The Efficacy of Dietary Intake, Supplementation, and Blood Concentrations of Carotenoids in Cancer Prevention: Insights from an Umbrella Meta-Analysis.

Previous meta-analyses of multiple studies have suggested that dietary intake and blood concentrations of carotenoids, as well as dietary supplement of certain carotenoids, play a role in reducing the risk of cancer. However, the conclusions of these studies have been subject to controversy. We conducted an umbrella review of meta-analyses to comprehensively analyze and evaluate the evidence pertaining the association between carotenoids and cancer outcomes. We searched PubMed, Web of Science, Embase, and Cochrane Library databases of meta-analyses and systematic reviews up to June 2023. Our selection criteria encompassed meta-analyses of cohort and case-control studies, as well as randomized controlled clinical trials, which investigated the associations between carotenoids and cancer risk. We also determined the levels of evidence for these associations with AMSTAR 2 criteria. We included 51 eligible articles, including 198 meta-analyses for qualitative synthesis in the umbrella review. Despite the presence of moderate to high heterogeneity among the studies, dietary intake, supplementation, and blood concentrations of carotenoids were inversely associated with the risk of total cancer, and certain specific cancers of lung, digestive system, prostate, breast, head and neck, and others. Subgroup analysis also showed that individual carotenoids (α-carotene, ß-carotene, ß-cryptoxanthin, lutein, zeaxanthin, and lycopene) offer certain protection against specific types of cancers. However, high doses of carotenoid supplements, especially ß-carotene, significantly increased the risk of total cancer, lung cancer, and bladder cancer. Our umbrella meta-analysis supported that high intake of dietary carotenoids as a whole food approach could be more beneficial in reducing cancer risk. Concurrently, the findings suggest that the efficacy of single-carotenoid supplementation in cancer prevention remains a subject of controversy.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes.

PURPOSE: The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR. METHODS: A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry. RESULTS: The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients. CONCLUSION: Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression.

Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.

Exploration of novel isoxazole-fused quinone derivatives as anti-colorectal cancer agents through inhibiting STAT3 and elevating ROS level.

Colorectal cancer (CRC) is trending to be a major health problem throughout the world. Therapeutics with dual modes of action have shown latent capacity to create ideal anti-tumor activity. Signal transducer and activator of transcription 3 (STAT3) has been proved to be a potential target for the development of anti-colon cancer drug. In addition, modulation of tumor redox homeostasis through deploying exogenous reactive oxygen species (ROS)-enhancing agents has been widely applied as anti-tumor strategy. Thus, simultaneously targeting STAT3 and modulation ROS balance would offer a fresh avenue to combat CRC. In this work, we designed and synthesized a novel series of isoxazole-fused quinones, which were evaluated for their preliminary anti-proliferative activity against HCT116 cells. Among these quinones, compound 41 exerted excellent in vitro anti-tumor effect against HCT116 cell line with an IC50 value of 10.18 ± 0.4 nM. Compound 41 was proved to bind to STAT3 by using Bio-Layer Interferometry (BLI) assay, and can significantly inhibit phosphorylation of STAT3. It also elevated ROS of HCT116 cells by acting as a substrate of NQO1. Mitochondrial dysfunction, apoptosis, and cell cycle arrest, which was caused by compound 41, might be partially due to the inhibition of STAT3 phosphorylation and ROS production induced by 41. Moreover, it exhibited ideal anti-tumor activity in human colorectal cancer xenograft model and good safety profiles in vivo. Overall, this study provided a novel quinone derivative 41 with excellent anti-tumor activity by inhibiting STAT3 and elevating ROS level, and gave insights into designing novel anti-tumor therapeutics by simultaneously modulation of STAT3 and ROS.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Sex differences and dietary patterns in the association of air pollutants and hypertension.

BACKGROUND: Hypertension is one of the major public health problems in China. Limited evidence exists regarding sex differences in the association between hypertension and air pollutants, as well as the impact of dietary factors on the relationship between air pollutants and hypertension. The aim of this study was to investigate the sex-specific effects of dietary patterns on the association between fine particulate matter (PM2.5), ozone(O3) and hypertension in adults residing in Jiangsu Province of China. METHODS: A total of 3189 adults from the 2015 China Adult Chronic Disease and Nutrition Surveillance in Jiangsu Province were included in this study. PM2.5 and O3 concentrations were estimated using satellite space-time models and assigned to each participant. Dietary patterns were determined by reduced rank regression (RRR), and multivariate logistic regression was used to assess the associations of the obtained dietary patterns with air pollutants and hypertension risk. RESULTS: After adjusting for confounding variables, we found that males were more sensitive to long-term exposure to PM2.5 (Odds ratio (OR) = 1.42 95%CI:1.08,1.87), and females were more sensitive to long-term exposure to O3 (OR = 1.61 95%CI:1.15,2.23). Traditional southern pattern identified through RRR exhibited a protective effect against hypertension in males (OR = 0.73 95%CI: 0.56,1.00). The results of the interaction between dietary pattern score and PM2.5 revealed that adherence to traditional southern pattern was significantly associated with a decreased risk of hypertension in males (P < 0.05), while no significant association was observed among females. CONCLUSIONS: Our findings suggested that sex differences existed in the association between dietary patterns, air pollutants and hypertension. Furthermore, we found that adherence to traditional southern pattern may mitigate the risk of long-term PM2.5 exposure-induced hypertension in males.

