIMITATIONS

Limitations include a retrospective SLE measure, cross-sectional data, a brief self-report measure of depressive symptoms, and possible attrition bias in the sample.

CONCLUSIONS

Our findings may have implications for public health efforts seeking to improve population mental health.

Characterization of heavy metal, antibiotic pollution, and their resistance genes in paddy with secondary municipal-treated wastewater irrigation

Secondary municipal-treated wastewater irrigation may introduce residual antibiotics into the agricultural systems contaminated with certain heavy metals, ultimately leading to the coexistence of antibiotics and heavy metals. The coexistence may induce synergistic resistance to both in the microbial community. Here, we investigated the effects of long-term municipal-treated irrigation for rice on the microbiome and resistome. The results showed that the target antibiotics were undetectable in edible grains, and the heavy metal concentrations did not exceed the standard in edible rice grains. Heavy metal resistance genes (MRGs) ruvB and acn antibiotic resistance genes (ARGs) sul1 and sul2 were the dominating resistant genes. The coexistence of antibiotics and heavy metals affected the microbial community and promoted metal and antibiotic resistance. Network analysis revealed that Proteobacteria were the most influential hosts for MRGs, ARGs, and integrons, and co-selection may serve as a potential mechanism for resistance maintenance. MRG czcA and ARG sul1 can be recommended as model genes to study the co-selection of ARGs and MRGs in environments. The obtained results highlight the importance of considering the co-occurrence of heavy metals and antibiotics while developing effective methods to prevent the transmission of ARGs. These findings are critical for assessing the possible human health concerns associated with secondary municipal-treated wastewater irrigation for agriculture and improving the understanding of the coexistence of heavy metals and antibiotics.

Molecular insight into the vertical migration and degradation of dissolved organic matter in riparian soil profiles

Due to the molecular complexity of dissolving organic matter (DOM), the vertical molecular distribution of riparian soil DOM (especially dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP)) in different land use types and their relationship with the bacterial community is still unclear. This study analyzed the spectral characteristics of riparian soil DOM from 0 to 100 cm in wild grassland, agricultural land, and bare land. The molecular distribution of DOM was revealed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and the specific relationship between DOM and bacterial community composition (BCC) was evaluated. The results showed that the DOM in the upper soil layer (0-40 cm) was mainly composed of recalcitrant macromolecular organics, while that in the lower layer (40-100 cm) was labile small molecular organics. In agricultural land, the total storage of DOM was lower than that in wild grassland, but with a higher abundance of recalcitrant organic carbon (lignin, etc.). At the same time, the bacterial community in agricultural land is shifting towards copiotrophs. In addition, the abundance of labile C degrading genes increases with nitrate as the main electron acceptor. However, sulfates are mainly used as electron acceptors in wild grasslands. Both DOP and DON were dominated by lignin and displayed higher chemical diversity in the upper soil. The bioavailability of DOP in three types of soil is higher than that of DON. DOM-BCC network analysis shows that the recalcitrant DON and DOP molecules in soil are positively correlated with phylum Actinobacteriota in agricultural land. These results emphasize that the DOM molecular characteristics were closely related to the function of the soil bacterial community.

Sentinel and networked symptoms in patients with breast cancer undergoing chemotherapy

PURPOSE

It was designed to identify the symptom clusters and sentinel symptoms among patients with breast cancer receiving chemotherapy at the community level, and to explore core and bridge symptoms at the global level.

METHODS

A cross-sectional survey was conducted using the MD Anderson Symptom Inventory. Patients with breast cancer receiving chemotherapy, recruited from the "Be Resilient to Breast Cancer" project between January 2023 and December 2023, were included in the study. Symptom clusters and their sentinel symptoms were identified using exploratory factor analysis and Apriori algorithm. Core and bridge symptoms were identified using network analysis.

RESULTS

A total of 468 patients with breast cancer participated in the current study. At the community level, three symptom clusters and their corresponding sentinel symptoms were identified: a gastrointestinal symptom cluster (with nausea as the sentinel symptom), a psycho-sleep-related symptom cluster (with distress as the sentinel symptom), and a neurocognition symptom cluster (with dry mouth as the sentinel symptom). At the global level, fatigue emerged as the core symptom, while disturbed sleep and lack of appetite as bridge symptoms.

CONCLUSION

Addressing nausea, distress, and dry mouth are imperative for alleviating specific symptom clusters at the community level. Furthermore, targeting fatigue, disturbed sleep, and lack of appetite are crucial to break the interactions among diverse symptoms at the global level.

