مجله انفورماتیک سلامت و زیست پزشکی
سال دهم شماره 3 (پیاپی 37، پاییز 1402)

People with weakened immune system are more prone to opportunistic infections (OIs). Finding the association of these viruses and the transmission pathways associated with these viruses, such as EBV and CMV, play a significant role in investigating their association with helper T lymphocytes. In this study, bioinformatics analysis was used to examine and candidate genes in pathways associated with opportunistic viruses with helper T lymphocytes.

In this study, by referring to the GEO database, a suitable database was selected for analysis. This dataset included gene expression profiles in EBV and CMV virus infections. Gene clusters were classified into high and low expression. Rich databases such as Enrichr, STRING, and Networkanalyst were used to evaluate the data more accurately. Finally, the candidate genes were isolated and their protein binding was measured.

964 high-expression genes and 837 low-expression genes are involved in the progression of opportunistic viral infections with lymphocytes. Cell cycle pathways, oxidative stress, RNA synthesis and TGFB were significantly observed.

The present study showed that important proteins and genes played a major role in enhancing the inflammation of opportunistic viruses such as Epstein-Barr and Cytomegalovirus, among which CDK2, CCNB1, GSK3B, SRC, and SMAD3 showed a more prominent role in this pathway.

The present study was conducted to evaluate the feasibility of telemedicine implementation in two cities in Kerman province with tourist attractions.

This descriptive cross-sectional study was conducted in 2021. Research data were collected using the standard questionnaire "Telemedicine Assessment Tool," designed by the Institute for Medicare (United States). Using the G-power software, the sample size of 68 people was determined. The researchers collected the required data in person at the hospitals under study. The collected data were analyzed using descriptive and analytical statistical methods through SPSS version 26.

According to demographic characteristics, the majority of people were women (78.7%), aged less than 30 years old (37.3%), with less than five years of work experience (40.0%), bachelor’s degree (88.0%), and clinical (57.3%). Regarding P-value, there is a significant difference between telemedicine services in terms of importance. Therefore, telemedicine services, consultation with specialists outside the hospital, patient referrals, and remote drug prescribing had respectively the highest priority. In contrast, changing the processes and structure of the hospital had the lowest priority. The lack of technical staff and the problems of initial costs were the main obstacles to the use of telemedicine, and the attitude of employees and competition were the least essential obstacles.

Due to the tourism situation of the studied cities, hospitals must try to establish telemedicine. In this regard, investment to solve the problems of initial costs and technical staff has priority.

Heart diseases are one of the most common types of diseases, which cause the death of many people. Arrhythmias are an irregular heartbeat that causes the heart to beat abnormally fast (tachycardia) or slow (bradycardia). Therefore, the identification and classification of cardiac arrhythmias using ECG signals is of great importance. This research aimed to provide a data mining-based model to improve the diagnosis of previous arrhythmia.

In this descriptive-analytical study, the UCI reference dataset, which consists of 452 samples with 279 features, was used. The samples were categorized into five classes for the detection and identification of different types of cardiac arrhythmias. The algorithm employed in this research is a combination of hierarchical neural networks (expert system combination).

In all networks, 70% of the samples were used for training, while the remaining 30% were used for testing. After modeling and comparing the generated models and recording the results, the prediction accuracy for cardiac arrhythmia in the absence of combination learning reached 89.5%, and it increased to 93.5% after employing the hierarchical expert combination approach.

The results of this research show that the proposed method based on the combination of neural networks in a hierarchical form, which leads to the specialization of the task of each class, can have better performance compared to similar models in diagnosing cardiac arrhythmia.

According to the World Health Organization, lung diseases are the third cause of death in the world. These diseases are chronic, so early diagnosis of these diseases is very important. Pulmonary function tests are important tools in examining and monitoring patients with respiratory injuries. This research aimed to optimize the K-Nearest Neighbor algorithm, which facilitates and accelerates self-assessment and interpretation of spirometry test results with higher accuracy.

In this study, a method is proposed that improves the limitations of the basic algorithm by optimizing, valuing features, and weighted voting. Using this method, obstructive pulmonary diseases are detected based on the data set of spirometry tests, and general parameters are classified into three categories, namely, asthma, chronic bronchitis, and emphysema.

In determining the appropriate method for calculating the data distance, the Minkowski method was chosen, and by applying the coefficients of the feature values, the accuracy of the classification increased. Weighted voting was done in the final part of the algorithm based on the Gaussian kernel, based on which a constant performance was obtained for changing the parameter of the number of neighbors. The results of the evaluations were carried out in the form of mutual validation. 95.4% accuracy and 93.2% precision were obtained in 3.12 seconds.

The use of machine learning algorithms can be effective in the analysis of medical data. Therefore, in this study, these approaches were used to provide a new method of classification, so that the proposed algorithm could improve the basic method, and also, had better accuracy and performance than other previous methods.