[Genetic analysis of a child with Multiple congenital anomalies-hypotonia-seizures syndrome 1 due to variant of PIGN gene].

OBJECTIVE: To analyze the clinical phenotype and genetic etiology of a child with Multiple congenital anomalies-hypotonia-seizures syndrome 1 (MCAHS1). METHODS: Clinical data of a 2-year-old boy who had presented at the Affiliated Hospital of Qingdao University in March 2023 for "intermittent limb twitching for 2 years" was collected. Peripheral blood samples were collected from the child and his parents for whole-exome sequencing (WES). Candidate variants were verified by Sanger sequencing and bioinformatic analysis based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). RESULTS: The child had manifested with distinctive facial features, limb deformities, hypotonia, motor and intellectual delays, and epileptic seizures. WES revealed that he has harbored compound heterozygous variants of the PIGN gene, namely c.963G>A (p.Q321=) and c.994A>T (p.I332F), which were inherited from his phenotypically normal mother and father, respectively. Based on the ACMG guidelines, the c.963G>A was classified as a pathogenic variant (PVS1+PM2_Supporting+PM3), whilst the c.994A>T was classified as a variant of uncertain significance (PM2_Supporting+PP3). CONCLUSION: Above discovery has expanded the mutational spectrum of the PIGN gene variants associated with MCAHS1, which may facilitate delineation of its genotype-phenotype correlation.

Rational Design of a Novel Class of Human ClpP Agonists through a Ring-Opening Strategy with Enhanced Antileukemia Activity.

The activation of Homo sapiens Casein lysing protease P (HsClpP) by a chemical or genetic strategy has been proved to be a new potential therapy in acute myeloid leukemia (AML). However, limited efficacy has been achieved with classic agonist imipridone ONC201. Here, a novel class of HsClpP agonists is designed and synthesized using a ring-opening strategy based on the lead compound 1 reported in our previous study. Among these novel scaffold agonists, compound 7k exhibited remarkably enhanced proteolytic activity of HsClpP (EC50 = 0.79 ± 0.03 µM) and antitumor activity in vitro (IC50 = 0.038 ± 0.003 µM). Moreover, the intraperitoneal administration of compound 7k markedly suppressed tumor growth in Mv4-11 xenograft models, achieving a tumor growth inhibition rate of 88%. Concurrently, 7k displayed advantageous pharmacokinetic properties in vivo. This study underscores the promise of compound 7k as a significant HsClpP agonist and an antileukemia drug candidate, warranting further exploration for AML treatment.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression.

Importance: Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury and suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the brain alterations in adolescent MDD focused on single-modal images or analyzed images of different modalities separately, ignoring the potential role of aberrant interactions between brain structure and function in the psychopathology. Objective: To examine alterations of structural and functional connectivity (SC-FC) coupling in adolescent MDD by integrating both diffusion magnetic resonance imaging (MRI) and resting-state functional MRI data. Design, Setting, and Participants: This cross-sectional study recruited participants aged 10 to 18 years from January 2, 2020, to December 28, 2021. Patients with first-episode MDD were recruited from the outpatient psychiatry clinics at The First Affiliated Hospital of Chongqing Medical University. Healthy controls were recruited by local media advertisement from the general population in Chongqing, China. The sample was divided into 5 subgroup pairs according to different environmental stressors and clinical characteristics. Data were analyzed from January 10, 2022, to February 20, 2023. Main Outcomes and Measures: The SC-FC coupling was calculated for each brain region of each participant using whole-brain SC and FC. Primary analyses included the group differences in SC-FC coupling and clinical symptom associations between SC-FC coupling and participants with adolescent MDD and healthy controls. Secondary analyses included differences among 5 types of MDD subgroups: with or without suicide attempt, with or without nonsuicidal self-injury behavior, with or without major life events, with or without childhood trauma, and with or without school bullying. Results: Final analyses examined SC-FC coupling of 168 participants with adolescent MDD (mean [mean absolute deviation (MAD)] age, 16.0 [1.7] years; 124 females [73.8%]) and 101 healthy controls (mean [MAD] age, 15.1 [2.4] years; 61 females [60.4%]). Adolescent MDD showed increased SC-FC coupling in the visual network, default mode network, and insula (Cohen d ranged from 0.365 to 0.581; false discovery rate [FDR]-corrected P < .05). Some subgroup-specific alterations were identified via subgroup analyses, particularly involving parahippocampal coupling decrease in participants with suicide attempt (partial η2 = 0.069; 90% CI, 0.025-0.121; FDR-corrected P = .007) and frontal-limbic coupling increase in participants with major life events (partial η2 ranged from 0.046 to 0.068; FDR-corrected P < .05). Conclusions and Relevance: Results of this cross-sectional study suggest increased SC-FC coupling in adolescent MDD, especially involving hub regions of the default mode network, visual network, and insula. The findings enrich knowledge of the aberrant brain SC-FC coupling in the psychopathology of adolescent MDD, underscoring the vulnerability of frontal-limbic SC-FC coupling to external stressors and the parahippocampal coupling in shaping future-minded behavior.