Ecological evolution of algae in connected reservoirs under the influence of water transfer: Algal density, community structure, and assembly processes

Reservoir connectivity provides a solution for regional water shortages. Understanding the water quality of reservoirs and the response of algal communities to water transfer could provide the basis for a long-term evolutionary model of reservoirs. In this study, a water-algal community model was established to study the effects of water transfer on water quality and algal communities in reservoirs. The results showed that water transfer significantly decreased total nitrogen and nitrate concentrations. However, the water transfer resulted in an increase in the CODMn concentration and conductivity in the receiving reservoir. Additionally, the algal density and chlorophyll-a (chl-a) concentration showed an increase with water transfer. Bacillariophyta, Cyanophyta, and Chlorophyta were the dominant algal phyllum in all three reservoirs. Water transfer induced the evolution of the algal community by driving changes in the chemical parameters of the receiving reservoir and led to more complex relationships within the algal community. The effects of stochastic processes on algal communities were also enhanced in the receiving reservoirs. These results provide specific information for water quality safety management and eutrophication prevention in connected reservoirs.

Associations between anxiety, depression, and personal mastery in community-dwelling older adults: a network-based analysis

BACKGROUND

In China, about 18.70% of the population aged 60 years and older are at risk of low personal mastery as well as anxiety and depression for a variety of reasons. The purpose of this study was to construct a symptom network model of the relationship between anxiety, depression, and personal mastery in community-dwelling older adults and to identify central and bridge symptoms in this network.

METHODS

Depression, anxiety, and personal mastery were measured using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Personal Mastery Scale (PMS), respectively. A total of 501 older adults in 16 communities in Changzhou and Zhenjiang, Jiangsu Province, China, were surveyed by using a combination of stratified sampling and convenience sampling methods. The R language was used to construct the network.

RESULTS

(1) The network structure of anxiety-depression-personal mastery was stable, with "Nervousness" (node GAD1, strength = 1.38), "Sad mood" (node PHQ2, strength = 1.22), " Inability to change" (node PMS2, strength = 1.01) and "Involuntarily" (node PMS3, strength = 0.95) as the central symptoms. (2) "Irritability" (node GAD6, bridge strength = 0.743), "Sad mood" (node PHQ2, bridge strength = 0.655), and "Trouble relaxing" (node GAD4, bridge strength = 0.550) were the bridge symptoms connecting anxiety, depressive symptoms, and personal mastery. (3) In the network comparison test (NCT), residence, somatic chronic comorbidity and gender had no significant effect on network structure.

CONCLUSIONS

The construction of the anxiety-depression-personal mastery network structure opens up new possibilities for mechanisms of action and intervention formulation for psychological disorders in community-dwelling older adults. The identification of central symptoms (e.g., nervousness, sad mood, inability to change, involuntarily) and bridge symptoms (e.g., irritability, sad mood, trouble relaxing) in community-dwelling older adults with anxiety, depression, and low sense of mastery can provide a scientific basis for the development of precise interventions.

Chemotherapy-related symptoms in children with leukemia: application of latent profile analysis and network analysis

PURPOSE

Children with leukemia may experience a range of chemotherapy-related symptoms. Identifying subgroups and their distinct characteristics of symptoms may improve symptom management. We aimed to identify subgroups and their distinct characteristics of chemotherapy-related symptoms in children with leukemia.

METHODS

A cross-sectional survey was conducted among 500 children with leukemia, who completed questionnaires that assessed their demographic and clinical characteristics, as well as the Memorial Symptom Assessment Scale. Latent profile analysis was conducted to identify subgroups of symptoms. Additionally, multiple regression analysis and network analysis were utilized to reveal the characteristics of each subgroup.

RESULTS

Four subgroups were identified: "Profile 1: low symptom burden subgroup" (26.2%), "Profile 2: moderate symptom burden subgroup in transitional period" (14.8%), "Profile 3: moderate psychological symptom burden subgroup" (35.6%), and "Profile 4: high symptom burden subgroup" (23.4%). Multiple logistic regression analysis indicated that lower primary caregiver's education level, lower family monthly income, self-paid medical expenses, induction remission period, and consolidation enhancement period were associated with more severe symptoms of subgroups. Network analysis further revealed that nausea was the core symptom in Profiles 1 and 2, while the core symptom in Profile 3 was "I don't look like myself." Additionally, worrying was the core symptom in Profile 4.

