Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Clinical observation of the treatment of refractory cancer pain with cancer pain information platform and IDDS under home analgesia mode: A retrospective study.

To evaluate the effectiveness and safety of a cancer pain information platform combined with semi-implantable intrathecal drug delivery systems among the patients with refractory cancer pain under a "home analgesia" model. This was a retrospective study. A total of 49 patients underwent semi-implantable intrathecal drug delivery systems with patient-controlled analgesia in conjunction with the establishment of a cancer pain information platform. Numeric rating scales (NRS), Bruggrmann comfort scale (BCS), high-quality sleep duration, and opioid-related adverse effects were recorded at various time points and analyzed: the day on admission (T0), the day of discharge (T1), 30 days post-discharge (T2), 60 days post-discharge (T3), 90 days post-discharge (T4), 120 days post-discharge (T5), 150 days post-discharge (T6), 180 days post-discharge (T7), and the day before death (T8). Compared with T0, NRS significantly decreased and BCS significantly increased at T1 to T8 time points (P < .05). However, NRS and BCS did not show differences at T1 to T8 time points (P > .05). The duration of high-quality sleep was significantly extended, and the incidence of opioid-related adverse effects was significantly reduced. Postoperative complications included 1 case of cerebrospinal fluid leakage, 3 cases of infection at the butterfly needle insertion site, 6 cases of hospital readmission for equipment malfunction, and no cases of respiratory depression. Eleven patients continued standardized antitreatment after IDDS surgery. The mean survival time for all patients was 135.51 ±â€…102.69 days, and the survival rate at T7 was 30.61%. The cancer pain information platform combined with semi-implantable IDDS is beneficial for the pain management of refractory cancer patients under the "home analgesia" model, improving their quality of life.

Neural dysfunction underlying working memory processing at different stages of the illness course in schizophrenia: a comparative meta-analysis.

Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

Interferon-α could induce liver steatosis to promote HBsAg loss by increasing triglyceride level.

Background: The correlation between metabolic syndrome (MetS) and hepatitis B surface antigen (HBsAg) loss remains to be further elucidated, particularly in patients receiving pegylated interferon-α (PEG-IFN) treatment. Methods: 758 patients with low HBsAg quantification who had received nucleos(t)ide analog (NUC) therapy for at least one year and subsequently switched to or add on PEG-IFN therapy over an unfixed course were enrolled. 412 patients were obtained with baseline data matched. A total of 206 patients achieved HBsAg loss (cured group) within 48 weeks. Demographic and biochemical data associated with MetS were gathered for analysis. HepG2.2.15 cell line was used in vitro experiments to validate the efficacy of interferon-α (IFN-α). Results: The proportion of patients with diabetes or hypertension in the uncured group was significantly higher than in the cured group. The levels of fasting blood glucose (FBG) and glycated albumin remained elevated in the uncured group over the 48 weeks. In contrast, the levels of blood lipids and uric acid remained higher in the cured group within 48 weeks. Triglycerides levels and liver steatosis of all patients increased after PEG-IFN therapy. Baseline elevated uric acid levels and hepatic steatosis may be beneficial for HBsAg loss. IFN-α could induce hepatic steatosis and indirectly promote HBsAg loss by increasing triglyceride level through upregulation of acyl-CoA synthetase long-chain family member 1(ACSL1). Conclusions: IFN-α could induce liver steatosis to promote HBsAg loss by increasing triglyceride level through upregulation of ACSL1. Comorbid diabetes may be detrimental to obtaining HBsAg loss with PEG-IFN therapy in CHB patients.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

[Evidence mapping of traditional Chinese medicine intervention in diabetic retinopathy].

