Background: /Aim: It is known that an imbalance in the intestinal f lora plays a crucial role in colorectal cancer (CRC), but the effect of food consumption patterns on the types of intestinal flora remains to be clarified. We aimed to analyze the associations between food intake and intestinal flora in healthy and CRC individuals. Methods: Food intake data were recorded using the Food Frequency Questionnaire (FFQ). The composition and diversity of the intestinal flora detected by 16S rRNA gene sequencing, and the data were analyzed by R version 3.1.1 software. Results: Higher intake of red meat or pickled foods, and lower intake of white meat, fruits, vegetables, beans, nuts were found in the CRC group compared with the healthy group. Higher levels of Fusobacteria and Proteobacteria, and lower levels of Firmicutes were observed in the CRC group. Partial correlation analysis revealed that the intake of fruits, beans, and nuts was negatively correlated with Proteobacteria and Fusobacteria, but pickled food was positively correlated with Fusobacteria (p < 0.05). Fish, beans, and nuts intake was negatively correlated with Escherichia (p = 0.01). Multiple regression analysis revealed that vegetable oil (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.13 to 0.82), vegetables (OR, 0.26; 95% CI, 0.10 to 0.64), eggs (OR, 0.26; 95% CI, 0.10 to 0.69), pickled foods (OR, 21.02; 95% CI, 6.02 to 73.45), and red meat (OR, 4.23; 95% CI, 1.68 to 10.60) had an impact on CRC risk. Conclusions The species and abundance of intestinal flora varies between CRC and healthy individuals and may be affected by their food preference.

Background: /Aim: It is known that an imbalance in the intestinal f lora plays a crucial role in colorectal cancer (CRC), but the effect of food consumption patterns on the types of intestinal flora remains to be clarified. We aimed to analyze the associations between food intake and intestinal flora in healthy and CRC individuals. Methods: Food intake data were recorded using the Food Frequency Questionnaire (FFQ). The composition and diversity of the intestinal flora detected by 16S rRNA gene sequencing, and the data were analyzed by R version 3.1.1 software. Results: Higher intake of red meat or pickled foods, and lower intake of white meat, fruits, vegetables, beans, nuts were found in the CRC group compared with the healthy group. Higher levels of Fusobacteria and Proteobacteria, and lower levels of Firmicutes were observed in the CRC group. Partial correlation analysis revealed that the intake of fruits, beans, and nuts was negatively correlated with Proteobacteria and Fusobacteria, but pickled food was positively correlated with Fusobacteria (p < 0.05). Fish, beans, and nuts intake was negatively correlated with Escherichia (p = 0.01). Multiple regression analysis revealed that vegetable oil (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.13 to 0.82), vegetables (OR, 0.26; 95% CI, 0.10 to 0.64), eggs (OR, 0.26; 95% CI, 0.10 to 0.69), pickled foods (OR, 21.02; 95% CI, 6.02 to 73.45), and red meat (OR, 4.23; 95% CI, 1.68 to 10.60) had an impact on CRC risk. Conclusions The species and abundance of intestinal flora varies between CRC and healthy individuals and may be affected by their food preference.

Objective To develop a nomogram for predicting the risks of early anastomotic recurrence(EAR)after primary bowel resection in patients with Crohn's disease(CD).Methods The patients with CD under-going preoperative magnetic resonance enterography(MRE)and primary bowel resection were enrolled in this retrospective study and divided into an EAR group(18 patients)and EAR-free group(12 patients).The EAR group included the patients having an endoscopic Rutgeerts score of≥I2 month or the need for anastomotic resection within 12 months after surgery.All the 38 indexes including preoperative demographic characteristics,laboratory examina-tions,multi-parameter MRE features of the resected intestine and its adjacent mesentery,histological findings,and postoperative pharmacotherapy were analyzed.Least absolute shrinkage and selection operator(LASSO)regression and multivariate binary logistic regression analysis were performed to identify independent risk factors to be incorpo-rated into the nomogram for predicting the risks of early anastomotic recurrence and the prediction performance was evaluated.Results Mesenteric creeping fat index on MRE and comb sign were independent risks of EAR,with a concordance index of 0.882(95%CI:0.764～1).The calibration plot revealed a strong relationship between actual observation and predicted probability of EAR.Conclusions The preoperative MRE-based nomogram may be a potential tool for predicting EAR following surgery in patients with CD,which is beneficial to individual management in those patients.It provides reference for the formulation of early postoperative individualized drug adjuvant therapy in patients at high risk of EAR.