CONCLUSION

There exists heterogeneity in chemotherapy-related symptoms. Four subgroups and their corresponding characteristics of children with varying symptom severity were identified. Identifying these subgroups will facilitate personalized care, maximize intervention effectiveness, and alleviate symptom burden.

eHealth tools use and mental health: a cross-sectional network analysis in a representative sample

eHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18-87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.

Multivariate statistical and bioinformatic analyses for the seasonal variations of actinobacterial community structures in a drinking water reservoir

Actinobacterial community is a conspicuous part of aquatic ecosystems and displays an important role in the case of biogeochemical cycle, but little is known about the seasonal variation of actinobacterial community in reservoir ecological environment. In this study, the high-throughput techniques were used to investigate the structure of the aquatic actinobacterial community and its inducing water quality parameters in different seasons. The results showed that the highest diversity and abundance of actinobacterial community occurred in winter, with Sporichthya (45.42%) being the most abundant genus and Rhodococcus sp. (29.32%) being the most abundant species. Network analysis and correlation analysis suggested that in autumn the dynamics of actinobacterial community were influenced by more factors and Nocardioides sp. SX2R5S2 was the potential keystone species which was negatively correlated with temperature (R = -0.72, P < 0.05). Changes in environmental factors could significantly affect the changes in actinobacterial community, and the dynamics of temperature, dissolved oxygen (DO), and turbidity are potential conspicuous factors influencing seasonal actinobacterial community trends. The partial least squares path modeling further elucidated that the combined effects of DO and temperature not only in the diversity of actinobacterial community but also in other water qualities, while the physiochemical parameters (path coefficient = 1.571, P < 0.05) was strong environmental factors in natural mixture period. These results strengthen our understanding of the dynamics and structures of actinobacterial community in the drinking water reservoirs and provide scientific guidance for further water quality management and protection in water sources.

Characterising proximal and distal drivers of antimicrobial resistance: An umbrella review

INTRODUCTION

Antimicrobial resistance (AMR) is a multifactorial challenge driven by a complex interplay of proximal drivers, such as the overuse and misuse of antimicrobials and the high burden of infectious diseases, and distal factors, encompassing broader societal conditions such as poverty, inadequate sanitation, and healthcare system deficiencies. However, distinguishing between proximal and distal drivers remains a conceptual challenge.

OBJECTIVES

We conducted an umbrella review, aiming to systematically map current evidence about proximal and distal drivers of AMR and to investigate their relationships.

METHODS

Forty-seven reviews were analysed, and unique causal links were retained to construct a causality network of AMR. To distinguish between proximal and distal drivers, we calculated a 'driver distalness index (Di)', defined as an average relative position of a driver in its causal pathways to AMR.

RESULTS

The primary emphasis of the literature remained on proximal drivers, with fragmented existing evidence about distal drivers. The network analysis showed that proximal drivers of AMR are associated with risks of resistance transmission (Di = 0.49, SD = 0.14) and antibiotic use (Di = 0.58, SD = 0.2), which are worsened by intermediate drivers linked with challenges of antibiotic discovery (Di = 0.62, SD = 0.07), infection prevention (Di = 0.67, SD = 0.14) and surveillance (Di = 0.69, SD = 0.16). Distal drivers, such as living conditions, access to sanitation infrastructure, population growth and urbanisation, and gaps in policy implementation were development and governance challenges, acting as deep leverage points in the system in addressing AMR.

CONCLUSIONS

Comprehensive AMR strategies aiming to address multiple chronic AMR challenges must take advantage of opportunities for upstream interventions that specifically address distal drivers.

Seeking arrangements: cell contact as a cleavage-stage biomarker

RESEARCH QUESTION

What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos?

DESIGN

This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed. Unpaired t-tests and receiver operating characteristic curve analysis were used to statistically analyse mean cell contact outcomes. Cell contact networks from different embryos were compared with identical embryos with similar cell arrangements.

RESULTS

At t4, a higher mean number of contacts per cell was associated with greater rates of blastulation and blastocyst quality. No associations were found with biochemical pregnancy, live birth, miscarriage or ploidy. At t8, a higher mean number of contacts was associated with increased blastocyst quality, biochemical pregnancy and live birth. No associations were found with miscarriage or aneuploidy. Mean contacts at t4 weakly correlated with those at t8. Four-cell embryos fell into nine distinct cell arrangements; the five most common accounted for 97% of embryos. Eight-cell embryos, however, displayed a greater degree of variation with 59 distinct cell arrangements.

