Autism spectrum disorder (ASD) is a hereditary heterogeneous neurodevelopmental disorder characterized by social and speech dysplasia. We collected the expression pro les of ASD in GSE26415, GSE42133 and GSE123302, as well as methylation data of GSE109905. Differentially expressed genes (DEGs) between ASD and controls were obtained by differential expression analysis. Enrichment analysis identi ed the biological functions and signaling pathways involved by common genes in three groups of DEGs. PPI networks were used to identify genes with the highest connectivity as key genes. In addition, we identi ed methylation markers by associating differentially methylated positions (DMPs). Key methylation markers were identi ed using the LASSO model. ROC curves and nomograms were used to identify the diagnostic role of key methylation markers for ASD. A total of 57 common genes were identi ed in the three groups of DEGs. These genes were mainly enriched in Sphingolipid metabolism and PPAR signaling pathway. In the PPI network, we identi ed seven key genes with higher connectivity, and used qPCR experiments to verify the expressions. In addition, we identi ed 31 methylation markers and screened 3 key methylation markers (RUNX2, IMMP2L and MDM2) by LASSO model. They all had good diagnostic effects on ASD, and their methylation levels were closely related to the risk of ASD. Our analysis identi ed RUNX2, IMMP2L and MDM2 as possible diagnostic markers for ASD. Identifying different biomarkers and risk genes will contribute to the early diagnosis of ASD and the development of new clinical and drug treatments.

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Introduction: To control the spread of human immunodeficiency virus (HIV) among sero-discordant couples, we explored the HIV seroconversion and its contributing factors. Methodology: We recruited negative partners in HIV sero-discordant couples to established a prospective cohort between January 2010 and June 2015 from areas with severe HIV epidemic in Xinjiang. Follow up once every 3 months, serological tests and risk behavior surveys every 6 months. Variables were screened by LASSO regression and a Cox proportional hazards model was established. Results: A total of 1162 negative partners of sero-discordant couples were recruited. The seroconversion occurred in 42 negative partners during follow-up period, with a seroconversion rate of 2/100 (95% CI = 1.21-2.27), and the median time for seroconversion was 0.92 years. The Cox model showed that frequency of sexual behavior for nearly six months, consistent condom use, knowledge of the transmission route for HIV, a history of sexually transmitted diseases, recent CD4 + T lymphocyte count were all significant contributing factors to the seroconversion in negative partner of HIV sero-discordant couples. In addition, the Cox model was used to evaluate the risk factors of seroconversion for HIV negative partners. Conclusions: The seroconversion rate of HIV negative partners in Xinjiang was lower. The LASSO Cox model may accurately predict the risk of HIV transmission in sero-discordant couples.

Autism spectrum disorder (ASD) is a hereditary heterogeneous neurodevelopmental disorder characterized by social and speech dysplasia. We collected the expression profiles of ASD in GSE26415, GSE42133 and GSE123302, as well as methylation data of GSE109905. Differentially expressed genes (DEGs) between ASD and controls were obtained by differential expression analysis. Enrichment analysis identified the biological functions and signaling pathways involved by common genes in three groups of DEGs. PPI networks were used to identify genes with the highest connectivity as key genes. In addition, we identified methylation markers by associating differentially methylated positions (DMPs). Key methylation markers were identified using the LASSO model. ROC curves and nomograms were used to identify the diagnostic role of key methylation markers for ASD. A total of 57 common genes were identified in the three groups of DEGs. These genes were mainly enriched in Sphingolipid metabolism and PPAR signaling pathway. In the PPI network, we identified seven key genes with higher connectivity, and used qPCR experiments to verify the expressions. In addition, we identified 31 methylation markers and screened 3 key methylation markers (RUNX2, IMMP2L and MDM2) by LASSO model. They all had good diagnostic effects on ASD, and their methylation levels were closely related to the risk of ASD. Our analysis identified RUNX2, IMMP2L and MDM2 as possible diagnostic markers for ASD. Identifying different biomarkers and risk genes will contribute to the early diagnosis of ASD and the development of new clinical and drug treatments.