Discovery of a Novel Series of Homo sapiens Caseinolytic Protease P Agonists for Colorectal Adenocarcinoma Treatment via ATF3-Dependent Integrated Stress Response.

Homo sapiens caseinolytic protease P (HsClpP) activation is a promising strategy for colon cancer treatment. In this study, CCG1423 was identified as a selective activator of HsClpP. After optimization, NCA029 emerged as the most potent compound, with an EC50 of 0.2 µM against HsClpP. Molecular dynamics revealed that the affinity of NCA029 for the YYW aromatic network is crucial for its selectivity toward HsClpP. Furthermore, NCA029 displayed favorable pharmacokinetics and safety profiles and significantly inhibited tumor growth in HCT116 xenografts, resulting in 83.6% tumor inhibition. Mechanistically, NCA029 targeted HsClpP, inducing mitochondrial dysfunction and activating the ATF3-dependent integrated stress response, ultimately causing cell death in colorectal adenocarcinoma. These findings highlight NCA029 as an effective HsClpP activator with potential for colon cancer therapy.

Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population.

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks.

OBJECTIVE: Multi-site collaboration is essential for overcoming small-sample problems when exploring reproducible biomarkers in MRI studies. However, various scanner-specific factors dramatically reduce the cross-scanner replicability. Moreover, existing harmony methods mostly could not guarantee the improved performance of downstream tasks. METHODS: we proposed a new multi-scanner harmony framework, called 'maximum classifier discrepancy generative adversarial network', or MCD-GAN, for removing scanner effects in the original feature space while preserving substantial biological information for downstream tasks. Specifically, the adversarial generative network was utilized for persisting the structural layout of each sample, and the maximum classifier discrepancy module was introduced for regulating GAN generators by incorporating the downstream tasks. RESULTS: We compared the MCD-GAN with other state-of-the-art data harmony approaches (e.g., ComBat, CycleGAN) on simulated data and the Adolescent Brain Cognitive Development (ABCD) dataset. Results demonstrate that MCD-GAN outperformed other approaches in improving cross-scanner classification performance while preserving the anatomical layout of the original images. SIGNIFICANCE: To the best of our knowledge, the proposed MCD-GAN is the first generative model which incorporates downstream tasks while harmonizing, and is a promising solution for facilitating cross-site reproducibility in various tasks such as classification and regression.

A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants.

BACKGROUND: Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation. METHODS: We investigated the brainwide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual's FNC to that of psychiatric disorders versus healthy control references. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder and their corresponding healthy control individuals. The BRS metric was employed to assess the psychiatric risk in 2 new datasets: Adolescent Brain Cognitive Development (ABCD) Study (n = 8191) and Human Connectome Project Early Psychosis (n = 170). RESULTS: The BRS revealed a clear, reproducible gradient of FNC patterns from low to high risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD Study adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD Study participants and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD Study participants and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy control individuals. CONCLUSIONS: The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.

Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders.

BACKGROUND: The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis. METHODS: The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively. RESULTS: We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs. CONCLUSIONS: To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.

Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study.

BACKGROUND: Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS: We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS: FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS: This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.

Discovery and Mechanistic Study of Novel Mycobacterium tuberculosis ClpP1P2 Inhibitors.

Caseinolytic protease P (ClpP) responsible for the proteolysis of damaged or misfolded proteins plays a critical role in proteome homeostasis. MtbClpP1P2, a ClpP enzyme complex, is required for survival in Mycobacterium tuberculosis, and it is therefore considered as a promising target for the development of antituberculosis drugs. Here, we discovered that cediranib and some of its derivatives are potent MtbClpP1P2 inhibitors and suppress M. tuberculosis growth. Protein pull-down and loss-of-function assays validated the in situ targeting of MtbClpP1P2 by cediranib and its active derivatives. Structural and mutational studies revealed that cediranib binds to MtbClpP1P2 by binding to an allosteric pocket at the equatorial handle domain of the MtbClpP1 subunit, which represents a unique binding mode compared to other known ClpP modulators. These findings provide us insights for rational drug design of antituberculosis therapies and implications for our understanding of the biological activity of MtbClpP1P2.

ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder.

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from "each one patient + all controls" and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.

BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN: We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY RESULTS: The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. CONCLUSIONS: Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.

Data-driven multimodal fusion: approaches and applications in psychiatric research.

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.