Objective To summarize the effect of supra-arch branches bypass combined with endovascular aortic repair for aortic diseases.Methods From January 2012 to August 2015,120 cases of thoracic aortic diseases (aortic dissection 103,aortic aneurysm 16,penetrating aortic ulcer 1) received hybrid operation in Guangdong Cardiovascular Institute.Vascular bypass was established among the brachiocephalic arteries,followed by endovascular repair through femoral artery either one-stage or two-stage.Patients were followed up for 3-24 months.Results Technical success was achieved among all the patients.Five patients died after the operation(one patient had retrograde aortic dissection,2 patients had pericardial tamponade,one patient had apnea,and one patient had respiratory and cardiac arrest.The death rate is 4.1％),4 patients had stroke,among them,symptoms were relieved in three patients,one patient was not cured.Total 92 patients were followed-up and had no symptoms of up-limb ischemia or dizziness.CT scan showed bypass graft and endovascular stent patency.6 patients had endoleak (type Ⅰ b 2 cases,type Ⅱ 3 cases,and type Ⅲ 1 case),distal aortic dissection occurred in one patient,three patients had mild contrast agent leakage around the distal endovascular stent,type A aortic dissection occurred in one patient,there were no late stage death.Conclusion Supraarch branches bypass combined with endovascular aortic repair for treating aortic disease is minimally invasive,safe,and can reduce the incidence of postoperative complications.

Neural stem/progenitor cells (NSCs) can spontaneously differentiate into neurons and glial cells in the absence of mitogen fibroblast growth factor-2 (FGF-2) or epidermal growth factor (EGF) in medium and the spontaneous differentiation of NSCs is mediated partially by endogenous ciliary neurotrophic factor (CNTF). This study examined the relationship of FGF-2 and CNTF in the spontaneous differentiation of adult hippocampal progenitor cells (AHPs). AHPs were cultured in the medium containing different concentration of FGF-2 (1-100 ng/mL). Western blotting and immunofluorescence staining were applied to detect the expression of the astrocytic marker GFAP, the neuronal marker Tuj1, the oligodendrocytic marker CNPase and, Nestin, the marker of AHPs. The expression of endogenous CNTF in AHPs at early (passage 4) and late stage (passage 22) was also measured by Western blotting. The results showed that FGF-2 increased the expression of Nestin, dramatically inhibited the expression of GFAP and Tuj1 and slightly suppressed the expression of CNPase. FGF-2 down-regulated the expression of endogenous CNTF in AHPs at both early (passage 4) and late stage (passage 22). These results suggested that FGF-2 could inhibit the spontaneous differentiation of cultured AHPs by negatively regulating the expression of endogenous CNTF in AHPs.

Objective To systematically investigate the molecular epidemiological profiles of human papillomavirus (HPV) in patients with condyloma acuminata(CA). Methods Two hundred and one samples of HPV DNA isolated from CA were PCR amplified by the PGMY09/11 primer system. The PCR products were simultaneously hybridized to 37 specific HPV probes immobilized on a nylon strip and then genotyped. All DNA templates were further PCR amplified using HPV 6 and 11 type specific primers for verification. Results All samples were HPV DNA positive consisting of totally 31 genotypes, the types of which were type 11(53.7%, 108/201), 6(43.8%, 88/201), 16(6.5%, 13/201), 52(6.0%, 12/201), 33(5.5%, 11/201), cp6108 (5.5%, 11/201) and 42 (5.0%, 10/201). The samples infected with a single and mixed types of HPV accounted for 60.2% (121/201) and 39.8% (80/201) respectively. Consistent results were found with the detection of HPV6 and 11 between hybridization assay and type-specific PCR. Conclusions At least 31 HPV genotypes are associated with CA. HPV 11 predominates while 68, 40, 54, 67, 73, 82, 35, 64 and 83 are rare in CA. Type cp6108 is detected in CA for the first time with a high prevalence. HPV26, 69, 70, 71,72 and IS39 might be not associated with CA. CA infected with a single and mixed HPV types accounts for 60.2% and 39.8%, respectively.

Neural stem/progenitor cells (NSCs) can spontaneously differentiate into neurons and glial cells in the absence of mitogen fibroblast growth factor-2 (FGF-2) or epidermal growth factor (EGF) in medium and the spontaneous differentiation of NSCs is mediated partially by endogenous ciliary neurotrophic factor (CNTF). This study examined the relationship of FGF-2 and CNTF in the spontaneous differentiation of adult hippocampal progenitor cells (AHPs). AHPs were cultured in the medium containing different concentration of FGF-2 (1-100 ng/mL). Western blotting and immunofluorescence staining were applied to detect the expression of the astrocytic marker GFAP, the neuronal marker Tuj1, the oligodendrocytic marker CNPase and, Nestin, the marker of AHPs. The expression of endogenous CNTF in AHPs at early (passage 4) and late stage (passage 22) was also measured by Western blotting. The results showed that FGF-2 increased the expression of Nestin, dramatically inhibited the expression of GFAP and Tuj1 and slightly suppressed the expression of CNPase. FGF-2 down-regulated the expression of endogenous CNTF in AHPs at both early (passage 4) and late stage (passage 22). These results suggested that FGF-2 could inhibit the spontaneous differentiation of cultured AHPs by negatively regulating the expression of endogenous CNTF in AHPs.
Animals ; Cell Differentiation ; physiology ; Ciliary Neurotrophic Factor ; metabolism ; Fibroblast Growth Factor 2 ; metabolism ; Hippocampus ; metabolism ; physiology ; Male ; Rats ; Rats, Wistar ; Stem Cells ; metabolism ; physiology

