مجله انفورماتیک سلامت و زیست پزشکی
سال هفتم شماره 4 (پیاپی 26، زمستان 1399)

Hypertension is one of the most common health problems. As it has a major impact on other serious diseases such as cardiovascular diseases and strokes, and due to not having any specific symptoms, it is known as a silent killer. Therefore, proper diagnosis, control, and treatment of hypertension is crucial in health care systems and will indeed prevent the development of the other diseases affected by. Studies have shown a strong connection between some diseases of parents and the probability of having hypertension in children.

In this study, using data collected from two health centers in Ardabil province and applying data mining techniques and Rapid Miner software, the impact of parental diseases on the probability of hypertension in children was investigated.

The results indicated that using paternal medical history on hypertension, premature coronary artery disease, hypercholesterolemia, and hyperthyroidism has a significant role in predicting hypertension in children. Moreover, the performance of the predictive model, developed using the collected samples and data mining techniques, was enhanced by 5%.

Some diseases of parents, especially hypertension, increase the risk of hypertension in children. However, individual characteristics are the major determinants of this complication.

Glaucoma is the most common cause of blindness in some countries. In the meantime, the field of retinal image processing has been proposed in order to provide automatic systems for disease diagnosis. Among the methods of medical image processing, image segmentation is a process of identification and change in the display of an image. The objective of this study was to use the segmentation method and compare it with previous algorithms so as to be able to diagnose retinal optic disc more accurately.

In the present analytical study, using the image segmentation method, each pixel was assigned a label in such a way that pixels with the same label had similar characteristics. The optic disc segmentation was performed. Using MATLAB software, retinal images of patients with glaucoma were entered into the program and an ideal output was obtained.

The quantitative analysis of the obtained results showed a high accuracy (85%) for the proposed method for the segmentation of the retinal optic disc; thus, the results can be used to efficiently diagnose a person with glaucoma.

The purpose of segmenting an image is to make raw data more usable for subsequent statistical processing. It is expected that in the future, feature extraction be more accurate, and more details be available to machine vision systems to identify objects in the images.

The prediction of microRNAs related to target genes using bioinformatics tools saves time and costs of the experimental analyses. In the present study, the prediction of microRNA target genes relevant to Alzheimer’s Diseases (AD) were compared with the experimentally reported data using different bioinformatics tools.

A total of 41 microRNAs associated with 21 essential genes involved in AD were selected based on experimental results reported in previously published literature. Then, the prediction of the target gene for each microRNA was done using three bioinformatics tools, including MirTarget, TargentScan, and Diana-microT. The results of the predictions for all three tools considering the reported target genes were compared with each other. 

The results showed that MirTarget, TargetScan, and Diana-microT correctly predicted 66%, 61%, and 27% of microRNAs’ attachment to the previously reported target genes involved in AD, respectively. However, none of the tools could predict the attachment of 22% of the microRNAs to the target genes reported in the literature.

It can be concluded that MirTarget and TargetScan can better predict the target gene for microRNAs involved in AD compared with Diana-microT. Considering the algorithm used in MirTarget, this bioinformatics tool provides more functional and accurate results in predicting the target genes for microRNAs and it is recommended for predicting the target genes of microRNAs. It can also be concluded that the reported target genes for microRNAs involved in AD need further investigations in some cases.

Schizophrenia is a chronic and disabling mental disorder and medication non-adherence remains a significant challenge for the patients and psychiatrists. The present study aimed to assess the impact of short-message service (SMS) on medication adherence in schizophrenic patients as compared with the control group.

A total of 63 patients with schizophrenia were assigned to the intervention group (N= 32) and the control group (N= 31). The patients in the intervention group received a daily SMS that read, ‘‘Please remember to take your medication” at 8:00am and 8:00pm. Medication adherence was assessed by the self-report Medication Adherence Rating Scale (MARS) at baseline and in the 3rd month, and the 6th month.

Patients receiving SMS had a significantly greater improvement in medication adherence compared with the control group in the 3rd month, but there was no significant difference in the 6th month.

