Objective To investigate the effect of Lactobacillus plantarum(LP)on intestinal flora and bacterial translocation in mice with spontaneous inflammatory bowel disease(IBD). Methods Interleukin 10 knockout mice(IL-10~(-/-))were used as models of IBD.Eight-week old female mice were randomized to control group, IL-10~(-/-)group and IL-10~(-/-)+LP group.IL-10~(-/-)+LP group received 0.5 mL LP(1.0×10~9CFU/mL)per day for 4 weeks,and the other groups received 0.5 mL Ringer buffer.Intestinal flora including Bifidobacteria,Lactobacilli,Enterobacteriaceae and Clostridium perfringens in the feces and bacterial translocation in mesenteric lymph nodes and spleens were detected. Results The contents of Bifidobacteria and Lactobacilli significantly decreased in the intestine of IL-10~(-/-)mice,while those of Enterobacteriaceae and Clostridium perfringens significantly increased,and the bacterial translocation significantly increased.Four weeks after LP treatment, the disturbed intestinal flora was restored, and the bacterial translocation decreased. Conclusion LP administration can modulate the imbalance of intestinal flora and decrease the bacterial translocation,thus enhance intestinal barrier function in mice with IBD.

Objective To evaluate the role of Lactobacillus plantarum (LP) in the treatment of colitis in interleukin (IL)-10 knockout (IL-10-/-) mice and to explore its possible mechanisms.Methods Eight weeks old female wildtype (WT) mice and IL-10-/- mice, twenty mice of each type,were randomly assigned to four groups, WT group, WT+ LP group, IL-10-/- group and IL-I0-/- +LP group. The WT and IL-10-/- mice were gavaged with 0.5 ml saline, WT+Lp and IL-10-/- +Lp groups were gavaged with Lp 0.02 g (0.5 ml) ,took Lp 1 × 109 cfu everyday,continued for 4 weeks and then the experiment finished. The fresh mice faeces was collected once every week before (week 0) and during the experiment. The mice were executed at the end of experiments, the change of mice weight Was recorded, the length and the wet weight of colon were measured, fresh colon tissue specimens were taken for biological slices and proinflammatory cytokines TNF-a and IFN-γ were measured in colon mucosa. The fresh faeces were selectively cultured. The colonization of Lp in normal and colitis mice and its regulation role in intestinal flora were observed. Results Compared with WT mice, IL-10-/- mice demonstrated severe diarrhea, significantly decreased in body weight (P ＜0.05)and serious malnutrition. After Lp treatment, diarrhea relieved in IL-10-/- mice and the body weight increased significantly (P＜0.05). Pathological examination suggested that 100％ of IL-10-/-mice had intestinal inflammation, however after Lp treatment intestinal inflammation improved significantly. Mucosal ulcer, epithelial hyperplasia, the infiltration of neutrophils and lymphocytes in the lamina propria were also significantly reduced.The histopathological score was significantly lowered (P＜0.01). After Lp treatment, colon wet weight and the ratio of wet weight to length of IL-10-/- mice changed significantly (P＜0.01). Colon edema and thickening improved remarkably. The TNF-a and IFN-γ concentration of colon in IL-10-/- mice were 377.4±84.4 μg/g and 602.6±108.1 μg/g,which increased obviously than WT group (139.2 ± 32. 7 μg/g and 173.0± 52.4 μg/g, P＜0.05). After treated with Lp for four weeks, the TNF-α and IFN-γ concentration of colon in IL-10-/-+Lp group mice were 207.2±65.7 μg/g and 442.1 ± 138.4 μg/g, both were lower than that of IL-10-/- group mice (P＜0.05). The intestinal flora was disrupted in IL-10-/- mice. Conclusion Lp can effectively reduce intestinal inflammation in IL-10-/- mice, which take certain part in treatment in colitis. This treatment effect is associated with intestinal flora regulation and the inhibition of proinflammation cytokines expression.

Objective To investigate the effects of tuina manipulation on creep rate and maximal breaking stress of contractural Achilles tendons of rabbits. Methods Twenty-four rabbits were randomly divided into contrac-tural Achilles tendon model group, free activities group and tuina manipulation group. The opposite legs of model group were used as normal controls. Creep rate and maximal breaking stress of Achilles tendons were compared among the 4 groups. Results The average creep rate in tuina manipulation group approximated to that in normal group and was lower than that in model and free activities groups. The average maximal breaking stress in tuina manipulation group approximated to that in normal group and was higher than that in model and free activities groups. Conclusion Tuina manipulation can promote the creep rate and maximal breaking stress of contractura Achilles tendon approximately to normal level. Tuina manipulation may be useful in rehabilitation.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.