Objective To investigate the effect of computerized cognitive remediation therapy (CCRT) on cognitive function in female schizophrenia patients in remission. Methods This study included 42 female schizophrenia patients in remission who were treated at Shenyang Mental Health Center between September 2016 and September 2017. Patients were randomly divided into combined therapy and simple drug treatment groups. Patients in the combined therapy group were treated with oral olanzapine plus CCRT, which was used as cognitive therapy for 12 weeks. Those in the simple drug treatment group only received oral olanzapine for 12 weeks. The MATRICS consensus cognitive battery (MCCB) was used to evaluate cognitive function before treatment and 6 and 12 weeks after treatment. Results At12 weeks after treatment, significant differences were observed in symbol coding, digital sequence, spatial span, semantic fluency, continuous operation, speech memory, visual memory, maze, and total scores in the combined therapy group, while significant differences in symbol coding, semantic fluency, spatial span, speech memory, visual memory, and total scores were observed in the simple drug treatment group (all P ＜ 0.05). The MCCB scores in the combined therapy group were higher than those in the simple drug treatment group at 12 weeks after treatment, with statistically significant differences in continuous operation, digital sequence, speech memory, visual memory, maze, and total scores (P ＜ 0.05). Conclusion CCRT can significantly improve cognitive function in female schizophrenia patients in remission.

With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been gained.However,there is still much more to learn about microbial communities,including billions of novel species and genes,as well as count-less spatiotemporal dynamic patterns within the microbial communities,which together form the microbial dark matter.In this work,we summarized the dark matter in microbiome research and reviewed current data mining methods,especially artificial intelligence(AI)methods,for different types of knowledge discovery from microbial dark matter.We also provided case studies on using AI methods for microbiome data mining and knowledge discovery.In summary,we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore,with the goal of advancing our understanding of microbial communities,as well as developing better solu-tions to global concerns about human health and the environment.