State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.

BACKGROUND: The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS: We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS: We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS: Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION: Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Diagnosis and treatment of secondary nephrotic syndrome with rash as the first symptom: a case report.

BACKGROUND: Membranous nephropathy (MN) is a common type of nephrotic syndrome (NS) in adults, accounting for about 20-30% of cases. Although secondary to specific factors, the coexistence of MN and mantle cell lymphoma (MCL) has been scarcely reported in clinical literature. CASE PRESENTATION: A 59-year-old Chinese male was admitted to the hospital with a generalized pruritic rash with bilateral lower extremity edema, which did not improve significantly after symptomatic treatment. He had undergone renal biopsy, and the diagnosis was thought to be secondary MN (SMN), therefore, we did a lymph node biopsy on the patient and found that MN was complicated with MCL. Soon after, the patient was admitted to the hematology department for a BR chemotherapy regimen (composed of bendamustine 90 mg/m2 BSA (body surface area), rituximab 375 mg/m2 BSA and dexamethasone 5 mg), and during the post-treatment follow-up, both his symptoms and renal function improved. CONCLUSIONS: The mechanism underlying the combination of SMN and MCL remains elusive and exceedingly rare, consequently often overlooked in clinical practice. This case serves to offer valuable clinical insights for diagnosis and treatment, while emphasizing the pivotal role of renal pathology in clinical assessment.

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

BACKGROUND: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognosis prediction of CC patients using bioinformatics. METHODS: The genes differentially expressed and associated with oxidative stress were extracted with the help of "limma" packages. The model for prognosis was created using Multivariate Cox regression analysis to determine the risk related to the genes related to oxidative stress. Patients were categorized as low-risk or high-risk based on the median score. The receiver operation characteristic (ROC) and survival curves were used to evaluate the predictive effect of the prognostic signature. We utilized quantitative real-time PCR to assess the expression levels of key genes associated with oxidative stress in ovarian cancer cell lines (SKOV3, OVCAR3, and HeyA8) and normal ovarian epithelial cells (HOSEpiC). RESULTS: A signature comprising seven genes associated with oxidative stress was developed to prognosticate patients with ovarian cancer. Overall survival (OS) of the patient having CC was determined using Kaplan-Meier analysis. It was found that patient with a higher risk score had lower OS than the low-risk score. The signature of genes associated with oxidative stress was found to be independently prognostic for 1, 2, and 3 years. Further research found that the expression levels of nine hub genes had a strong association with patient outcomes. Our analysis revealed a higher expression of CX3CR1 in ovarian cancer cell lines compared with normal cells. CONCLUSIONS: To deploy a novel oxidative stress-related prognostic signature as an independent biomarker in cervical cancer, we developed and validated it.

The connection between autophagy and ferroptosis in AKI: recent advances regarding selective autophagy.

Acute kidney injury (AKI) is a significant issue in public health, displaying a high occurrence rate and mortality rate. Ferroptosis, a form of programmed cell death (PCD), is characterized by iron accumulation and intensified lipid peroxidation. Recent studies have demonstrated the pivotal significance of ferroptosis in AKI caused by diverse stimuli, including ischemia-reperfusion injury (IRI), sepsis and toxins. Autophagy, a multistep process that targets damaged organelles and macromolecules for degradation and recycling, also plays an essential role in AKI. Previous research has demonstrated that autophagy deletion in proximal tubules could aggravate tubular injury and renal function loss, indicating the protective function of autophagy in AKI. Consequently, finding ways to stimulate autophagy has become a crucial therapeutic strategy. The recent discovery of the role of selective autophagy in influencing ferroptosis has identified new therapeutic targets for AKI and has highlighted the importance of understanding the cross-talk between autophagy and ferroptosis. This study aims to provide an overview of the signaling pathways involved in ferroptosis and autophagy, focusing on the mechanisms and functions of selective autophagy and autophagy-dependent ferroptosis. We hope to establish a foundation for future investigations into the interaction between autophagy and ferroptosis in AKI as well as other diseases.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Epigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is progressively recognized as a stress-related disorder characterized by aberrant brain network dynamics, encompassing both structural and functional domains. Yet, the intricate interplay between these dynamic networks and their molecular underpinnings remains predominantly unexplored. METHODS: Both structural and functional networks were constructed using multimodal neuroimaging data from 183 MDD patients and 300 age- and gender-matched healthy controls (HC). structural-functional connectivity (SC-FC) coupling was evaluated at both the connectome- and nodal-levels. Methylation data of five HPA axis key genes, including NR3C1, FKBP5, CRHBP, CRHR1, and CRHR2, were analyzed using Illumina Infinium Methylation EPIC BeadChip. RESULTS: We observed a significant reduction in SC-FC coupling at the connectome-level in patients with MDD compared to HC. At the nodal level, we found an imbalance in SC-FC coupling, with reduced coupling in cortical regions and increased coupling in subcortical regions. Furthermore, we identified 23 differentially methylated CpG sites on the HPA axis, following adjustment for multiple comparisons and control of age, gender, and medication status. Notably, three CpG sites on NR3C1 (cg01294526, cg19457823, and cg23430507), one CpG site on FKBP5 (cg25563198), one CpG site on CRHR1 (cg26656751), and one CpG site on CRHR2 (cg18351440) exhibited significant associations with SC-FC coupling in MDD patients. CONCLUSIONS: These findings provide valuable insights into the connection between micro-scale epigenetic changes in the HPA axis and SC-FC coupling at macro-scale connectomes. They unveil the mechanisms underlying increased susceptibility to MDD resulting from chronic stress and may suggest potential pharmacological targets within the HPA-axis for MDD treatment.

Module control of network analysis in psychopathology.

The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.

The prevalence and risk factors for anxiety in frontline nurses under COVID-19 pandemic based on a large cross-sectional study using the propensity score-matched method

ABSTRACT Introduction: We determined the prevalence of anxiety and the associated risk factors in frontline nurses under COVID-19 pandemic. Methods: This cross-sectional study was conducted from February 20, 2020, to March 20, 2020, and involved 562 frontline nurses. The effective response rate was 87.68%. After propensity score matched, there were 532 participants left. Extensive characteristics, including demographics, dietary habits, life-related factors, work-related factors, and psychological factors were collected based on a self-reported questionnaire. Specific scales measured the levels of sleep quality, physical activity, anxiety, perceived organization support and psychological capital. Adjusted odds ratios and 95% confidence intervals were determined by binary paired logistic regression. Results: Of the nurses enrolled in the study, 33.60% had anxiety. Five independent risk factors were identified for anxiety: poor sleep quality (OR=1.235), experienced major events (OR=1.653), lower resilience and optimism of psychological capital (OR=0.906, and OR=0.909) and no visiting friend constantly (OR=0.629). Conclusions: This study revealed a considerable high prevalence of anxiety in frontline nurses during the COVID-19 outbreak, and identified five risk factors, which were poor sleep quality, experienced major events, lower resilience and optimism of psychological capital, and no visiting friend constantly. Protecting mental health of nurses is important for COVID-19 pandemic control and their wellbeing. These findings enrich the existing theoretical model of anxiety and demonstrated a critical need for additional strategies that could address the mental health in frontline nurses for policymakers.