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Purpose This study shows the epidemiological profile of the first gastroenteritis outbreak of GII.P17 in the Romanian territory. An outbreak with such large amplitude in a European territory was previously undocumented. Patients and methods Using a cross-sectional design, with the susceptible-infected-recovered (SIR) deterministic compartmental model for a fixed population, and the cluster method for establishing the high-incidence zones, we carried out our investigation by means of questionnaires containing personal data, affected collectivities, disease onset and duration, symptoms displayed, medical assistance provided, previous antibiotic intake where applicable, food consumption and water sources, and sanitation conditions. The confirmation of cases was done based on the typical norovirus gastroenteritis symptomatology and using three laboratory confirmations (by molecular diagnosis) for GII.P17-GII.17 genotype noroviruses from three patients. Results A gastroenteritis outbreak occurred in October–November 2015, affecting 328 people in Arad, a county in Western Romania, covering 44 neighbouring localities with a total population of 35,440 people. The study detected an inter-human transmission of the infection, with an intrafamilial risk of disease of 2.26 (95% CI 1.76 to 2.90) compared with the community transmission (in school collectivity). The basic reproduction number Ro dropped from 1.26 to 0.18 during weeks 43:44, after controlling the transmission by decontamination and isolation. Conclusion SIR made it possible to highlight the expansion of the emerging norovirus strain infection from community to family collectivities. This study provides practical solutions to limit disease cases, even in the absence of etiology, and shows the importance of sometimes underestimated traditional control methods.

Purpose This study shows the epidemiological profile of the first gastroenteritis outbreak of GII.P17 in the Romanian territory. An outbreak with such large amplitude in a European territory was previously undocumented. Patients and methods Using a cross-sectional design, with the susceptible-infected-recovered (SIR) deterministic compartmental model for a fixed population, and the cluster method for establishing the high-incidence zones, we carried out our investigation by means of questionnaires containing personal data, affected collectivities, disease onset and duration, symptoms displayed, medical assistance provided, previous antibiotic intake where applicable, food consumption and water sources, and sanitation conditions. The confirmation of cases was done based on the typical norovirus gastroenteritis symptomatology and using three laboratory confirmations (by molecular diagnosis) for GII.P17-GII.17 genotype noroviruses from three patients. Results A gastroenteritis outbreak occurred in October-November 2015, affecting 328 people in Arad, a county in Western Romania, covering 44 neighbouring localities with a total population of 35,440 people. The study detected an inter-human transmission of the infection, with an intrafamilial risk of disease of 2.26 (95% CI 1.76 to 2.90) compared with the community transmission (in school collectivity). The basic reproduction number Ro dropped from 1.26 to 0.18 during weeks 43:44, after controlling the transmission by decontamination and isolation. Conclusion SIR made it possible to highlight the expansion of the emerging norovirus strain infection from community to family collectivities. This study provides practical solutions to limit disease cases, even in the absence of etiology, and shows the importance of sometimes underestimated traditional control methods.

This paper presents an Android prototype called HOLOTECH, a system to help blind people to understand obstacles in the environment. The goal of this paper is the analysis of different techniques and procedures to perform a fast and lightweight model able to detect obstacles in this context. The predictions and analysis are statistically evaluated, and results are used in order to improve the inference results. The model keeps working using low precision images drifted from an Android cell phone supported with ultrasonic sensors. Images are pre-processed on the fly, using multiple neural networks in conjunction with other heuristics, to infer obstacles and their displacement. Results indicate that it is possible to improve the prediction rate and at the same time to reduce extra processing load.

Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stages of cancer by using conventional methods. The gene expression microarray technology is applied to detect and diagnose most types of cancers in their early stages. Furthermore, it allows researchers to analyze thousands of genes simultaneously. To acquire knowledge from gene expression data, data mining methods are needed. Due to the rapid evolution of cancer detection and diagnosis techniques, a survey of modern techniques is desirable. This study reviews and provide a detailed description of these techniques. As a result, it helps to enhance existing techniques for cancer detection and diagnosis as well as guiding researchers to develop new approaches.

