[Application of Intermittent Lung Inflation Combined with Rigid Ureteroscopy in Treating Upper Ureteral Stones That Were Not Fully Visible].

Objective To analyze the effectiveness and safety of intermittent lung inflation combined with rigid ureteroscopy in the treatment of upper ureteral stones that were not fully visible. Methods The clinical and imaging data of 56 patients with upper ureteral stone undergoing rigid ureteroscopic lithotripsy combined with intermittent lung inflation in Zhejiang Quhua Hospital from March 2016 to October 2017 were retrospectively analyzed.Intermittentt lung inflation was used to change and stabilize the position of ureteral calculi during the operation,so as to ensure the visual field of ureteroscopy.Holmium laser lithotripsy was performed to remove the stones.Urinary tract abdominal plain X-ray or CT urography was performed 1 and 3 months after the operation to evaluate the residual stones and the clinical efficacy. Results Stones were successfully removed after a single attempt in 48 patients.In 5 patients,stones escaped into the kidney during ureteroscopic lithotripsy,and thus flexible ureteroscopy were performed.In 3 patients,a second session of auxiliary procedure was required,among whom 2 patients received extracorporeal shock wave lithotripsy and 1 patient underwent extracorporeal shock wave lithotripsy+ureteroscopic lithotripsy.The stone-free rates 1 and 3 months after surgery were 94.6%(53/56)and 100%(56/56),respectively.No severe complication such as ureter perforation,gross hematuria,septic shock,or pneumothorax occurred during and after surgery. Conclusions Intermittent lung inflation in tracheal intubation under general anesthesia in patients with proximal ureteral stones that can not be fully visible during rigid ureteroscopic lithotripsy was feasible and reliable.It can effectively change the location of stones and thus enable safe and effective lithotripy.It expands the indications of rigid ureteroscopy for treating upper ureteral stones.

Serum level of co-expressed hub miRNAs as diagnostic and prognostic biomarkers for pancreatic ductal adenocarcinoma.

Background: Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC. Methods: A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis. Results: Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five "real" miRNAs were able to stratify PDAC patients into low-risk and high-risk groups. Conclusions: The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.

The current and future potential geographic range of West Indian fruit fly, Anastrepha obliqua (Diptera: Tephritidae).

The West Indian fruit fly, Anastrepha obliqua (Macquart), is one of the most important pests throughout the Americas. CLIMEX 3.0 and ArcGIS 9.3 were used to model the current and future potential geographical distribution of this pest. Under current climatic conditions, A. obliqua is predicted to be able to establish throughout much of the tropics and subtropics, including not only North and South America, where it has been reported, but also southern Asia, northeastern Australia and Sub-Saharan Africa. The main factors limiting the pest's range expansion may be cold stress. Climate change expands the potential distribution of A. obliqua poleward as cold stress boundaries recede, but the predicted distribution in northwestern Australia and northern parts of Sub-Saharan Africa will decrease because of heat stress. Considering the widely suitable range for A. obliqua globally and in China, enhanced quarantine and monitoring measures should be implemented in areas that are projected to be suitable for the establishment of the pest under current and future climatic conditions.

[The preliminary research on information redundance in fluorescence spectral data process].

This paper presented the problem of information redundance factor in spectral data analysis. Three-component mixtures fluorescence spectra data of anthracene, pyrene and phenanthrene, which were typical polycyclic aromatic hydrocarbons (PAHs), were investigated through principal component regression analysis and move windows wavelength selection in chemometrics. Theoretical analysis and experimental result demonstrated that there was information redundancy in fluorescence signal; the data which have been optimized by redundancy techniques could show more realistic information of the test samples quantitatively, and for the multi-component mixed system with spectral overlapped seriously, analysis with the data which have been optimized through redundancy techniques could improve the sensitivity and stability of the models.

[The quantitative analysis of polycomponent PAHs by netural network based on data synthese and principal].

The present paper used synthesized data from the experiment samples to replace partial basic experiments, and increased the training samples amount from 14 to 27. In principal component analysis (PCA), the dimensionality of multivariate data was reduced to n principal components and almost all data information was kept. The PCA reduced the network's input nodes from 60 to 3 to simplify the neural network's structure. Finally, back-propagation neural network was used to train and predict these samples. It had 27 training samples, the input layer had three nodes, the hidden layer had two nodes, and the output layer had two nodes. Its excitation function is variable learning rate method. The results show that the coefficient of recovery can reach 89.6-109.0. It has reached the expected purpose.