Nanjing Medical University

About: Nanjing Medical University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Cancer & Cell growth. The organization has 52221 authors who have published 37904 publications receiving 635831 citations. The organization is also known as: National Jiangsu Medical College & Nanjing Medical College.

Color-Multiplexing-Based Fluorescent Test Paper: Dosage-Sensitive Visualization of Arsenic(III) with Discernable Scale as Low as 5 ppb

Abstract: Fluorescent colorimetry test papers are promising for the assays of environments, medicines, and foods by the observation of the naked eye on the variations of fluorescence brightness and color. Unlike dye-absorption-based pH test paper, however, the fluorescent test papers with wide color-emissive variations with target dosages for accurate quantification remain unsuccessful even if the multicolorful fluorescent probes are used. Here, we report the dosage-sensitive fluorescent colorimetry test paper with a very wide/consecutive “from red to cyan” response to the presence and amount of arsenic ions, As(III). Red quantum dots (QDs) were modified with glutathione and dithiothreitol to obtain the supersensitivity to As(III) by the quenching of red fluorescence through the formation of dispersive QDs aggregates. A small amount of cyan carbon dots (CDs) with spectral blue-green components as the photostable internal standard were mixed into the QDs solution to produce a composited red fluorescence. Upon the ad...

The long noncoding RNA SPRY4-IT1 increases the proliferation of human breast cancer cells by upregulating ZNF703 expression

Abstract: Long noncoding RNAs (lncRNAs) have emerged recently as a new class of genes that regulate cellular processes, such as cell growth and apoptosis. The SPRY4 intronic transcript 1 (SPRY4-IT1) is a 708-bp lncRNA on chromosome 5 with a potential functional role in tumorigenesis. The clinical significance of SPRY4-IT1 and the effect of SPRY4-IT1 on cancer progression are unclear. Quantitative reverse transcriptase PCR (qRT-PCR) was performed to investigate the expression of SPRY4-IT1 in 48 breast cancer tissues and four breast cancer cell lines. Gain and loss of function approaches were used to investigate the biological role of SPRY4-IT1 in vitro. Microarray bioinformatics analysis was performed to identify the putative targets of SPRY4-IT1, which were further verified by rescue experiments, and by western blotting and qRT-PCR. SPRY4-IT1 expression was significantly upregulated in 48 breast cancer tumor tissues comparedwith normal tissues. Additionally, increased SPRY4-IT1 expression was found to be associated with a larger tumor size and an advanced pathological stage in breast cancer patients. The knockdown of SPRY4-IT1 significantly suppressed proliferation and caused apoptosis of breast cancer cells in vitro. Furthermore, we discovered that ZNF703 was a target of SPRY4-IT1 and was downregulated by SPRY4-IT1 knockdown. Moreover, we provide the first demonstration that ZNF703 plays an oncogenic role in ER (−) breast carcinoma cells. SPRY4-IT1 is a novel prognostic biomarker and a potential therapeutic candidate for breast cancer.

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI

Abstract: Background Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). Purpose To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Study type Retrospective. Subjects In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. Sequence T2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. Assessment A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Statistical test Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. Results The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. Data conclusion The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer-aided diagnosis (CAD) for PCa classification. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1570-1577.

Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes.

Abstract: Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method. Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 70 consecutive patients with BTC between January 2017 and February 2018. Results The radiomics signature, composed of three LN-status-related features, was associated with LN metastasis in primary and validation cohorts (P < .001). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status showed good calibration and discrimination in primary cohort (area under the curve, 0.81) and validation cohort (area under the curve, 0.80). Patients at high risk of LN metastasis portended lower disease-specific and recurrence-free survival than did those at low risk after surgery (both P < .001). High-risk LN metastasis was an independent preoperative predictor of disease-specific survival (hazard ratio, 3.37; P < .001) and recurrence-free survival (hazard ratio, 1.98; P = .003). Conclusion A radiomics model derived from portal phase CT of the liver has good performance for predicting lymph node metastasis in biliary tract cancer and may help to improve clinical decision making. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Laghi and Voena in this issue.