Zika Virus Non-structural Protein 4A Blocks the RLR-MAVS Signaling

Flaviviruses have evolved complex mechanisms to evade the mammalian host immune systems including the RIG-I (retinoic acid-inducible gene I) like receptor (RLR) signaling. Zika virus (ZIKV) is a re-emerging flavivirus that is associated with severe neonatal microcephaly and adult Guillain-Barre syndrome. However, the molecular mechanisms underlying ZIKV pathogenesis remain poorly defined. Here we report that ZIKV non-structural protein 4A (NS4A) impairs the RLR-mitochondrial antiviral-signaling protein (MAVS) interaction and subsequent induction of antiviral immune responses. In human trophoblasts, both RIG-I and melanoma differentiation-associated protein 5 (MDA5) contribute to type I interferon (IFN) induction and control ZIKV replication. Type I IFN induction by ZIKV is almost completely abolished in MAVS(-/-) cells. NS4A represses RLR-, but not Toll-like receptor-mediated immune responses. NS4A specifically binds the N-terminal caspase activation and recruitment domain (CARD) of MAVS and thus blocks its accessibility by RLRs. Our study provides in-depth understanding of the molecular mechanisms of immune evasion by ZIKV and its pathogenesis

Genetic polymorphisms of VIP variants in the Tajik ethnic group of northwest China

BACKGROUND: Individual response to medications varies significantly among different populations, and great progress in understanding the molecular basis of drug action has been made in the past 50 years. The field of pharmacogenomics seeks to elucidate inherited differences in drug disposition and effects. While we know that different populations and ethnic groups are genetically heterogeneous, we have not found any pharmacogenomics information regarding minority groups, such as the Tajik ethnic group in northwest China. RESULTS: We genotyped 85 Very Important Pharmacogene (VIP) variants selected from PharmGKB in 100 unrelated, healthy Tajiks from the Xinjiang Uygur Autonomous Region and compared our data with HapMap data from four major populations around the world: Han Chinese (CHB), Japanese in Tokyo (JPT), Utah Residents with Northern and Western European Ancestry (CEU), and Yorubia in Ibadan, Nigeria (YRI). We found that Tajiks differed from CHB, JPT and YRI in 30, 32, and 32 of the selected VIP genotypes respectively (p < 0.005), while differences between Tajiks and CEU were found in only 6 of the genotypes (p < 0.005). Haplotype analysis also demonstrated differences between the Tajiks and the other four populations. CONCLUSION: Our results contribute to the pharmacogenomics database of the Tajik ethnic group and provide a theoretical basis for safer drug administration that may be useful for diagnosing and treating disease in this population. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-014-0102-y) contains supplementary material, which is available to authorized users

Association Between Post-procedure Cerebral Blood Flow Velocity and Severity of Brain Edema in Acute Ischemic Stroke With Early Endovascular Therapy

ObjectivesWe aimed to investigate the association between post-procedure cerebral blood flow velocity (CBFV) and severity of brain edema in patients with acute ischemic stroke (AIS) who received early endovascular therapy (EVT).MethodsWe retrospectively included patients with AIS who received EVT within 24 h of onset between February 2016 and November 2021. Post-procedure CBFV of the middle cerebral artery was measured in the affected and the contralateral hemispheres using transcranial Doppler ultrasound. The severity of brain edema was measured using the three-level cerebral edema grading from the Safe Implementation of Thrombolysis in Stroke-Monitoring Study, with grades 2–3 indicating severe brain edema. The Association between CBFV parameters and severity of brain edema was analyzed.ResultsA total of 101 patients (mean age 64.2 years, 65.3% male) were included, of whom 56.3% (57/101) suffered brain edema [grade 1, 23 (22.8%); grade 2, 10 (9.9%); and grade 3, 24 (23.8%)]. Compared to patients with non-severe brain edema, patients with severe brain edema had lower affected/contralateral ratios of systolic CBFV (median 1 vs. 1.2, P = 0.020) and mean CBFV (median 0.9 vs. 1.3, P = 0.029). Multivariate logistic regression showed that severe brain edema was independently associated with affected/contralateral ratios of systolic CBFV [odds ratio (OR) = 0.289, 95% confidence interval (CI): 0.069–0.861, P = 0.028] and mean CBFV (OR = 0.278, 95% CI: 0.084–0.914, P = 0.035) after adjusting for potential confounders.ConclusionPost-procedure affected/contralateral ratio of CBFV may be a promising predictor of brain edema severity in patients with AIS who received early EVT

Diagnostic gene signatures and aberrant pathway activation based on m6A methylation regulators in rheumatoid arthritis

PurposeRheumatoid arthritis (RA) is a chronic autoimmune disease (AD) characterized by persistent synovial inflammation, bone erosion and progressive joint destruction. This research aimed to elucidate the potential roles and molecular mechanisms of N6-methyladenosine (m6A) methylation regulators in RA.MethodsAn array of tissues from 233 RA and 126 control samples was profiled and integrated for mRNA expression analysis. Following quality control and normalization, the cohort was split into training and validation sets. Five distinct machine learning feature selection methods were applied to the training set and validated in validation sets.ResultsAmong the six models, the LASSO_λ-1se model not only performed better in the validation sets but also exhibited more stringent performance. Two m6A methylation regulators were identified as significant biomarkers by consensus feature selection from all four methods. IGF2BP3 and YTHDC2, which are differentially expressed in patients with RA and controls, were used to predict RA diagnosis with high accuracy. In addition, IGF2BP3 showed higher importance, which can regulate the G2/M transition to promote RA-FLS proliferation and affect M1 macrophage polarization.ConclusionThis consensus of multiple machine learning approaches identified two m6A methylation regulators that could distinguish patients with RA from controls. These m6A methylation regulators and their target genes may provide insight into RA pathogenesis and reveal novel disease regulators and putative drug targets