The results of the present study showed that text message reminder for medication intake is an effective and feasible strategy for enhancing medication adherence in patients with schizophrenia.

The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. Ample experimental data confirm the existence of random behaviors in the interactions between genes and proteins in gene regulatory networks. Genetic factors, regulatory dynamics at the microscopic level, transcription rates of genes, and many other factors that depend on variable environmental conditions cause random behaviors in the cell cycle network.

The aim of this study was to present a stochastic Markov model to simulate interactions between proteins in a complex network of fission yeast cell cycle and to predict the dynamics of proteins. We used local sensitivity analysis to investigate the relationship between the weight of protein / gene interactions with the probabilities of phase transition in the cell cycle.

Using this model, the probability of transition between different phases of the cell cycle in the presence of different levels of noise was investigated and it was proved that the cell cycle path has the highest probability among all possible pathways for the cell. By performing sensitivity analysis, the correlation between the weight of interactions between proteins and the probability of transition between different phases of the cell cycle was calculated.

Our local sensitivity analysis revealed that how perturbation on parameters affect the transition probabilities between subsequent cell cycle phases, so it suggests testable hypotheses in the experimental environments. Also, the model of this study proves the stability of the cell cycle in the presence of moderate levels of noise.

One of the most important and challenging areas under the influence of information technology is the field of health. This pervasive influence has led to the development of electronic health (e-health) networks with a variety of services of different qualities. The issue of security management, maintaining confidentiality and data integrity, and exchanging it in a secure environment between trusted parties is a challenge for e-health networks. Reviewing previous studies revealed that providing a comprehensive and efficient model for authentication and secure exchange of information with a distributed approach while avoiding the single point of failure, which can meet the various needs of the e-health network, was necessary to fill the gap. In this study, it was attempted to introduce a secure authentication model for the e-health network by overcoming the limitations of previous articles on authentication in the e-health network and using the benefits of authentication models in various fields.

In this study, a secure and distributed authentication model was designed for the health network with a combination of blockchain and public-key infrastructure and its usability was presented in the form of an applicable instance (electronic prescribing).

This model showed that combining public-key infrastructure with blockchain can provide a secure, two-way, scalable, and distributed authentication with avoiding a single point of failure for the e-Health network.

It can be concluded that in order to meet the security requirements of the e-health network, it is necessary to use peer-to-peer authentication models which are not dependent on a central server and the proposed model indicated that combining public-key infrastructure with blockchain can bring benefits and fulfill security requirements.

Using mobile phones is one of the ways to increase peoplechr('39')s knowledge and awareness. The objective of this study was to design and develop a mobile-based application for “identification, prevention, and control of head lice” and also to assess the satisfaction of mobile users with this educational application.

This descriptive cross-sectional study was conducted in two stages including design and development of an educational application for head lice and assessment of the level of satisfaction among 140 users using electronic questionnaires. Finally, the data were analyzed by SPSS software (version 24) using descriptive statistics indices.

The descriptive findings of this study showed that 61.4% of users participating in the study were women, 45.8% of them were in the age group of 20 to 30 years, and 42.1% had high school diploma. Moreover, more than 95% of users stated that the scientific content in the application was easy to understand and the data were up-to-date. Overall, the results revealed that 81% of users in this study preferred to use this educational application over reading texts when exposed to head lice infestation and the effect of using the educational information of this mobile-based application was reported to be at high level (61%).

The target population including families involved with head lice, school teachers, health center experts, and medical students are recommended to use this educational application.

The precision and time required for analysis of data in next-generation sequencing (NGS) depends on many factors including the tools utilized for alignment, variant calling, annotation and filtering of variants, personnel expertise in data analysis and interpretation, and computational capacity of the lab and its optimization is a challenging task. 

An application software was designed and implemented in C# for optimizing the third step of NGS data analysis. In this study, annotation, filtering, and interpretation of NGS data were specifically optimized for non-syndromic autosomal recessive hearing loss disease.

