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Surgical site infections (SSIs) pose significant risks to patients undergoing colorectal cancer (CRC) surgery. With increasing evidence on the benefits of oral probiotics in various clinical contexts, there is a need to assess their efficacy and safety in reducing SSIs following CRC surgery. A systematic review and meta-analysis were conducted in line with PRISMA guidelines using the PICO framework. On 19 September 2023, four major databases (PubMed, Embase, Web of Science and Cochrane Library) were searched without any temporal or language restrictions. Rigorous inclusion and exclusion criteria were employed. Data extraction was independently undertaken by two assessors, and any discrepancies were discussed. The Cochrane Collaboration's risk of bias instrument was utilized to assess study quality. The meta-analysis incorporated a fixed-effects model or random-effects model based on the I2 statistic to assess heterogeneity. The initial search yielded 1282 articles, of which 10 met the inclusion criteria and were analysed. Probiotic administration not only significantly reduced the incidence of SSIs but also curtailed the duration of hospital stays. Moreover, the subgroup analysis indicated that interventions employing multiple strains of probiotics were more effective in reducing postoperative infections than those utilizing a single strain. Probiotics effectively prevent postoperative infections and shorten hospital stays. Multi-strain probiotics outperform single strain in efficacy. Future studies should focus on their safety and optimal clinical use.
by Zhitao Guo, Xiaojie Zhao, Li Yao, Zhiying Long
The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.by Ting Zou, Xiaohui Zhou, Qinwen Wang, Yongjie Zhao, Meisheng Zhu, Lei Zhang, Wei Chen, Pari Abuliz, Haijun Miao, Keyimu Kabinur, Kader Alimu
ObjectiveTo investigate the associations of serum DNA methylation levels of chemokine signaling pathway genes with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) in elderly people in Xinjiang, China, and to screen out genes whose DNA methylation could distinguish AD and MCI.
Materials and methods37 AD, 40 MCI and 80 controls were included in the present study. DNA methylation assay was done using quantitative methylation-specific polymerase chain reaction (qMSP). Genotyping was done using Sanger sequencing.
ResultsDNA methylation levels of ADCY2, MAP2K1 and AKT1 were significantly different among AD, MCI and controls. In the comparisons of each two groups, AKT1 and MAP2K1’s methylation was both significantly different between AD and MCI (p MAP2K1’s methylation was also significantly different between MCI and controls. Therefore, AKT1’s methylation was considered as the candidate serum marker to distinguish AD from MCI, and its association with AD was independent of APOE ε4 allele (p AKT1 hypermethylation was an independent risk factor for AD and MAP2K1 hypomethylation was an independent risk factor for MCI in logistic regression analysis (p Conclusion
This study found that the serum of AKT1 hypermethylation is related to AD independently of APOE ε4, which was differentially expressed in the Entorhinal Cortex of the brain and was an independent risk factor for AD. It could be used as one of the candidate serum markers to distinguish AD and MCI. Serum of MAP2K1 hypomethylation is an independent risk factor for MCI.
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