This study aims to assess the quality of evidence for the treatment of diabetic retinopathy with traditional Chinese medicine based on the systematic reviews/Meta-analyses of relevant studies. CNKI, Wanfang, VIP, SinoMed, PubMed, Web of Science, EMbase, and Cochrane Library were searched for the systematic reviews/Meta-analyses of traditional Chinese medicine interventions in diabetic retinopathy published from the inception to November 2023. A Measurement Tool to Assess Systematic Reviews 2(AMSTAR2) scale was used to assess the methodological quality of the included studies. An evidence map was built to present the information on intervention measures, the number of studies included in the systematic reviews/Meta-analyses, research conclusions, and methodological quality assessment results. A total of 51 studies were included. Traditional Chinese medicine interventions accounted for a large proportion of the intervention measures, followed by Chinese patent medicines. The treatment methods mainly included tonifying deficiency, activating blood, and resolving stasis. According to the AMSTAR2 scale assessment results, the descriptions of funding information for included studies, lists of excluded articles, and preliminary research protocols were particularly lacking. The evidence map showed that 48, 2, and 1 studies concluded with beneficial effects, possible beneficial effects, and unclear effects, respectively. On the whole, traditional Chinese medicine demonstrated definite efficacy in the treatment of diabetic retinopathy, while the evidence pre-sents moderate to low quality. It is suggested that higher-quality studies remain to be carried out to provide more evidence.

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states.

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Cardiac glycosides with cytotoxic activity from the seeds of Thevetia peruviana.

Phytochemical investigation on the extract of the seeds of Thevetia peruviana resulted in the isolation of six new cardiac glycosides, namely theveperosides A-F (1-6), including a rare 19-nor-cardenolide (1), together with seven known analogues (7-13). The chemical structures of these compounds were determined based on detailed spectroscopic analysis. The cytotoxic activities of 1-13 were evaluated against MCF-7, HCT-116, HeLa, and HepG2 cancer cell lines, and their structure-activity relationships (SARs) were investigated. Compound 3 exhibited the significant cytotoxic effects with IC50 values ranging from 0.032 to 0.055 µΜ, which could induce HepG2 cells apoptosis in a dose-dependent manner.

Leaf Segmentation Using Modified YOLOv8-Seg Models.

Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. In this paper, the YOLOv8-seg model was used for the automated segmentation of individual leaves in images. In order to improve the segmentation performance, we further introduced a Ghost module and a Bidirectional Feature Pyramid Network (BiFPN) module into the standard Yolov8 model and proposed two modified versions. The Ghost module can generate several intrinsic feature maps with cheap transformation operations, and the BiFPN module can fuse multi-scale features to improve the segmentation performance of small leaves. The experiment results show that Yolov8 performs well in the leaf segmentation task, and the Ghost module and BiFPN module can further improve the performance. Our proposed approach achieves a 86.4% leaf segmentation score (best Dice) over all five test datasets of the Computer Vision Problems in Plant Phenotyping (CVPPP) Leaf Segmentation Challenge, outperforming other reported approaches.

Regioselective 1,4-/1,3-Difunctionalization of 1,3-Enynes with Selenosulfonates in Water.

1,4-/1,3-Regioselective bifunctionalization of 1,3-enynes with selenosulfonates in water under catalyst-free conditions for the construction of sulfonyl allene and 1,3-disulfonyl-conjugated dienes respectively have been developed. The reactions feature mild reaction conditions in aqueous solution and remarkable regioselectivity controlled by substrates.

Overexpression of PavHIPP16 from Prunus avium enhances cold stress tolerance in transgenic tobacco.

BACKGROUND: The heavy metal-associated isoprenylated plant protein (HIPP) is an important regulatory element in response to abiotic stresses, especially playing a key role in low-temperature response. RESULTS: This study investigated the potential function of PavHIPP16 up-regulated in sweet cherry under cold stress by heterologous overexpression in tobacco. The results showed that the overexpression (OE) lines' growth state was better than wild type (WT), and the germination rate, root length, and fresh weight of OE lines were significantly higher than those of WT. In addition, the relative conductivity and malondialdehyde (MDA) content of the OE of tobacco under low-temperature treatment were substantially lower than those of WT. In contrast, peroxidase (POD), superoxide dismutase (SOD), catalase (CAT) activities, hydrogen peroxide (H2O2), proline, soluble protein, and soluble sugar contents were significantly higher than those of WT. Yeast two-hybrid assay (Y2H) and luciferase complementation assay verified the interactions between PavbHLH106 and PavHIPP16, suggesting that these two proteins co-regulated the cold tolerance mechanism in plants. The research results indicated that the transgenic lines could perform better under low-temperature stress by increasing the antioxidant enzyme activity and osmoregulatory substance content of the transgenic plants. CONCLUSIONS: This study provides genetic resources for analyzing the biological functions of PavHIPPs, which is important for elucidating the mechanisms of cold resistance in sweet cherry.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

