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Introduction: Iron deficiency anemia (IDA) and β-thalassemia trait (β-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and β-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. Aim: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and β-TT in comparison with traditional methods. Methods: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and β-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and β-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. Result: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with β-TT. Conclusion: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from β-TT and assist in accurate and timely manner diagnosis.

Introduction: High blood pressure or hypertension is one of the chronic diseases causing other serious diseases and syndromes. Active involvement of the patient in the management of the disease is crucial in improving self-care and clinical outcomes. Mobile technology is nowadays used widely to improve the self-care process in people with chronic diseases such as hypertension. Aim: The objective of this study was to provide an overview of the existing research evaluating the impact of mobile applications on the self-care of patients with hypertension. Methods: The Scopus and PubMed databases were investigated using a comprehensive search strategy from the beginning of 2010 to 2019. All controlled clinical trial studies as well as quasi-experimental studies used mobile as a device for improving the self-care and conducted on patients with hypertension were included in the study. The studies were reviewed by two independent individuals. Results: Out of 1032 studies found, 6 studies were finally reviewed after applying the inclusion criteria. Out of 6 studies reviewed, three studies confirmed the effect of using mobile applications on lowering blood pressure. Other studies reported a decline in blood pressure, while statistically significant were not shown. Conclusion: The results showed that mobile apps have positive potential on improving the self-care behavior of patients with hypertension, but the evidences presenting their impact are varied. Different reports for efficiency of mobile phone apps for the self-care modification was due to diverse condition of studies for mobile intervention on the patients with hypertension.

The coronavirus disease (COVID-19) spread rapidlyaround the world. Two types of approaches have beenapplied to use of face masks as a tool to prevent the spreadthis disease in society. The aim of the systematic reviewwas to assess the effectiveness of face masks against thenovel coronavirus. A literature search was performed usingdifferent databases until April 30, 2020. Search termswere ‘facemasks’, ‘novel coronavirus’, and ‘healthcareworkers’. Five studies were included in the systematicreview. A study stated that no difference between surgicaland cotton masks. Also, two studies have emphasized theuse of surgical masks or N95 respirators by medical staff,and two other studies emphasized the use of any type offace mask by general public. More studies in controlledcontexts and studies of infections in healthcare andcommunity places are needed for better definition of theeffectiveness of face masks in preventing coronavirus.

In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.

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Background Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.