Whole-exome sequencing data of a patient with a pathogenic mutation confirmed by familial genetic analysis, which contained a total number of 671829 variants after primary analysis, were evaluated by the implemented software. After filtering the variants based on a predefined BED file, 508 variants remained. According to the patient’s pedigree, in the next step of analysis, homozygote variants were selected and only 187 variants remained. After applying the population frequency threshold of 0.6% on gnomeAD and ExAC databases, the number of variants reached 110 and 3, respectively. The identified pathogen was approved by the results of Sanger sequencing done for family co-segregation. This analysis took about 15 minutes on a moderate PC.

The designed software is a fully graphical one that has the capability of comparing, viewing, filtering, and merging input files without any coding. Moreover, it can construct a local database from the analyzed files and apply region constraints and user-defined thresholds on various fields of the database.

Today, the use of picture archiving and communication system (PACS) in health care centers is increasing. Meanwhile, the security of data of patients with COVID-19 in this system and control of access to such data is considerably significant. Therefore, this study aimed to evaluate the security of medical picture archiving and communication systems for recording data of COVID-19 patients in health care centers affiliated to Kerman University of Medical Sciences.

The present study was a cross-sectional one conducted in 2020. The research population included information technology staff in health care centers affiliated to Kerman University of Medical Sciences. Data were collected using IHS HIPAA Security Checklist for three types of PACS systems in 12 health care centers of Kerman University of Medical Sciences. Data were analyzed via SPSS software version 24 using descriptive statistics and linear regression to measure the relationship between administrative, physical, and technical dimensions of the system.

In this study, 50% of the participants were female and 58% of them had more than 5 years of work experience. The results of the present study showed that the most significant challenge in the security of PACS systems was in the technical dimension and the least significant was in the physical dimension. Moreover, there was a significant relationship between administrative and physical dimensions (P <0.05).

The security status of PACS has weaknesses and it seems necessary to plan so as to develop the necessary laws regarding the security of access to data of patients with COVID-19 and its observance by system support companies, user training, access control, and risk management.

The prevalence of gastrointestinal cancers, especially gastric and colorectal cancers, is increasing. Gastric cancer is the most common gastrointestinal cancer in Iran and the age of esophageal and colon cancers onset has decreased in Iran. Therefore, it is important to study the factors and proteins involved in these cancers. Use of computational tools is an effective way to study protein networks and identify the important proteins involved in these diseases.

At first identified proteins from esophageal, gastric and colon cancers were extracted from relevant databases and articles; then, the Protein-Protein Interaction (PPI) networks were mapped and analyzed for cross-linking and determination of key proteins using servers and software such as STRING 11.0, Cytoscape 3.7.3, NDEx 2.4.3, and UniProt. The CentiScaPe 2.2 plugin was used by examining the centrality indices (Betweenness, Closeness, Degree) and the CytoHubba plugin was used by different centrality indices for determination of the key nodes and the central common proteins. Finally, after investigation of the expression profiles of proteins with high centrality indices using GTEx v8, Expression Atlas, and GEPIA2, the final common proteins were determined in all three cancers.

The results showed that PLK1, CCNB1, CCNA2, BUB1B, and CDK1 proteins are the central common proteins in all three tissues. These proteins are highly correlated (R => 0.8) with each other and have significant expression differences in normal and cancerous tissues.

PLK1, CCNB1, CCNA2, BUB1B, and CDK1 proteins can be used as diagnostic and therapeutic markers for esophageal, gastric, and colon cancers. Further studies on these proteins will have a bright future for the treatment of various types of gastrointestinal cancer.

Lack of proper linkage between a patient’s medications and the results of laboratory tests can lead to common medication errors called drug-laboratory interactions (DLIs). DLIs are among the major types of preventable medication errors in the treatment process. Application of a clinical decision support system (CDSS) for physicians and other health care providers to decrease DLIs can effectively improve the treatment process and care quality helping to prevent potential adverse drug events. Design and implementation of these systems requires measurements to tackle a number of challenges. The present article briefly reviews these challenges and provides solutions to increase their effectiveness. With informed design and implementation of DLI-CDSSs, it is hoped that these interactions will be decreased and the quality of prescriptions will be improved with regard to patients’ laboratory test results.