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Introduction The use of telemedicine in orthopaedics can provide high-quality orthopaedic services to patients in remote areas. Tele-orthopaedics is widely acknowledged for decreasing travel, time and cost, increasing accessibility and quality of care. In the absence of a comprehensive review on tele-orthopaedics applications and services, here, we systematically identify and classify the tele-orthopaedic applications and services and provide an overview of the trends in the field. Methods In this study, a systematic mapping was conducted to answer six research questions, we searched the databases Scopus, PubMed, IEEE Digital Library and Web of Science up to 2019. Consequently, 77 papers were screened and selected on the basis of specific inclusion and exclusion criteria. Results We found that mobile-based teleconsultation was mostly asynchronous, while non-mobile teleconsultation was synchronous. The results showed that the physician–patient relationship was more common than other interactions, such as physician–physician and physician–robot interactions. In addition, more than half of the services provided by tele-orthopaedics have been used for orthopaedic diseases/traumas in which joint replacement and fracture reduction have been the most important orthopaedic procedures. It has been noted that more attention has been paid to tele-orthopaedics in developed countries such as the USA, Australia, Canada and Finland. Discussion Telemonitoring (teleconsultation and telemetry) and telesurgery (telerobotics and telementoring) were found to be the two major forms of tele-orthopaedics. Mobile phones were used asynchronously in most of the teleconsultations. The development of different applications may result in the use of multiple smartphones applications in real-time teleconsultation. The use of smartphones is expected to increase in the near future.

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Nowadays, applications for the Internet of Things (IoT) have been introduced in different fields of medicine to provide more efficient medical services to the patients. A systematic mapping study was conducted to answer ten research questions with the purposes of identifying and classifying the present medical IoT technological features as well as recognizing the opportunities for future developments. We reviewed how cloud, wearable technologies, wireless communication technologies, messaging protocols, security methods, development boards, microcontrollers, mobile/IoT operating systems, and programming languages have been engaged in medical IoT. Based on specific inclusion/exclusion criteria, 89 papers, published between 2000 and 2018, were screened and selected. It was found that IoT studies, with a publication rise between 2015 and 2018, predominantly dealt with the following IoT features: (a) wearable sensor types of chiefly accelerometer and ECG placed on 16 different body parts, especially the wrist (33%) and the chest (21%) or implanted on the bone; (b) wireless communication technologies of Bluetooth, cellular networks, and Wi-Fi; (c) messaging protocols of mostly MQTT; (d) utilizing cloud for both storing and analyzing data; (e) the security methods of encryption, authentication, watermark, and error control; (f) the microcontrollers belonging to Atmel ATmega and ARM Cortex-M3 families; (g) Android as the commonly used mobile operating system and TinyOS and ContikiOS as the commonly used IoT operating systems; (h) Arduino and Raspberry Pi development boards; and finally (i) MATLAB as the most frequently employed programming language in validation research. The identified gaps/opportunities for future exploration are, namely, employment of fog/edge computing in storage and processing big data, the overlooked efficient features of CoAP messaging protocol, the unnoticed advantages of AVR Xmega and Cortex-M microcontroller families, employment of the programming languages of Python for its significant capabilities in evaluation and validation research, development of the applications being supported by the mobile/IoT operating systems in order to provide connection possibility among all IoT devices in medicine, exploiting wireless communication technologies such as BLE, ZigBee, 6LoWPAN, NFC, and 5G to reduce power consumption and costs, and finally uncovering the security methods, usually used in IoT applications, in order to make other applications more trustworthy.

Background. In today’s industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods. In this study, different ML algorithms’ performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results. Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81, F-measure = 0.81, ROC = 0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies and a major health problem worldwide. There were no major advances in conventional treatments in inhibiting tumor progression and increasing patient survival time. In order to suppress mechanisms responsible for tumor cell development such as those with oncogenic roles, more advanced therapeutic strategies should be sought. One of the most important oncogenes of pancreatic cancer is the MYC gene. The overexpression of MYC can activate many tumorigenic processes such as cell proliferation and pancreatic cancer cell invasion. MiRNAs are important molecules that are confirmed by targeting mRNA transcripts to regulate the expression of the MYC gene. Therefore, restoring MYC-repressing miRNAs expression tends to be an effective method of treating MYC-driven cancers. Objective: The purpose of this study was to identify all validated microRNAs targeting C-MYC expression to inhibit PDAC progression by conducting a systematic review. Methods: In this systematic review study, the papers published between 2000 and 2020 in major online scientific databases including PubMed, Scopus, and Web of Science were screened, following inclusion and exclusion criteria. We extracted all the experimental studies that showed miRNAs could target the expression of the MYC gene in PDAC. Results: Eight papers were selected from a total of 89 papers. We found that six miRNAs (Let-7a, miR-145, miR-34a, miR-375, miR-494, and miR-148a) among the selected studies were validated for targeting MYC gene and three of them confirmed Let-7a as a direct MYC expression regulator in PC cells. Finally, we summarized the latest shreds of evidence of experimentally validated miRNAs targeting the MYC gene with respect to PDAC's therapeutic potential. Conclusion: Restoring the expression of MYC-repressing miRNAs tends to be an effective way to treat MYC-driven cancers such as PDAC. Several miRNAs have been proposed to target this oncogene via bioinformatics tools, but only a few have been experimentally validated for pancreatic cancer cells and models. Further studies should be conducted to find the interaction network of miRNA-MYC to develop more successful therapeutic strategies for PC, using the synergistic effects of these miRNAs.

IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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