Heart Rate Variability (HRV) designates the progressive variation in the intervals between successive heartbeats in the sinus rhythm. The HRV analysis is a non-invasive/effective tool to demonstrate the influence of the autonomic nervous system over the heart rhythm regulation. Recently, researches are interested with developing advanced software systems for HRV analysis. For cardiac disease patients’ investigation, nonlinear modeling and analysis can keep track in real time and foresee possible changes in circadian heart rate. The current work presents a novel created software system for HRV analysis based on 24-h Holter ECG signals of group of healthy and unhealthy subjects. The nonlinear analysis of heart intervals was performed with the implementation of original high performance algorithms and software to quantify the heart rate irregularity. The proposed software system achieved the short time ability for parametric estimation of patients’ cardiac status. It is based on long-term (24-h) Holter ECG signals with implementation of mathematical nonlinear methods. The experimental results established that the designed software system for analysis of 24-h Holter recordings is appropriate for diagnostic, forecast and prevention of the pathological cardiac statuses. The developed and implemented graphical representation and visualization approaches for the results can be stored in specialized data base.

Classifier selection is a significant problem in machine learning to reduce the computational time and the number of ensemble members. Over the past decade, multiple classifier systems (MCS) have been actively exploited to enhance the classification accuracy. Finding a pertinent objective function for measuring the competence of base classifier is a critical issue to select the appropriate subset from a pool of classifiers. Along with the accuracy, diversity measures are designed as objective functions for ensemble selection. This current work proposed a new selection method based on accuracy and diversity in order to achieve better medical data classification performance. The classifiers correlation was calculated using Minimum Redundancy Maximum Relevance (mRMR) method based on relevance and diversity measures. Experiments were carried out on five data sets from UCI Machine Learning Repository and LudmilaKuncheva Collection. The experimental results proved the superiority of the proposed classifiers selection method.

The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means.

Osseointegration of a dental implant may encounter a variety of problems caused by various factors, as prior health-related problems, patients� habits and the technique of the implant inserting. Retrospective cohort study of 70 patients who received implants between January 2011- April 2016 in one dental unit, with Kaplan-Meier method to calculate the probability of implants�s survival at 60 months. The analysis included demographic data, age, gender, medical history, behavior risk factors, type and location of the implant. For this cohort the implants�survival for the first 6 months was 92.86% compared to the number of patients and 97.56% compared to the number of total implants performed, with a cumulative failure rate of 2.43% after 60 months. Failures were focused exclusively on posterior mandible implants, on the percentage of 6.17%, odds ratio (OR) for these failures being 16.76 (P = 0.05) compared with other localisations of implants, exclusively in men with median age of 42 years.

Despite advances in treatment, the prognosis for lung cancer patients remains poor. Angiogenesis appears to be a promising target for lung cancer therapy; however, the clinical significance of vascular changes are not completely understood. The aim of this study was to evaluate the types and morphology of blood vessels in various lung carcinomas. Using double immunostaining, we investigated 39 biopsies from patients admitted with various histological types of lung carcinoma. Tumor blood vessels were quantified separately for CD34/smooth muscle actin and described as either immature, intermediate or mature. Double immunostaining evaluation of the type of blood vessels in lung carcinomas revealed a marked heterogeneity. The immature and intermediate type of vessels were more common in adenocarcinomas (ADCs) and squamous cell carcinomas (SCCs) of the lung. Small cell lung carcinomas revealed a significant correlation between pathological and immature types of blood vessels. Therefore, quantifying the types of tumor vessels in lung carcinomas may be an important element to improve the results of anti-vascular therapy.