CONCLUSIONS

Evidence is provided for the clinical relevance of cleavage-stage cell arrangement in the human preimplantation embryo beyond the four-cell stage, which may improve selection techniques for day-3 transfers. This pilot study provides a strong case for further investigation into spatial biomarkers and three-dimensional morphokinetics.

Identifying the core residual symptom in patients with major depressive disorder using network analysis and illustrating its association with prognosis: A study based on the national cohorts in China

OBJECTIVE

To identify the core residual symptom of MDD and assess its relationship with patients' long-term outcomes.

METHOD

All patients were administered antidepressants during the acute phase and treated continuously. The 521 patients remitted at month 6 of a multicenter prospective project were included. Remission was defined as a Quick Inventory of Depressive Symptoms-Self-Report total score of ≤5. Functional impairments were measured with the Sheehan Disability Scale, quality of life with the Quality of Life Enjoyment and Satisfaction Questionnaire - short form, and family burden with the Family Burden Scale of Disease. Visits were scheduled at baseline, weeks 2, 8, 12, and month 6.

RESULTS

Difficulty with concentration/decision making was the core residual symptom of MDD, determined with the centrality measure of network analysis. It was positively associated with functional impairments and family burden (r = 0.35, P < 0.01 and r = 0.31, P < 0.01, respectively) and negatively associated with life satisfaction (r = -0.29, P < 0.01). The exhibition of this residual symptom was associated with a family history of psychiatric disorders (OR = 2.610 [1.242-5.485]).

CONCLUSIONS

The core residual symptom of MDD, difficulty with concentration/decision making, is associated with poorer social functioning, heavier family burden, and lower life satisfaction. Early detection and intervention of this symptom may be beneficial.

CLINICAL TRIALS REGISTRATION NUMBER

(Chinese Clinical Trials.gov identifier) ChiCTR-OOC-17012566 and ChiCTR-INR-17012574.

Network analysis of depression and anxiety symptoms and their associations with mobile phone addiction among Chinese medical students during the late stage of the COVID-19 pandemic

Network analysis provides a novel approach to discovering associations between mental disorders at the symptom level. This study aimed to examine the characteristics of the network of depression and anxiety symptoms and their associations with mobile phone addiction (MPA) among Chinese medical students during the late stage of the COVID-19 pandemic. A total of 553 medical students were included. Depression and anxiety symptoms and MPA were measured by the nine-item Patient Health Questionnaire (PHQ-9), the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the Mobile Phone Addiction Index (MPAI), respectively. Central and bridge symptoms were identified with centrality indices and bridge centrality indices. Network stability was examined using the case-dropping procedure. "Uncontrollable worry", "restlessness" and "nervousness" were the central symptoms in the depression and anxiety network. "Restlessness" and "motor" were the most central bridge symptoms linking depression and anxiety. "Concentration", "anhedonia" and "sleep" were most strongly associated with MPA. "Uncontrollable worry", "restlessness", "nervousness," and "motor" may be the symptoms for interventions to target in medical students with comorbid depression and anxiety. From a network perspective, depressive symptoms may be more important than anxiety symptoms in medical students with MPA.

TIMP1/CHI3L1 facilitates glioma progression and immunosuppression via NF-κB activation

Gliomas are highly heterogeneous brain tumours that are resistant to therapies. The molecular signatures of gliomas play a high-ranking role in tumour prognosis and treatment. In addition, patients with gliomas with a mesenchymal phenotype manifest overpowering immunosuppression and sophisticated resistance to treatment. Thus, studies on gene/protein coexpression networks and hub genes in gliomas holds promise in determining effective treatment strategies. Therefore, in this study, we aimed to. Using average linkage hierarchical clustering, 13 modules and 224 hub genes were described. Top ten hub genes (CLIC1, EMP3, TIMP1, CCDC109B, CASP4, MSN, ANXA2P2, CHI3L1, TAGLN2, S100A11), selected from the most meaningful module, were associated with poor prognosis. String analysis, co-immunoprecipitation and immunofluorescence revealed a significant correlation between TIMP1 and CHI3L1. Furthermore, we found, both in vivo and in vitro, that TIMP1 promoted gliomagenesis via CHI3L1 overexpression as well as NF-κB activation. TIMP1 expression correlated with tumour immune infiltration and immune checkpoint-related gene expression. In addition, TIMP1 resulted in immunosuppressive macrophage polarization. In summary, TIMP1/CHI3L1 might be perceived as a diagnostic marker and an immunotherapy target for gliomas.