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems

In Situ Measurements of the Mechanical Properties of Electrochemically Deposited Li₂CO₃ and Li₂O Nanorods

Solid-electrolyte interface (SEI) is “the most important but least understood (component) in rechargeable Li-ion batteries”. The ideal SEI requires high elastic strength and can resist the penetration of a Li dendrite mechanically, which is vital for inhibiting the dendrite growth in lithium batteries. Even though Li$_{2}$CO$_{3}$ and Li$_{2}$O are identified as the major components of SEI, their mechanical properties are not well understood. Herein, SEI-related materials such as Li$_{2}$CO$_{3}$ and Li$_{2}$O were electrochemically deposited using an environmental transmission electron microscopy (ETEM), and their mechanical properties were assessed by in situ atomic force microscopy (AFM) and inverse finite element simulations. Both Li$_{2}$CO$_{3}$ and Li$_{2}$O exhibit nanocrystalline structures and good plasticity. The ultimate strength of Li$_{2}$CO$_{3}$ ranges from 192 to 330 MPa, while that of Li$_{2}$O is less than 100 MPa. These results provide a new understanding of the SEI and its related dendritic problems in lithium batteries

Identification of stable reference genes for quantitative gene expression analysis in the duodenum of meat-type ducks

Quantitative polymerase chain reaction (qPCR) is an important method to detect gene expression at the molecular level. The selection of appropriate housekeeping genes is the key to accurately calculating the expression level of target genes and conducting gene function studies. In this study, the expression of eight candidate reference genes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-actin (β-actin), 18S ribosomal RNA (18S rRNA), hydroxymethylbilane synthase (HMBS), hypoxanthine phosphoribosyltransferase 1 (HPRT1), TATA box binding protein (TBP), ribosomal protein L13 (RPL13), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAZ), in the duodenal epithelial tissue of 42-day-old meat-type ducks were detected using qPCR. Furthermore, their expression stability was analyzed using the geNorm, NormFinder, and BestKeeper programs. The results indicated that HMBS and YWHAZ were the most stably expressed genes. All three programs indicated that the expression of 18S rRNA was the least stable, making it unsuitable for the study of gene expression in meat-type duck tissues. This study provides stable reference genes for gene expression analysis and contributes to further studies on the gene function of meat-type ducks

Environmental Justice Assessment of Fine Particles, Ozone, and Mercury over the Pearl River Delta Region, China

Assessment of environmental justice (EJ, a concept related to the distributional fairness of environmental risks) is a crucial component in environmental risk management. However, the risks associated with air pollutants and toxins have rarely been evaluated jointly. Therefore, using an approach integrating modeling, data fusion, and health benefits analysis, we performed an EJ assessment on the mortalities caused by fine particle (PM2.5) and ozone (O3) concentrations and mercury (Hg) deposition over the Pearl River Delta (PRD) region. The concentration index (CI) was used to measure EJ in low-income distributions and age structures, and a larger value implied a greater EJ issue. The results revealed that the CIs of PM2.5, O3, and Hg were 0.35, 0.32, and 0.16, respectively, based on the percentage of the low-income population, and 0.39, 0.36, and 0.23, respectively, based on the elderly and children, indicating that environmental injustice was more prominent for PM2.5 and more reflected in the elderly and children. The center (e.g., Guangzhou) and some marginal areas (e.g., northeast of Jiangmen) in the PRD were overburdened areas with PM2.5, O3, and Hg pollution due to their intensive source emissions. Moreover, cumulative environmental risk (CER) corrected by population vulnerability exhibited significant differences among the cities; for example, cumulative environmental risk scores (CERSs) in Jiangmen, Huizhou, and Zhaoqing were 14.18 to 32.98 times higher than that in Shenzhen. Hence, the implementation of multipollutant control policies for local PM2.5, O3, and Hg in overburdened areas is recommended to further promote EJ in the PRD

Zika Virus Non-structural Protein 4A Blocks the RLR-MAVS Signaling

Flaviviruses have evolved complex mechanisms to evade the mammalian host immune systems including the RIG-I (retinoic acid-inducible gene I) like receptor (RLR) signaling. Zika virus (ZIKV) is a re-emerging flavivirus that is associated with severe neonatal microcephaly and adult Guillain-Barre syndrome. However, the molecular mechanisms underlying ZIKV pathogenesis remain poorly defined. Here we report that ZIKV non-structural protein 4A (NS4A) impairs the RLR-mitochondrial antiviral-signaling protein (MAVS) interaction and subsequent induction of antiviral immune responses. In human trophoblasts, both RIG-I and melanoma differentiation-associated protein 5 (MDA5) contribute to type I interferon (IFN) induction and control ZIKV replication. Type I IFN induction by ZIKV is almost completely abolished in MAVS-/- cells. NS4A represses RLR-, but not Toll-like receptor-mediated immune responses. NS4A specifically binds the N-terminal caspase activation and recruitment domain (CARD) of MAVS and thus blocks its accessibility by RLRs. Our study provides in-depth understanding of the molecular mechanisms of immune evasion by ZIKV and its pathogenesis