Background Esophageal cancer is a common gastrointestinal tumor with a high incidence in China. Some studies suggest that intestinal flora is significantly related to the occurrence and development of tumors and other diseases. Traditional 16S rDNA sequencing technology only provides taxonomic resolution at genus level. Objective Based on PacBio single molecule real time (SMRT) sequencing technology to identify characteristic microbial biomarkers associated with esophageal cancer at the species level. Methods A total of 120 newly diagnosed cases of esophageal cancer were recruited and 60 healthy patients with matched sex and age were recruited as the control group. Fresh stool samples were collected from all subjects. Full-length 16S rDNA sequencing was performed on samples from 4 patients with esophageal cancer and 1:1 matched healthy controls using the third-generation sequencing PacBio SMRT technology, and the structural differences of intestinal flora were analyzed based on the sequencing results. Function prediction was performed by PICRUSt software. Large population samples were validated by screening different gut microbes by linear discriminant analysis and linear discriminant analysis effect size to identify esophageal cancer-associated gut microbes. Results Based on sequencing samples, the results of α diversity analysis showed that the Ace, Chao1, Simpson Diversity, and Shannon Wiener indices of the esophageal cancer group were higher than those of the healthy control group (P<0.05), and the results of β diversity showed that the scattered clusters of the esophageal cancer group and the healthy control group were separated, which meant that there were differences in the structure of intestinal flora between the two groups. It was found at the phylum level that the abundances of Proteobacteria, Bacteroidetes, and Firmicutes in the intestinal flora of the esophageal cancer group were increased. At the genus level, the relative abundances of Spirospira, Pasteurella, Roxella, and Bacteroides in the esophageal cancer group were increased. At the species level, there were 11 microbial species with increased relative abundances in the esophageal cancer group, including Enterobacter sp. E.20, Bacteroides ovatus V975, and Faecalibacterium prausnitzii, and the microbial species with decreased relative abundances in the esophageal cancer group were Ralstonia pickettii, Enterobacter unclassified, and Streptococcus salivarius JIM8777. The PICRUSt functional annotation found differences in alanine, aspartate and glutamate metabolism (map00250), peptidoglycan (map00550), one carbon pool by folate (map00670), thiamine metabolism (map00730), and biosynthesis of amino acids (map01230) between the two groups. The results of the population validation study showed that the abundances of Enterobacter sp E.20 and Bacteroides massilience in the esophageal cancer group were increased, the abundance of Streptococcus salivarius JIM8777 was decreased, and the differences between the two groups were statistically significant (P<0.05). By establishing receiver operating characteristic analysis for representative species level biomarkers, the area under curve (AUC) of combining Enterobacter sp E.20, Streptococcus salivarius JIM8777, and Bacteroides massilience was 0.779, higher than single diagnosis (AUC=0.610, 0.608, and 0.659, respectively). Conclusion There are significant differences in gut microbiota between the esophageal cancer group and the healthy control group. The combination of Enterobacter sp E.20, Streptococcus salivarius JIM8777, and Bacteroides Massilience has potential application value for the diagnosis of esophageal cancer.

【Objective】 To study the annual financial expenditure in blood stations with different scales, and to establish the regression equation between blood collection units and total expenditure. 【Methods】 The annual total expenditure, the per capita cost of serving population, as well as the collection units of whole blood and apheresis platelet of 24 blood stations were collected. The financial expenditure required for collecting 10 000U blood was calculated.The statistical analysis was carried out with SPSS statistical software. 【Results】 From 2017 to 2020, the total annual financial expenditure of 24 blood stations showed an upward trend. The total expenditure among blood stations was different. The per capita cost of servicing population in the areas where the 24 blood stations were located had been increasing year by year. The 24 blood stations were divided into two grades according to the blood collection volume as 50 000 U, and the relationship equation between the blood collection volume and the annual total expenditure had been established. After testing, each equation was effective(P<0.05); There was no difference in the financial expenditure required for collecting 10 000U blood among blood stations with different scales. 【Conclusion】 From 2017 to 2020, the blood stations with an annual collection volume more than 50 000 U demonstrated a higher financial expenditure and the per capita cost of serving population than those <50 000 U. The blood collection volume of blood stations is significantly correlated with the annual total expenditure and the per capita cost of serving population.