Maltose Additive Enables Compacted Deposition of Zn Ions for Stabilizing the Zn Anode.

Aqueous zinc-ion batteries (AZIBs) have emerged as one of the most promising energy storage technologies due to their high safety and cost-effectiveness. However, several challenges associated with the Zn metal anode, such as dendrite growth, corrosion, and hydrogen evolution reaction (HER), have hindered further applications of AZIBs. Herein, maltose (MT) is used as a functional electrolyte additive to protect the Zn metal electrode during the interface deposition process. The additive can effectively affect the interface of Zn metal, suppressing HER and corrosion reactions. Moreover, it facilitates the uniform deposition of Zn by inducing Zn2+ to form a stable (100) crystal plane. As a result, the symmetric cell exhibited stable cycling performance for 2000 h at a current density of 2 mA cm-2, and the Zn||NH4V4O10 full cell maintained steady cycling for 1000 cycles at 2 A g-1. This study provides an approach to achieve uniform Zn deposition through additives.

[Comparison of the Prognostic Value of C-Reactive Protein to Albumin Ratio and Glasgow Prognostic Score in Patients with Diffuse Large B-Cell Lymphoma].

OBJECTIVE: To compare the prognostic value of two predictive models based on C-reactive protein (CRP) and albumin (ALB), namely the CRP to ALB ratio (CAR) and the Glasgow prognostic score (GPS), in newly diagnosed patients with diffuse large B-cell lymphoma (DLBCL). METHODS: The data of newly diagnosed DLBCL patients admitted to our center from May 2014 to January 2022 were reviewed. A total of 111 patients who completed at least 4 cycles of R-CHOP or R-CHOP-like chemotherapy with detailed clinical, laboratory data and follow-up information were included. The receiver operating characteristic (ROC) curve was performed to evaluate the predictive value of pre-treatment CAR on disease progression and survival. Furthermore, the association between CAR and baseline clinical, laboratory characteristics of patients was evaluated, and progression-free survival (PFS) and overall survival (OS) were compared between different CAR and GPS subgroups. Finally, the univariate and multivariate COX propor-tional hazard regression models were used to analyze the factors affecting disease outcomes. RESULTS: ROC curve showed that the area under the curve (AUC) of CAR predicting PFS and OS in DLBCL patients was 0.687 (P =0.002) and 0.695 (P =0.005), respectively, with the optimal cut-off value of 0.11 for both predicting PFS and OS. Compared with the lower CAR (<0.11) group, the higher CAR (≥0.11) group had more clinical risk factors, including age >60 years (P =0.025), ECOG score ≥2 (P =0.004), Lugano stage III-IV (P < 0.001), non-germinal center B-cell-like (non-GCB) subtype (P =0.035), elevated lactate dehydrogenase (LDH) ( P < 0.001), extranodal involved site >1 (P =0.004) and IPI score >2 (P < 0.001). The interim response evaluation of patients showed that the overall response rate (ORR) and complete response rate (CRR) in the lower CAR group were both significantly better than those in the higher CAR group (ORR: 96.9% vs 80.0%, P =0.035; CRR: 63.6% vs 32.5%, P =0.008). With a median follow-up of 24 months, patients with lower CAR had significantly longer median PFS and OS than those with higher CAR (median PFS: not reached vs 67 months, P =0.0026; median OS: not reached vs 67 months, P =0.002), while there was no statistical difference in PFS (P =0.11) and OS (P =0.11) in patients with GPS of 0, 1, and 2. Multivariate Cox regression analysis indicated that only sex (male) and IPI score >2 were independent risk factors for both PFS and OS. CONCLUSION: CAR is significantly correlated with disease progression and survival in DLBCL patients; And compared with GPS, CAR has more advantages in predicting disease outcomes in DLBCL patients.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution.

