Search

Background: Bipolar I disorder (BD‐I) is associated with a high risk of suicide attempt; however, the neural circuit dysfunction that confers suicidal vulnerability in individuals with this disorder remains largely unknown. Resting‐state functional magnetic resonance imaging (rs‐fMRI) allows non‐invasive mapping of brain functional connectivity. The current study used an unbiased voxel‐based graph theory analysis of rs‐fMRI to investigate the intrinsic brain networks of BD‐I patients with and without suicide attempt. Methods: A total of 30 BD‐I patients with suicide attempt (attempter group), 82 patients without suicide attempt (non‐attempter group), and 67 healthy controls underwent rs‐fMRI scan, and then global brain connectivity (GBC) was computed as the sum of connections of each voxel with all other gray matter voxels in the brain. Results: Compared with the non‐attempter group, we found regional differences in GBC values in emotion‐encoding circuits, including the left superior temporal gyrus, bilateral insula/rolandic operculum, and right precuneus (PCu)/cuneus in the bipolar disorder (BD) attempter group, and these disrupted hub‐like regions displayed fair to good power in distinguishing attempters from non‐attempters among BD‐I patients. GBC values of the right PCu/cuneus were positively correlated with illness duration and education in the attempter group. Conclusions: Our results indicate that abnormal connectivity patterns in emotion‐encoding circuits are associated with the increasing risk of vulnerability to suicide attempt in BD patients, and global dysconnectivity across these emotion‐encoding circuits might serve as potential biomarkers for classification of suicide attempt in BD patients. [ABSTRACT FROM AUTHOR]

Steroids have become the second largest class of drugs after antibiotics. Microbial transformation is an indispensable key to synthesize steroids or intermediates. We review the research status of steroid hormone drugs in recent years from the aspects of microbial transformation, side chain degradation reaction and strategies to enhance biotransformation efficiency. [ABSTRACT FROM AUTHOR]

Motivation It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes. Results We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P -values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains. Availability and implementation The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Aims: To investigate the current situation of mental psychology and quality of life (QoL) in patients with inflammatory bowel disease (IBD) in China, and analyze the influencing factors., Methods: A unified questionnaire was developed to collect clinical data on IBD patients from 42 hospitals in 22 provinces from September 2021 to May 2022. Multivariate Logistic regression analysis was conducted, and independent influencing factors were screened out to construct nomogram. The consistency index (C-index), receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, accuracy, and clinical utility of the nomogram model., Results: A total of 2478 IBD patients were surveyed, including 1371 patients with ulcerative colitis (UC) and 1107 patients with Crohn's disease (CD). Among them, 25.5%, 29.7%, 60.2%, and 37.7% of IBD patients had anxiety, depression, sleep disturbance and poor QoL, respectively. The proportion of anxiety, depression, and poor QoL in UC patients was significantly higher than that in CD patients (all p < 0.05), but there was no difference in sleep disturbance between them (p = 0.737). Female, higher disease activity and the first visit were independent risk factors for anxiety, depression and sleep disturbance in IBD patients (all p < 0.05). The first visit, higher disease activity, abdominal pain and diarrhea symptoms, anxiety, depression and sleep disturbance were independent risk factors for the poor QoL of patients (all p < 0.05). The AUC value of the nomogram prediction model for predicting poor QoL was 0.773 (95% CI: 0.754-0.792). The calibration diagram of the model showed that the calibration curve fit well with the ideal curve, and DCA showed that the nomogram model could bring clinical benefits., Conclusion: IBD patients have higher anxiety, depression, and sleep disturbance, which affect their QoL. The nomogram prediction model we constructed has high accuracy and performance when predicting QoL., (© 2024 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Motivation: It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes., Results: We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains., Availability and Implementation: The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)