BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.

Numerical study of non-Darcy hybrid nanofluid flow with the effect of heat source and hall current over a slender extending sheet.

The current evaluation described the flow features of Darcy Forchhemier hybrid nanoliquid across a slender permeable stretching surface. The consequences of magnetic fields, second order exothermic reaction, Hall current and heat absorption and generation are all accounted to the fluid flow. In the working fluid, silicon dioxide (SiO2) and titanium dioxide (TiO2) nano particulates are dispersed to prepare the hybrid nanoliquid. TiO2 and SiO2 NPs are used for around 100 years in a vast number of diverse products. The modeled has been designed as a nonlinear set of PDEs, Which are degraded to the dimensionless system of ODEs by using the similarity transformation. The reduced set of nonlinear ODEs has been numerically estimated through bvp4c package. The outcomes are tested for validity and consistency purpose with the published report and the ND solve technique. It has been noted that the energy curve lessens with the influence of thermodiffusion, Brownian motion and rising number of nanoparticles, while boosts with the result of magnetic field. Furthermore, the concentration outline of hybrid nanoliquid improves with the upshot of chemical reaction.

Establishment of a Potential Serum Biomarker Panel for the Diagnosis and Prognosis of Cholangiocarcinoma Using Decision Tree Algorithms.

Potential biomarkers which include S100 calcium binding protein A9 (S100A9), mucin 5AC (MUC5AC), transforming growth factor ß1 (TGF-ß1), and angiopoietin-2 have previously been shown to be effective for cholangiocarcinoma (CCA) diagnosis. This study attempted to measure the sera levels of these biomarkers compared with carbohydrate antigen 19-9 (CA19-9). A total of 40 serum cases of CCA, gastrointestinal cancers (non-CCA), and healthy subjects were examined by using an enzyme-linked immunosorbent assay. The panel of biomarkers was evaluated for their accuracy in diagnosing CCA and subsequently used as inputs to construct the decision tree (DT) model as a basis for binary classification. The findings showed that serum levels of S100A9, MUC5AC, and TGF-ß1 were dramatically enhanced in CCA patients. In addition, 95% sensitivity and 90% specificity for CCA differentiation from healthy cases, and 70% sensitivity and 83% specificity for CCA versus non-CCA cases was obtained by a panel incorporating all five candidate biomarkers. In CCA patients with low CA19-9 levels, S100A9 might well be a complementary marker for improved diagnostic accuracy. The high levels of TGF-ß1 and angiopoietin-2 were both associated with severe tumor stages and metastasis, indicating that they could be used as a reliable prognostic biomarkers panel for CCA patients. Furthermore, the outcome of the CCA burden from the Classification and Regression Tree (CART) algorithm using serial CA19-9 and S100A9 showed high diagnostic efficiency. In conclusion, results have shown the efficacy of CCA diagnosis and prognosis of the novel CCA-biomarkers panel examined herein, which may prove be useful in clinical settings.

Circulating TGF-ß1 as the potential epithelial mesenchymal transition-biomarker for diagnosis of cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a malignant tumor arising from bile duct epithelium. The oncogenic risk factor is infection by the liver fluke, Opisthorchis viverrini (Ov). One of key mechanism in the development of CCA is epithelial mesenchymal transition (EMT). We aimed to investigate the expression of EMT-related proteins namely, E-cadherin, TGF-ß1 and BMP-7 in CCA tissues, to determine the level of candidate EMT-related protein, and to examine whether there were significant correlations with clinicopathological data in sera of CCA patients compared with normal groups. METHODS: The expression of E-cadherin, TGF-ß1 and BMP-7 was analyzed in human CCA tissues by immunohistochemistry and altered expressions compared to clinicopathological data were analyzed to identify the potential candidate EMT-biomarker. Subsequently, the level of candidate marker was determined in sera of CCA patients compared with normal and inflammatory-related diseases groups by enzyme-linked immunosorbent assay (ELISA). RESULTS: Immunohistochemical analysis showed that E-cadherin was expressed at a low level whereas TGF-ß1 and BMP-7 showed high expression in CCA tissues when compared with liver from cadaveric donor. Interestingly, only high TGF-ß1 expression in CCA tissues was significantly correlated with lymph node metastasis, severe cancer stage, intrahepatic CCA type and shorter survival time of CCA patients (P<0.05). Consequently, TGF-ß1 was selected to determine the level in serum of CCA patients using ELISA. The results showed that serum TGF-ß1 level was elevated in CCA patients compared to the normal group. Patients with high TGF-ß1 levels were significantly correlated with metastasis status (P=0.03). Furthermore, receiver operating characteristic (ROC) analysis showed that serum TGF-ß1 level is effective in distinguishing CCA patients from normal at the cut-off of 38.54 ng/mL with high sensitivity (71.1%) and specificity (68.9%) and from inflammatory-related diseases group at the cut-off of 38.67 ng/mL with effective sensitivity (68.0%) and specificity (71.1%). Furthermore, TGF-ß1 could serve as a novel metastatic biomarker in CCA to diagnose the disease with 48.95 ng/mL as the cut-off along with the desired sensitivity and specificity (48.2% and 88.9% respectively). CONCLUSIONS: The results of this study show that TGF-ß1 could be a potential EMT-biomarker for diagnosis and prognosis of CCA.

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity.

Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.