Breast cancer is the most common cancer in women that causes more deaths than other cancers. Thermography is one of the methods of breast cancer diagnosis. The most important challenge in early detection of these images can be human error or lack of access to a skilled person. The use of artificial intelligence methods in image processing can be effective in early detection and reduction of human error. The main aim of this research was to introduce hybrid networks for intelligent diagnosis of breast cancer from thermographic images.

The thermographic images used in this study were collected from the DMR-IR database. First, the main features of the images were extracted by deep convolutional network (CNN). Then, FCNNs and SVM algorithms were used to classify breast cancer from thermographic images.

The accuracy rate for CNN_FC and CNN-SVM algorithms was 94.2% and 0.95%, respectively. In addition, the reliability parameters for these classifiers were calculated as 92.1%, and 97.5%, and the sensitivity for each of these classifiers as 95.5%, and 94.1%, respectively.

The proposed model based on the deep hybrid network has good accuracy compared to similar algorithms; therefore, it can help doctors in the early diagnosis of breast cancer through thermographic images and minimize human error.

In recent years, COVID-19 has been recognized as a health threat. Despite vaccination, people still get the disease because the new variants have mutations in their genomes that allow them to bind to host receptors and evade the immune system’s responses. Therefore, the main aim of this study was to use bioinformatics tools to introduce a rapid and practical vaccine to fight against these diverse mutations in different SARS-CoV-2 strains.

To epitopes mapping, 32 different spike protein variants were retrieved. We then used the Immune Epitope Database (IEDB), NetCTL, and NetMHCIIpan to predict T and B cell epitopes. The vaccine based on protected epitopes was evaluated in terms of antigenicity, allergenicity, toxicity, solubility, physicochemical properties, population coverage, and secondary structure with relevant servers. Modeling using Robetta and docking with Toll-like receptor (TLR3) were performed using Cluspro, PatchDock, and FireDock, respectively.

After detailed evaluations, all the results confirmed the optimal quality of the vaccine. According to further investigations, this structure is similar to native proteins and there is a stable and strong interaction between the vaccine and the receptor. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli.

Based on the obtained results, the designed multi-epitope vaccine can serve as a prophylactic candidate against SARS-CoV-2.

In recent years, the collection of data from the monitoring of clinical trials in the form of electronic case report forms (eCRF) has been increasingly used, because avoiding mistakes, reducing the duration of clinical studies, and preventing increasing data collection costs can affect the success of the study. Accordingly, this research aimed to present the conceptual model and requirements of the proposed eCRF, which is based on NoSQL database to achieve complications caused by the COVID-19 vaccine.

In this study, using the descriptive-applicative method, the conceptual model of the eCRF system was first designed to collect the minimum set of data on the complications of the COVID-19 vaccine. Then, the functional and non-functional requirements of the proposed system were identified using the methods of observation, thinking and reflection, and questionnaire.

This research has two main parts: the proposed conceptual model and the functional and non-functional requirements of the eCRF system. A conceptual model was proposed based on the components, modules, and objectives of the eCRF core, which can be used by researchers and clinical trial specialists. Requirements including the ability to register, insert, delete, and edit the case report form, were also calculated for four groups of patients, health care workers, disease experts, and the executive manager.

The proposed system provides researchers and experts of clinical trials with classified and valid data regarding the registration of complications caused by the COVID-19 vaccine in an integrated manner. Therefore, some eCRF stakeholders such as patients, healthcare workers, disease experts, and executive managers and researchers can achieve a vaccine with acceptable safety and effectiveness by analyzing, interpreting, and sharing the recorded information.

Mobile health (mHealth) is an emerging technology with great potential for improving people's health. Due to its availability, this technology can create more motivation to improve women's health by providing safe, coordinated, and effective health care. Considering the importance of women's health, the present study was conducted to systematically investigate the impact of mHealth interventions on women's health outcomes.

The present study was conducted as a systematic review of clinical trial studies in English. PubMed, Scopus, Science Direct, the Cochrane Library, and ClinicalTrials.gov databases were reviewed according to the inclusion criteria from 2011 to 2021. 

A total of 28 articles were included in the study. mHealth tools, such as applications, text messages, voice or video calls, and wearable devices, were found to have multiple functions in this study, including providing information, reminders, education, and practical recommendations to improve the lifestyles of pregnant women in various areas. The study results indicated that in 82% of the studies, mHealth tools were effective in improving health outcomes for pregnant women, while in 18% of the studies, the intervention results did not differ from the standard method.

mHealth interventions can have a positive effect on improving the health of pregnant women, including their lifestyle, knowledge, and attitude regarding their own and their baby's health. However, in all these studies, high heterogeneity limits the certainty of the evidence for these findings. Therefore, future studies should identify the context in which mHealth can be more effective for women's health.