Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis

BackgroundBipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome.MethodsA validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein–protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed.ResultsThere were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value.ConclusionPotential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance

Aberrant neural activity in patients with bipolar depressive disorder distinguishing to the unipolar depressive disorder: a resting-state functional magnetic resonance imaging study

This study aims to explore the intrinsic patterns of spontaneous activity of bipolar depression (BD) patients by analyzing the fractional amplitude of low frequency fluctuation (fALFF) that help differentiate BD from unipolar depressive disorder(UD). Twenty eight patients with BD, 47 patients with UD and 29 healthy controls were enrolled to receive the resting-state functional magnetic resonance imaging (rs-fMRI) scans. The group differences of fALFF values were calculated among three groups. In addition, the correlations between the clinical variables and mfALFF values were estimated. The brain regions with activation discrepancies among three groups are located in precuneus, the left middle temporal gyrus (MTG) and left inferior parietal lobe (IPL) and lingual gyrus. Compared with HC group, BD group shows decreased fALFF in precuneus, the left IPL and increased fALFF in lingual gyrus remarkably; UD group shows significantly decreased fALFF in precuneus, the left MTG and the left IPL. On the contrast of patients with UD, patients with BD have significantly increased fALFF value in the left precuneus, the left MGT and lingual gyrus. Furthermore, a negative correlation is found between the mfALFF values in precuneus and the scores of cognitive impairment factor in the UD group. The similar pattern of intrinsic activity in PCC suggests depressive state-dependent change. The aberrant patterns of intrinsic activity in precuneus, the IPL and lingual gyrus might be provide quantitative nodes that help to conduct further study for better distinguishing between BD and UD

Discovery of an orally active benzoxaborole prodrug effective in the treatment of Chagas disease in non-human primates

Trypanosoma cruzi, the agent of Chagas disease, probably infects tens of millions of people, primarily in Latin America, causing morbidity and mortality. The options for treatment and prevention of Chagas disease are limited and underutilized. Here we describe the discovery of a series of benzoxaborole compounds with nanomolar activity against extra- and intracellular stages of T. cruzi. Leveraging both ongoing drug discovery efforts in related kinetoplastids, and the exceptional models for rapid drug screening and optimization in T. cruzi, we have identified the prodrug AN15368 that is activated by parasite carboxypeptidases to yield a compound that targets the messenger RNA processing pathway in T. cruzi. AN15368 was found to be active in vitro and in vivo against a range of genetically distinct T. cruzi lineages and was uniformly curative in non-human primates (NHPs) with long-term naturally acquired infections. Treatment in NHPs also revealed no detectable acute toxicity or long-term health or reproductive impact. Thus, AN15368 is an extensively validated and apparently safe, clinically ready candidate with promising potential for prevention and treatment of Chagas disease

Long-term vegetation changes in the four mega-sandy lands in Inner Mongolia, China.

Desertification in China has become one of the most serious ecological and social problems. The four mega-sandy lands (Hulunbeir, Horqin, Otindag, and Mu Us) in Inner Mongolia are reported to be the most widespread and seriously desertified areas in China. To explore changes of vegetation activity and the possible driving forces in the four mega-sandy lands over the last three decades. We investigated spatiotemporal variations in the growing-season (May-September) normalized difference vegetation index (NDVI) and their relationships with climate factors and human activities during 1982-2011, using two NDVI datasets from Global Inventory Modelling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS). We found a significant overall NDVI increase in Mu Us, but no such trends in the other three. A significant increase was in south and northeast Mu Us and southeast Horqin, and a decrease in south Hulunbeir, northwest Horqin, and central Otindag. NDVI trends were positively correlated with precipitation and uncorrelated with temperature and wind speed in all sandy lands except Mu Us. NDVI trends showed a large spatial heterogeneity in the four sandy lands. Precipitation was a major determiner for the interannual variations and spatial patterns of NDVI at regional scale, whereas human activities were the cause of NDVI variations at local scale. The consistent interannual variations between two NDVI datasets of GIMMS and MODIS for all four sandy lands suggested that GIMMS NDVI was appropriate for investigating long-term vegetation changes in sandy lands.National Natural Science Foundation of China [31330012, 31021001]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA05050300]SCI(E)[email protected]

Long-term vegetation changes in the four mega-sandy lands in Inner Mongolia, China

Desertification in China has become one of the most serious ecological and social problems. The four mega-sandy lands (Hulunbeir, Horqin, Otindag, and Mu Us) in Inner Mongolia are reported to be the most widespread and seriously desertified areas in China. To explore changes of vegetation activity and the possible driving forces in the four mega-sandy lands over the last three decades. We investigated spatiotemporal variations in the growing-season (May-September) normalized difference vegetation index (NDVI) and their relationships with climate factors and human activities during 1982-2011, using two NDVI datasets from Global Inventory Modelling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS). We found a significant overall NDVI increase in Mu Us, but no such trends in the other three. A significant increase was in south and northeast Mu Us and southeast Horqin, and a decrease in south Hulunbeir, northwest Horqin, and central Otindag. NDVI trends were positively correlated with precipitation and uncorrelated with temperature and wind speed in all sandy lands except Mu Us. NDVI trends showed a large spatial heterogeneity in the four sandy lands. Precipitation was a major determiner for the interannual variations and spatial patterns of NDVI at regional scale, whereas human activities were the cause of NDVI variations at local scale. The consistent interannual variations between two NDVI datasets of GIMMS and MODIS for all four sandy lands suggested that GIMMS NDVI was appropriate for investigating long-term vegetation changes in sandy lands