In vitro strain measurements in cerebral aneurysm models for cyber-physical diagnosis

Background: The development of new diagnostic technologies for cerebrovascular diseases requires an understanding of the mechanism behind the growth and rupture of cerebral aneurysms. To provide a comprehensive diagnosis and prognosis of this disease, it is desirable to evaluate wall shear stress, pressure, deformation and strain in the aneurysm region, based on information provided by medical imaging technologies. Methods: In this research, we propose a new cyber-physical system composed of in vitro dynamic strain experimental measurements and computational fluid dynamics (CFD) simulation for the diagnosis of cerebral aneurysms. A CFD simulation and a scaled-up membranous silicone model of a cerebral aneurysm were completed, based on patient-specific data recorded in August 2008. In vitro blood flow simulation was realized with the use of a specialized pump. A vision system was also developed to measure the strain at different regions on the model by way of pulsating blood flow circulating inside the model. Results: Experimental results show that distance and area strain maxima were larger near the aneurysm neck (0.042 and 0.052), followed by the aneurysm dome (0.023 and 0.04) and finally the main blood vessel section (0.01 and 0.014). These results were complemented by a CFD simulation for the addition of wall shear stress, oscillatory shear index and aneurysm formation index. Diagnosis results using imaging obtained in August 2008 are consistent with the monitored aneurysm growth in 2011. Conclusion: The presented study demonstrates a new experimental platform for measuring dynamic strain within cerebral aneurysms. This platform is also complemented by a CFD simulation for advanced diagnosis and prediction of the growth tendency of an aneurysm in endovascular surgery.

One year of sitagliptin treatment protects against islet amyloid-associated β-cell loss and does not induce pancreatitis or pancreatic neoplasia in mice

The dipeptidyl peptidase-4 (DPP-4) inhibitor sitagliptin is an attractive therapy for diabetes, as it increases insulin release and may preserve β-cell mass. However, sitagliptin also increases β-cell release of human islet amyloid polypeptide (hIAPP), the peptide component of islet amyloid, which is cosecreted with insulin. Thus, sitagliptin treatment may promote islet amyloid formation and its associated β-cell toxicity. Conversely, metformin treatment decreases islet amyloid formation by decreasing β-cell secretory demand and could therefore offset sitagliptin's potential proamyloidogenic effects. Sitagliptin treatment has also been reported to be detrimental to the exocrine pancreas. We investigated whether long-term sitagliptin treatment, alone or with metformin, increased islet amyloid deposition and β-cell toxicity and induced pancreatic ductal proliferation, pancreatitis, and/or pancreatic metaplasia/neoplasia. hIAPP transgenic and nontransgenic littermates were followed for 1 yr on no treatment, sitagliptin, metformin, or the combination. Islet amyloid deposition, β-cell mass, insulin release, and measures of exocrine pancreas pathology were determined. Relative to untreated mice, sitagliptin treatment did not increase amyloid deposition, despite increasing hIAPP release, and prevented amyloid-induced β-cell loss. Metformin treatment alone or with sitagliptin decreased islet amyloid deposition to a similar extent vs untreated mice. Ductal proliferation was not altered among treatment groups, and no evidence of pancreatitis, ductal metaplasia, or neoplasia were observed. Therefore, long-term sitagliptin treatment stimulates β-cell secretion without increasing amyloid formation and protects against amyloid-induced β-cell loss. This suggests a novel effect of sitagliptin to protect the β-cell in type 2 diabetes that appears to occur without adverse effects on the exocrine pancreas.

Peptide-fluorescent bacteria complex as luminescent reagents for cancer diagnosis

Currently in clinic, people use hematoxylin and eosin stain (H&E stain) and immunohistochemistry methods to identify the generation and genre of cancers for human pathological samples. Since these methods are inaccurate and time consuming, developing a rapid and accurate method to detect cancer is urgently demanded. In our study, binding peptides for lung cancer cell line A549 were identified using bacteria surface display method. With those binding peptides for A549 cells on the surface, the fluorescent bacteria (Escherichia coli with stably expressed green fluorescent protein) were served as specific detecting reagents for the diagnosis of cancers. The binding activity of peptide-fluorescent bacteria complex was confirmed by detached cancer cells, attached cancer cells and mice tumor xenograft samples. A unique fixation method was developed for peptide-bacteria complex in order to make this complex more feasible for the clinic use. This peptide-fluorescent bacteria complex has great potential to become a new diagnostic tool for clinical application.

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

The initial laboratory diagnosis of equine influenza in Australia in 2007

Until August 2007, Australia had not recorded an outbreak of equine influenza; indeed, significant quarantine precautions exist to safeguard against such an event. This article outlines the lead up to virus confirmation and the procedures to first test, then contain it.