Temporal assembly patterns of microbial communities in three parallel bioreactors treating low-concentration coking wastewater with differing carbon source concentrations

Carbon source is an important factor of biological treatment systems, the effects of which on their temporal community assembly patterns are not sufficiently understood currently. In this study, the temporal dynamics and driving mechanisms of the communities in three parallel bioreactors for low-concentration coking wastewater (CWW) treatment with differing carbon source concentrations (S0 with no glucose addition, S1 with 200 mg/L glucose addition and S2 with 400 mg/L glucose addition) were comprehensively studied. High-throughput sequencing and bioinformatics analyses including network analysis and Infer Community Assembly Mechanisms by Phylogenetic bin-based null model (iCAMP) were used. The communities of three systems showed turnover rates of 0.0029∼0.0034 every 15 days. Network analysis results showed that the S0 network showed higher positive correlation proportion (71.43%) and clustering coefficient (0.33), suggesting that carbon source shortage in S0 promoted interactions and cooperation of microbes. The neutral community model analysis showed that the immigration rate increased from 0.5247 in S0 to 0.6478 in S2. The iCAMP analysis results showed that drift (45.89%) and homogeneous selection (31.68%) dominated in driving the assembly of all the investigated microbial communities. The contribution of homogeneous selection increased with the increase of carbon source concentrations, from 27.92% in S0 to 36.08% in S2. The OTUs participating in aerobic respiration and tricarboxylic acid (TCA) cycle were abundant among the bins mainly affected by deterministic processes, while those related to the metabolism of refractory organic pollutants in CWW such as alkanes, benzenes and phenols were abundant in the bins dominated by stochastic processes.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Further evidence for the bidimensionality of the components model of addiction: a reply to Amendola (2023)

In our original research article entitled "Deconstructing the components model of addiction: an illustration through "addictive" use of social media" (Fournier et al., 2023), we showed that the Bergen Social Media Addiction Scale, a six-item psychometric instrument derived from the components model of addiction to assess social media "addiction", did not form a unitary, but a bidimensional construct in which some components (i.e., salience, tolerance) were not associated with psychopathological symptoms, thus conflating central and peripheral features of addiction. Subsequently, in a recent commentary, Amendola (2023) sought to determine whether our findings were driven by the use of data aggregated from multiple independent datasets, i.e., a decision we transparently acknowledged as a limitation in our original research article. Following their re-analysis, Amendola (2023) claimed to have demonstrated that a unidimensional model best fitted the data. However, they only reported results for a partial set of models relevant to this investigation. In the present reply, through a transparent assessment and reporting of all unidimensional and bidimensional models relevant to this investigation, we show that the bidimensionality of the Bergen Social Media Addiction Scale is, in fact, tenable, robust, and consistent across multiple independent datasets. In line with the growing evidence demonstrating that many sets of criteria involved in operationalizing behavioral addictions pathologize involvement in appetitive behaviors, these results highlight the necessity to renew the conceptualization and assessment of behavioral addictions.

Emotion dynamics of clients with test anxiety before and after an imagery-based treatment

BACKGROUND AND OBJECTIVES

Imagery-based techniques have become a promising means in the treatment of test anxiety (TA). Although previous studies have demonstrated the effectiveness of imagery-based treatment, not all clients seem to benefit from it. The present study compares clients' pre- as well as post-treatment emotion dynamics between responders and non-responders. Furthermore, it examines treatment-related changes in emotion dynamics in both subgroups.

METHODS

The results are based on 44 clients suffering from TA who underwent a six-session imagery-based treatment and include Ecological Momentary Assessment (EMA). Emotions were assessed with the Profile of Mood States four times a day over the course of two weeks before and after the treatment. Temporal networks were computed to index emotion dynamics.

RESULTS

Pre-treatment emotion dynamics differed between responders and non-responders. Similarly, post-treatment emotion dynamics differed as well between both groups. Some changes were also observed between pre-treatment and post-treatment networks: for responders, fatigue no longer predicted anger, and depression predicted itself; for non-responders, calmness predicted fatigue, anger, depression, contentment, and anxiety. In addition, fatigue no longer predicted itself and anxiety predicted vigor.

LIMITATIONS

The investigation is marked by several limitations: a liberal inclusion threshold of at least a 50% response to EMA prompts, and a relatively homogenous sample.

CONCLUSION

These results provide first evidence for the idea that emotion dynamics may be associated with response to treatment for TA. Furthermore, effective imagery-based treatments may be tied to changes within these dynamics.