Appraisal of different ultrasonography indices in patients with carotid artery atherosclerosis

In this study a semi-automated image-processing based method was designed in which the parameters such as intima-media thickness (IMT), resistive index (RI), pulsatility index (PI), dicrotic notch index (DNI), and mean wavelet entropy (MWE) were evaluated in B-mode and Doppler ultrasound in patients presenting with carotid artery atherosclerosis. In a cross-sectional design, 144 men were divided into four groups of control, mild, moderate and severe stenosis subjects. In all individuals, far wall IMT, RI, PI, DNI, and MWE of the left common carotid artery (CCA) were extracted using the proposed method. Our findings showed that the maximum far wall IMT, RI, PI, DNI in the CCA were significantly different in the patients with mild, moderate, and severe stenosis compared to control group (p-value 0.05). The proposed method can help physicians to better identify patients at risk of cardiovascular diseases

A Novel Feature Selection Method for Microarray Data Classification Based on Hidden Markov Model

In this paper, a novel approach is introduced for integrating multiple feature selection criteria by using hidden Markov model (HMM). For this purpose, five feature selection ranking methods including Bhattacharyya distance, entropy, receiver operating characteristic curve, t-test, and Wilcoxon are used in the proposed topology of HMM. Here, we presented a strategy for constructing, learning and inferring the HMM for gene selection, which led to higher performance in cancer classification. In this experiment, three publicly available microarray datasets including diffuse large B-cell lymphoma, leukemia cancer and prostate were used for evaluation. Results demonstrated the higher performance of the proposed HMM-based gene selection against Markov chain rank aggregation and using individual feature selection criterion, where applied to general classifiers. In conclusion, the proposed approach is a powerful procedure for combining different feature selection methods, which can be used for more robust classification in real world applications

Hypoxic burden to guide CPAP treatment allocation in patients with obstructive sleep apnoea : a post hoc study of the ISAACC trial

Hypoxic burden (HB) has emerged as a strong predictor of cardiovascular risk in obstructive sleep apnoea (OSA). We aimed to assess the potential of HB to predict the cardiovascular benefit of treating OSA with continuous positive airway pressure (CPAP). This was a post hoc analysis of the ISAACC trial (: NCT01335087) including non-sleepy patients with acute coronary syndrome (ACS) diagnosed with OSA (apnoea-hypopnoea index ≥15 events·h −1) by respiratory polygraphy. Patients were randomised to CPAP or usual care and followed for a minimum of 1 year. HB was calculated as the total area under all automatically identified desaturations divided by total sleep time. Patients were categorised as having high or low baseline HB according to the median value (73.1%min·h −1). Multivariable Cox regression models were used to assess whether the effect of CPAP on the incidence of cardiovascular outcomes was dependent on the baseline HB level. The population (362 patients assigned to CPAP and 365 patients assigned to usual care) was middle-aged (mean age 59.7 years), overweight/obese and mostly male (84.5%). A significant interaction was found between the treatment arm and the HB categories. In the high HB group, CPAP treatment was associated with a significant reduction in the incidence of cardiovascular events (HR 0.57, 95% CI 0.34-0.96). In the low HB group, CPAP-treated patients exhibited a trend toward a higher risk of cardiovascular outcomes than those receiving usual care (HR 1.33, 95% CI 0.79-2.25). The differential effect of the treatment depending on the baseline HB level followed a dose-response relationship. In non-sleepy ACS patients with OSA, high HB levels were associated with a long-term protective effect of CPAP on cardiovascular prognosis

Design and Implementation of a Customable Automatic Vehicle Location System in Ambulances and Emergency Vehicle Systems

Background: Automatic vehicle location (AVL) refers to a system that calculates the geographical location of any vehicle, i.e., latitude and longitude. Vehicle location information about one or more moving vehicles can be stored in the internal memory and accessed when vehicles are available (offline tracking). It is also possible to get location information on a real-time basis (online tracking). The real-time tracking systems designed to date may incorporate three devices: global positioning system (GPS), geographic information system, and cellular communication platforms that may be either a general packet radio service (GPRS) or any private and local radiofrequency network. Methods: The GPS-based navigation system has been designed so as to allow for user-friendly real-time tracking applications for any emergency vehicles like ambulances. First, GPS coordinates are obtained from the SIM908 module and sent via to a server transmission control protocol/internet protocol. Server codes, which are written in C#, load Google map to show real-time location. Results: We designed online tracking AVL hardware in the two simple and advanced versions. The latter enables both the ambulance driver and the data center to monitor path real-time besides enabling the vehicle driver to receive and make calls and send or receive messages. The former only sends latitude and longitude to the data server continuously, and the path travelled by vehicle is displayed. Conclusion: SIM908 integrates GSM, GPRS, and GPS in one package. It can be a proper choice for real-time economic tracking systems despite its low accuracy in finding geolocations

An optimized framework for cancer prediction using immunosignature

Background: Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma. Methods: In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers. Results: The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm. Conclusion: The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

Abstract Background DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach. Methods In the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach. Results By applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced. Conclusions The combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis. Clinical trial number Not applicable

An ensemble soft weighted gene selection-based approach and cancer classification using modified metaheuristic learning

Abstract Hybrid algorithms are effective methods for solving optimization problems that rarely have been used in the gene selection procedure. This paper introduces a novel modified model for microarray data classification using an optimized gene subset selection method. The proposed approach consists of ensemble feature selection based on wrapper methods using five criteria, which reduces the data dimensions and time complexity. Five feature ranking procedures, including receiver operating characteristic curve, two-sample T-test, Wilcoxon, Bhattacharyya distance, and entropy, are used in the soft weighting method. Besides, we proposed a classification method that used the support vector machine (SVM) and metaheuristic algorithm. The optimization of the SVM hyper-parameters for the radial basis function (RBF) kernel function is performed using a modified Water Cycle Algorithm (mWCA). The results indicate that the ensemble performance of genes-mWCA SVM (EGmWS) is considered an efficient method compared to similar approaches in terms of accuracy and solving the uncertainty problem. Five benchmark microarray datasets, including leukemia, MicroRNA-Breast, diffuse large B-cell lymphoma, prostate, and colon, are employed for experiments. The highest and lowest numbers of genes are related to prostate with 12 533 genes and MicroRNA-Breast with 1926 genes, respectively. Besides, the highest and lowest numbers of samples are MicroRNA-Breast with 132 samples and colon with 62 samples, respectively. The results of classifying all data by applying effective genes of the EF-WS yielded high accuracies in microarray data classification. In addition to the robustness and simplicity of the proposed method, the model’s generalizability is another crucial aspect of the method that can be further developed to increase the accuracy while reducing classification error.</jats:p