Vertebral column diseases are of the main public health problems which cause a negative impact on patients. Disk hernia and spondylolisthesis are examples of pathologies of the vertebral column which cause intensive pain. Data mining tools play an important role in medical decision making and deal with human short-term memory limitations quite effectively. This paper presents a decision support tool that can help in detection of pathology on the vertebral column using three types of decision trees classifiers. They are Single Decision Tree (SDT), Boosted Decision Tree (BDT), and Decision Tree Forest (DTF). Decision Tree and Regression (DTREG) software package is used for simulation and the database is available from UCI Machine Learning Repository. The performance of the proposed structure is evaluated in terms of accuracy, sensitivity, specificity, ROC curves, and other metrics. The results showed that the accuracies of SDT and BDT in the training phase are 90.65 and 96.77 %, respectively. BDT performed better than SDT for all performance metrics. Value of ROC for BDT in the training phase is 0.9952. In the validation phase, BDT achieved 84.84 % accuracy, which is superior to SDT (81.94 %) and DTF (84.19 %). Results showed also that grade of spondylolisthesis is the most relevant feature for classification using BDT classifier.

Background: Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. Objectives: In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patients and Methods: Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden’s Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. Results: With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden’s Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. Conclusions: There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.

Betulinic acid, a very promising anti-melanoma agent, has very low water solubility that causes low bioavailability. To overcome this inconvenience, a highly water-soluble cyclodextrin was used (octakis-[6-deoxy-6-(2-sulfanyl ethanesulfonic acid)]-γ-cyclodextrin). The complex was physico-chemically analyzed using differential scanning calorimetry (DSC), X-ray and scanning electron microscopy (SEM) methods and then in vitro tested for its antiproliferative activity by the MTT assay and by cell cycle analysis. Finally, the complex was tested in vivo using an animal model of murine melanoma developed in C57BL/6J mice, where it caused a reduction in tumor volume and weight. The study revealed the beneficial influence of betulinic acid inclusion into the cyclodextrin in terms of antiproliferative activity and in vivo tumor development.

This chapter demonstrates the development of a brain computer interface (BCI) decision support system for controlling the movement of a wheelchair for neurologically disabled patients using their Electroencephalography (EEG). The subject was able to imagine his/her hand movements during EEG experiment which made EEG oscillations in the signal that could be classified by BCI. The BCI will translate the patient’s thoughts into simple wheelchair commands such as “go” and “stop”. EEG signals are recorded using 59 scalp electrodes. The acquired signals are artifacts contaminated. These artifacts were removed using blind source separation (BSS) by independent component analysis (ICA) to get artifact-free EEG signal from which certain features are extracted by applying discrete wavelet transformation (DWT). The extracted features were reduced in dimensionality using principal component analysis (PCA). The reduced features were fed to neural networks classifier yielding classification accuracy greater than 95 %.

The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50–50%, 60–40%, 70–30% and 80–20%. The highest classification accuracy of 95.7746% was achieved for 80–20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50–50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70–30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60–40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80–20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) post-dialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated postdialysis blood urea (C eq ) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. In this work a supervised Artificial Neural Network (ANN) is proposed for predicting equilibrated urea concentration taken at 30 min after the end of the hemodialysis (HD) session in order to predict PDUR. The advantage of ANN approach is that it doesn't require 30–60-minute post-dialysis urea sample. This approach is compared experimentally with other traditional methods for predicting equilibrated urea concentration (C eq ), PDUR and equilibrated dialysis dose ( eq Kt/V). The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms other traditional urea kinetic models (UKM) and the previous work using ANN.

Visceral Leishmaniasis is a multi-organic parasitic disease caused by an intracellular protozoon named Leishmania Donovani; the mean signs are: weight loss, cough, fever, hepatosplenomegaly, adenopathy and cutaneous lesions; death without treatment is the rule. The main treatment is a conservative one. Surgical treatment is necessary for complications, especially for those intra-abdominally. We wish to present a young female patient who underwent two subsequent interventions due to an unclear diagnosis. We emphasize the difficulties in achieving a certain diagnostic, because of the rarity of disease in Romania; there are also revealed surgical aspects, which are important because of very few available data in the literature.

The onset for rectal cancer is often revealed by one of its complications. With lower frequency, for the rectum below peritoneal reflexion, the perforative-suppurative onset is possible. We wish to expose a 64 years old woman's case, presenting with Fournier's gangrene as a unique sign of a lower rectal adenocarcinoma. Diabetes mellitus determined the extension of infectious process to its ultimate form, and the poor postoperative evolution, leading to death as well. The need for rectal examination is obvious in perianal abscesses, even in absence of malignant signs.