Objective To analyze the characteristics of time in range(TIR)and its relationship with oxidative stress(OS)and insulin resistance status(HOMA-IR)in patients with type 2 diabetes mellitus(T2DM)and sleep apnea-hypopnea syndrome(OSAHS).Methods According to apnea-hypopnea index(AHI),165 T2DM in patients were divided into simple T2DM group(AHI<5 times/h,n=43),T2DM combine OSAHS mild group(OSAHS-G,5≤AHI<15 times/h,n=51),T2DM combined OSAHS moderate group(OSAHS-M,15≤AHI≤30 times/h,n=40)and T2DM combine OSAHS severe group(OSAHS-S,AHI>30 times/h,n=31).TIR was calculated by dynamic blood glucose monitoring.Superoxide dismutase(SOD),glutathione peroxidase(GSH-Px)and other indexes were detected and analyzed.Results Compared with simple T2DM group,the levels of HOMA-IR,8-iso-PGF2a and Ox-LDL were higher in T2DM combined OSAHS-G,OSAHS-M or OSAHS-S group,while the levels of TIR,SOD and GSH-Px were lower(P<0.05).Pearson correlation analysis showed that TIR was positively correlated with the levels of SOD and GSH-Px(P<0.05 or P<0.01),and negatively correlated with the levels of 8-iso-PGF2a,Ox-LDL,HbA1c,HOMA-IR and the severity of OSAHS(P<0.01).Logistic regression analysis showed that TIR,SOD and GSH-Px were protective factors for severe OSAHS in T2DM patients,while 8-iso-PGE2a and Ox-LDL were the risk factors for severe OSAHS.Conclusions The glucose level fluctuates greatly in patients with T2DM and OSAHS.Insulin resistance and oxidative stress are factors that affect the normalization of TIR.

OBJECTIVES: Radiation therapy is a main method for female pelvic malignancies, which can cause some adverse reactions, such as radiation proctitis (RP). The incidence of RP is highly positively correlated with radiation dose. There is an urgent need for a scientific method to accurately predict the occurrence of RP to help doctors make clinical decisions. In this study, based on the clinical data of female pelvic tumor patients and dosimetric parameters of radiotherapy, the random forest method was used to screen the hub features related to the occurrence of RP, and then a machine learning algorithm was used to construct a risk prediction model for the occurrence of RP, in order to provide technical support and theoretical basis for the prediction and prevention of RP. METHODS: A total of 100 female patients with pelvic tumors, who received static three-dimensional conformal intensity-modulated radiation therapy in the Department of Radiation Oncology of the Affiliated Hospital of Xiangnan University from January 2019 to December 2020, were retrospectively collected, and their clinically relevant data and radiotherapy planning system data were collected. During radiotherapy and 18 months after radiotherapy, 35 cases developed RP (RP group), and the remaining 65 cases had no RP (non-RP group). The clinical and dosimetric characteristics of patients were ranked by the importance of random forest algorithm, and the independent prognostic characteristics associated with the occurrence of RP were selected for machine learning modeling. A total of 6 machine learning algorithms including support vector machines, random forests, logistic regression, lightweight gradient boosting machines, Gaussian naïve Bayes, and adaptive enhancement were used to build models. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Finally, the random forest model was determined as the prediction model, and the calibration curve and decision curve of the prediction model were drawn to evaluate the accuracy and clinical benefit of the model. RESULTS: The parameters for random forest prediction model in the training set were as follow: AUC, 1.000, accuracy, 0.988, sensitivity, 1.000, specificity, 1.000, positive predictive value, 1.000, negative predictive value, 0.981, and F1 score, 1.000. In validation set, AUC was 0.713, accuracy was 0.640, sensitivity was 0.618, specificity was 0.822, positive predictive value was 0.500, negative predictive value was 0.656, and F1 score was 0.440. Random forest showed high predictive performance. Moreover, the Brief of the calibration curve for the prediction model was 0.178, the prediction accuracy was high, and the decision curve showed that the prediction model could benefit clinically. CONCLUSIONS Based on the clinical and dosimetric parameters for the female pelvic tumor patients, the prediction model of radiation proctitis constructed by random forest algorithm has high predictive ability and strong clinical usability.
Bayes Theorem ; Female ; Humans ; Machine Learning ; Pelvic Neoplasms/radiotherapy* ; Proctitis/etiology* ; Radiation Oncology ; Retrospective Studies