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-ß (Aß), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aß clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aß plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

The optimal atropine concentration for myopia control in Chinese children: a systematic review and network Meta-analysis.

AIM: To figure out whether various atropine dosages may slow the progression of myopia in Chinese kids and teenagers and to determine the optimal atropine concentration for effectively slowing the progression of myopia. METHODS: A systematic search was conducted across the Cochrane Library, PubMed, Web of Science, EMBASE, CNKI, CBM, VIP, and Wanfang database, encompassing literature on slowing progression of myopia with varying atropine concentrations from database inception to January 17, 2024. Data extraction and quality assessment were performed, and a network Meta-analysis was executed using Stata version 14.0 Software. Results were visually represented through graphs. RESULTS: Fourteen papers comprising 2475 cases were included; five different concentrations of atropine solution were used. The network Meta-analysis, along with the surface under the cumulative ranking curve (SUCRA), showed that 1% atropine (100%)>0.05% atropine (74.9%) >0.025% atropine (51.6%)>0.02% atropine (47.9%)>0.01% atropine (25.6%)>control in refraction change and 1% atropine (98.7%)>0.05% atropine (70.4%)>0.02% atropine (61.4%)>0.025% atropine (42%)>0.01% atropine (27.4%)>control in axial length (AL) change. CONCLUSION: In Chinese children and teenagers, the five various concentrations of atropine can reduce the progression of myopia. Although the network Meta-analysis showed that 1% atropine is the best one for controlling refraction and AL change, there is a high incidence of adverse effects with the use of 1% atropine. Therefore, we suggest that 0.05% atropine is optimal for Chinese children to slow myopia progression.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Dopamine Inhibits the Expression of Hepatitis B Virus Surface and e Antigens by Activating the JAK/STAT Pathway and Upregulating Interferon-stimulated Gene 15 Expression.

Background and Aims: Hepatitis B virus (HBV) infection is a major risk factor for cirrhosis and liver cancer, and its treatment continues to be difficult. We previously demonstrated that a dopamine analog inhibited the packaging of pregenomic RNA into capsids. The present study aimed to determine the effect of dopamine on the expressions of hepatitis B virus surface and e antigens (HBsAg and HBeAg, respectively) and to elucidate the underlying mechanism. Methods: We used dopamine-treated HBV-infected HepG2.2.15 and NTCP-G2 cells to monitor HBsAg and HBeAg expression levels. We analyzed interferon-stimulated gene 15 (ISG15) expression in dopamine-treated cells. We knocked down ISG15 and then monitored HBsAg and HBeAg expression levels. We analyzed the expression of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway factors in dopamine-treated cells. We used dopamine hydrochloride-treated adeno-associated virus/HBV-infected mouse model to evaluate HBV DNA, HBsAg, and HBeAg expression. HBV virus was collected from HepAD38.7 cell culture medium. Results: Dopamine inhibited HBsAg and HBeAg expression and upregulated ISG15 expression in HepG2.2.15 and HepG2-NTCP cell lines. ISG15 knockdown increased HBsAg and HBeAg expression in HepG2.2.15 cells. Dopamine-treated cells activated the JAK/STAT pathway, which upregulated ISG15 expression. In the adeno-associated virus-HBV murine infection model, dopamine downregulated HBsAg and HBeAg expression and activated the JAK-STAT/ISG15 axis. Conclusions: Dopamine inhibits the expression of HBsAg and HBeAg by activating the JAK/STAT pathway and upregulating ISG15 expression.