Objective To study the effects and mechanisms of major immune nutrients and to introduce the progresses of clinical applications about enteral immunonutrition. Methods The related literatures about the effects and clinical applications of enteral immunonutrition were reviewed. Results Infection rate can be reduced and the hospitalization can be shortened as a result of the improved nutritional status and immune competence of patients which can be enhanced by reasonable enteral immune nutrition. Most of the patients suffering from serious diseases can benefit from enteral immunonutrition, such as gastrointestinal cancers, post-transplantation complications, chronic liver disorders, acute pancreatitis and so on. However, as a new nutrition therapy, the clinical use of enteral immunonutrition in critically ill patients is still controversial. Conclusions Enteral immunonutrition plays an important role in the nutritional support of patients with serious diseases, such as gastrointestinal cancers, organ failures. However, much work remains to be done.
Objective To review the relationship between histone modifications and gastrointestinal cancer. Methods Literatures on histone modifications and the relationship between histone modifications and gastrointestinal cancer were collected and reviewed. Results Histone modifications played an important role in the establishment of gene silencing during tumorgenesis. DNA methylation and histone modifications might interact with each other and form a complex network to establish and maintain gene silencing. Restoring gene function silenced by epigenetic changes in cancer had the potential of ‘normalizing’ cancer cells, which was named epigenetic therapy. Epigenetic therapy was very promising in prevention and treatment of gastrointestinal cancer, but many unsolved issues remain which need to be addressed in future studies. Conclusion Histone modifications are associated with the pathogenesis of gastrointestinal cancer. Restoring gene function silenced by epigenetic changes may have a great role in the prevention and treatment of gastrointestinal cancer.
ObjectiveTo investigate the diagnosis, treatment, and prognosis of the postoperative intestinal obstruction of gastrointestinal cancer. MethodThe clinical data of 58 patients with postoperative intestinal obstruction of gastrointestinal cancer from January 2011 to January 2013 were analyzed retrospectively. ResultsIn 58 patients with postoperative intestinal obstruction, there were 46 cases of incomplete intestinal obstruction, 12 cases of complete obstruction. Seventeen cases were treated conservatively and 41 cases were accepted laparotomy. The surgical exploration found that there were 4 cases of strangulated abdominal internal hernia, 4 cases of volvulus, 1 case of stercoral obstruction, 2 cases of intussusception, 9 cases of adhesive intestinal obstruction, and 21 cases of tumor recurrence. There were 32 patients with high tumor markers before laparotomy, including 19 cases of tumor recurrence. Fourteen cases had no obvious tumor lesions detected by PET-CT, but recurrence and metastasis were found by surgical exploration. ConclusionsThe recurrent postoperative intestinal obstruction of gastrointestinal cancer mostly means recurrence and metastasis, with poor prognosis. Early laparotomy may improve the prognosis and the quality of life, elevated tumor markers have some links with tumor recurrence and PET-CT is not sensitive for multiple nodular metastases.
O6-carboxymethyl guanine(O6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O6-CMG dataset and can accurately identify all sequence segments containing O6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.