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.

Objective: To explore the clinical efficacy of Xiaoshuan enteric-coated capsule (XSECC) in treating cerebral infarction and its potential mechanism of action. Methods: Patients with acute ischemic stroke (AIS) of the qi deficiency and blood stasis type were randomly assigned to the control and observation groups. They were evaluated using the National Institutes of Health Stroke Scale (NIHSS), Activities of Daily Living (ADL), Hachinskilnchemic Scale (HIS), Barthel Index (BI), clinical efficacy scores, and TCM syndrome scores on days 0, 14, 30, and 90. Furthermore, VEGF and BDNF levels were measured on days 30 and 90. Finally, we analyzed the changes in each scale score and vascular neurological factor in both groups. Results: After 14 days of treatment, the difference values in NIHSS, ADL, and BI were higher, and TCM syndrome and clinical efficacy scores were increased in the observation group compared with those of the control group (all P < .05). After 30 days, the NIHSS, ADL, HIS, and TCM syndrome scores were decreased compared with those of the control group, while BI and clinical efficacy scores were increased (all P < .05). After 90 days, the difference value in ADL was higher, and TCM syndrome score was increased in the observation group compared with that of the control group (P = .047, P = .005, respectively). The levels of VEGF and BDNF were higher in the observation group than in the control group on days 14, 30, and 90 (all P < .05). VEGF and BDNF levels on day 0 were associated with prognosis of patients with AIS; therefore, they have a predictive value for the prognosis of acute cerebral infarction. Conclusions XSECC therapy can improve clinical outcomes in patients with acute and recurrent cerebral infarctions. Its mechanism of action may be associated with the secretion of VEGF and BDNF.

Objective To investigate the correlation of ERα gene PvuⅡ ,XbaⅠ and ERβ gene RsaⅠ ,AluⅠ digestion polymorphism with coronary atherosclerotic heart disease(CHD) risk factors in Yancheng area .Methods A total of 124 cases of CHD and 163 persons undergoing physical examination served as the CHD group and CON group .The enzyme method was adopted to detect TG and TC .The direction method was adopted to detect HDL and LDL .ERα gene PvuⅡ ,XbaⅠ and ERβ gene RsaⅠ ,AluⅠ digestion polymorphisms were detected by adopting RFLP-PCR .Results The ratios of smoking history ,family history ,complicating hypertension and diabetes ,and the level of body mass index ,TC ,TG and LDLC in the CHD group were significantly higher than those in the control group ,the difference was statistically significant (P＜0 .05) .The various indicators had no statistically difference between male and female(P＞0 .05) .The frequency distribution and geographic distribution of ERα gene PvuⅡ ,XbaⅠ and ERβ gene RsaⅠ ,Alu Ⅰ digestion polymorphisms had no difference between the two groups ,all conformed to Hardy-Weinberg genetic equilibrium and had the group representativeness .pp ,xx ,RR and AA genotypes in the CHD group were maximal ,while PP , XX ,rr and aa genotypes were minimal ;Pp ,xx ,RR and AA genotypes in the CON group were maximal ,while PP ,XX ,rr and aa genotypes were minimal .The distribution frequency of p and x genes in the CHD group was significantly higher than that in the control group ,the difference was statistically significant(P＜0 .05) .Conclusion The estrogen gene polymorphism might be a target spot for effectively treating CHD ,and p and x gene distribution frequency may be related with CHD risk factors .

Objective To investigate the expression and significance of caveolin-1 in femoral nerve of diabetic patients with foot amputation. Methods Forty patients with foot amputation were assigned to 3 groups according to their duration of type 2 diabetes: group A ( ＜6 years＝, group B (6-10 years), and group C ( ＞10 years). Hematoxylin and eosin (HE) stain and Weil's stain were used to examine the femoral nerve. Silver staining was used to observe the axons and to count the nerve fiber density. The expression of caveolin-1 in Schwann cells of femoral nerve was tested by immunohistochemisty. Results There were evident progressive pathological changes in femoral nerve in the 3 groups. The variance of nerve fiber density in the 3 groups reached statistical significance ( P＜0. 05 ＝, the nerve fiber density showed negative correlation with HbA1C( r =-0. 792, P＜0. 01 ＝ and duration ( r=-0.592, P＜0. 01 ＝. The expression of caveolin-1 in Schwann cells of femoral nerve was positive in all the 3 groups and the variance with statistical significance (P＜0. 01 ), it was negatively correlated with HbA1C (r=-0. 762, P＜0. 01 )and duration (r=-0. 532, P＜0. 01 ), and it was positively correlated with nerve fiber density (r=0. 721, P＜0.01 ), the partial correlation coefficient of caveolin-1 and HbA1Cwas-0. 505 ( P＜0. 01 ).Conclusion In patients with diabetic foot amputation, caveolin-1 may play a role in the development of diabetic peripheral neuropathy and diabetic foot.

Objective To investigate the apearance and characteristics of skin rashes in patients suffering from dengue fever.Methods Clinical data were retrospectively analyzed for 126 hospitalized patients with dengue fever collected from August to October,2007.Results Out of 126 patients with dengue fever,69.0% (87/126)had skin rashes,which usually developed from day 1 and 11 after fever.About 59.7%(52/87)of these patients developed skin rashes from day 3 to 7 after fever.No eruption order was evidenced in 59(67.8%)patients.Skin rashes were located in both the trunk and limbs in 38(43.7%)patients,and only in the limbs in 30 (34.5%)patients.The incidence rates of maculae,maculopapules,papules.hemorrhagic rash and mixed eruptions were 14.9%(13/87),14.9%(13/87),18.4%(16/87),26.4%(23/87)and 21.8%(19/87),respectively.Conclusions Most patients with dengue fever developed skin rashes from day 3 to 7 after fever.Limbs and trunk are predilection sites of skin rashes in dengue fever.Skin rashes in dengue fever is complex,including maculae,papules,hemorrhagic rashes,vesicles and mixed rashes.