Functional and Nonfunctional Requirements of Virtual Clinic Mobile Applications: A Systematic Review.

Introduction: The Virtual Clinic Mobile Application (VCMA) is a valuable tool for managing and remotely monitoring patients with various medical conditions. It can alleviate the strain on outpatient services and offer follow-up options for patients who do not require a physical examination. A thorough understanding of recent literature can assist in identifying suitable functionalities for new development and future improvement of current applications (apps). This review study is aimed at identifying functional and nonfunctional requirements for VCMA. Methods: This study conducted a systematic search using databases such as PubMed, Scopus, ISI Web of Science, Science Direct, ProQuest, and IEEE to gather requirements of VCMA articles published in English from the inception of the databases up to April 2022. Out of a total of 1223 articles, 76 met the inclusion criteria. These articles were then analyzed using conventional content analysis to extract and categorize their requirements. Results: Two main themes and 8 subthemes in terms of VCMA requirements were extracted as follows: (1) functional requirements with 3 subthemes (demographic data documentation, health record, general features of the user interface (UI)); (2) nonfunctional requirements with 5 subthemes (usability, accessibility, compatibility, efficiency, and security). Conclusion: The findings highlight the importance of mHealth solutions for virtual care and the need for the development of apps based on the extracted functional and nonfunctional requirements for VCMA; however, controlled trials are necessary. It is recommended that transparent reporting of mHealth interventions be prioritized to enable effective interpretation of the extracted data.

In silico identification and in vitro evaluation of MRPS30-DT lncRNA and MRPS30 gene expression in breast cancer.

BACKGROUND: It has been reported that long non-coding RNAs (lncRNAs) can play important roles in a variety of biological processes and cancer regulatory networks, including breast cancer. AIMS: This study aimed to identify a novel upregulated lncRNA in breast cancer and its associated gene using bioinformatics analysis, and then evaluate their potential roles in breast cancer. METHODS AND RESULTS: Extensive in silico studies were performed using various bioinformatics databases and tools to identify a potential upregulated breast cancer-associated lncRNA and its co-expressed gene, and to predict their potential roles, functions, and interactions. The expression level of MRPS30-DT lncRNA and MRPS30 was assessed in both BC tissues and cell lines using qRT-PCR technology. MRPS30-DT lncRNA and MRPS30 were selected as target genes using bioinformatics analysis. We found that MRPS30-DT and MRPS30 were significantly overexpressed in BC tissues compared with normal tissues. Also, MRPS30 showed upregulation in all three BC cell lines compared with HDF. On the other hand, MRPS30-DT significantly increased in MDA-MB-231 compared with HDF. While the expression of MRPS30-DT was significantly dropped in the resistance cell line MCF/MX compared to HDF and MCF7. Moreover, bioinformatics analysis suggested that MRPS30-DT and MRPS30 may play a potential role in BC through their involvement in some cancer signaling pathways and processes, as well as through their interaction with TFs, genes, miRNAs, and proteins related to carcinogenesis. CONCLUSIONS: Overall, our findings showed the dysregulation of MRPS30-DT lncRNA and MRPS30 may provide clues for exploring new therapeutic targets or molecular biomarkers in BC.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Effect of Egg-White Protein Alone or Combined With Niacin on Nutritional Status, and Phosphorus Control in Maintenance Hemodialysis Patients: A Randomized Controlled Trial.

OBJECTIVE: Niacin is reported to decrease phosphorus concentration in maintenance hemodialysis (MHD) patients. Egg white is one of the main substitutable proteins in MHD patients due to its low phosphorus content. Therefore, we aimed to evaluate the effects of combined egg white and niacin supplementation on dialysis patients' serum phosphorus and nutritional biomarkers. DESIGN AND METHODS: In this randomized controlled clinical trial, 98 patients on MHD were randomly allocated to four groups for 8 weeks: 24 g egg white (n = 25), 600 g niacin daily (n = 24), egg white combined with niacin (n = 24), and control (n = 24). Calcium, phosphorus, fibroblast growth factor-23, and other nutritional markers were assessed. RESULTS: There was a significant difference among the groups only in phosphorus at the end of the trial, which was significantly lower in the niacin group (4.38 + 0.812 mg/dL) than in both the egg white (5.07 + 0.49 mg/dL) and egg white with niacin supplementation (5.41 + 0.662 mg/dL) groups. In this regard, albumin increased in egg white and egg white with niacin supplementation, while albumin did not change significantly in the niacin group. Urea reduction ratio and Kt/V rose only in the egg-white group, while aspartate aminotransferase increased only in the niacin and control groups. CONCLUSION: Niacin decreases serum phosphorus concentration more than egg-white protein or a combined intervention. Egg white protein supplementation has beneficial effects on some nutritional statuses other than phosphorus control without the side effects of niacin.

Computational design of a multi-epitope vaccine candidate against Langya henipavirus using surface proteins.

In July 2022, Langya henipavirus (LayV) was identified in febrile patients in China. There is currently no approved vaccine against this virus. Therefore, this research aimed to design a multi-epitope vaccine against LayV using reverse vaccinology. The best epitopes were selected from LayV's fusion protein (F) and glycoprotein (G), and a multi-epitope vaccine was designed using these epitopes, adjuvant, and appropriate linkers. The physicochemical properties, antigenicity, allergenicity, toxicity, and solubility of the vaccine were evaluated. The vaccine's secondary and 3D structures were predicted, and molecular docking and molecular dynamics (MD) simulations were used to assess the vaccine's interaction and stability with toll-like receptor 4 (TLR4). Immune simulation, codon optimization, and in silico cloning of the vaccine were also performed. The vaccine candidate showed good physicochemical properties, as well as being antigenic, non-allergenic, and non-toxic, with acceptable solubility. Molecular docking and MD simulation revealed that the vaccine and TLR4 have stable interactions. Furthermore, immunological simulation of the vaccine indicated its ability to elicit immune responses against LayV. The vaccine's increased expression was also ensured using codon optimization. This study's findings were encouraging, but in vitro and in vivo tests are needed to confirm the vaccine's protective effect.Communicated by Ramaswamy H. Sarma.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

In silico design and immunoinformatics analysis of a universal multi-epitope vaccine against monkeypox virus.

Monkeypox virus (MPXV) outbreaks have been reported in various countries worldwide; however, there is no specific vaccine against MPXV. In this study, therefore, we employed computational approaches to design a multi-epitope vaccine against MPXV. Initially, cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), linear B lymphocytes (LBL) epitopes were predicted from the cell surface-binding protein and envelope protein A28 homolog, both of which play essential roles in MPXV pathogenesis. All of the predicted epitopes were evaluated using key parameters. A total of 7 CTL, 4 HTL, and 5 LBL epitopes were chosen and combined with appropriate linkers and adjuvant to construct a multi-epitope vaccine. The CTL and HTL epitopes of the vaccine construct cover 95.57% of the worldwide population. The designed vaccine construct was found to be highly antigenic, non-allergenic, soluble, and to have acceptable physicochemical properties. The 3D structure of the vaccine and its potential interaction with Toll-Like receptor-4 (TLR4) were predicted. Molecular dynamics (MD) simulation confirmed the vaccine's high stability in complex with TLR4. Finally, codon adaptation and in silico cloning confirmed the high expression rate of the vaccine constructs in strain K12 of Escherichia coli (E. coli). These findings are very encouraging; however, in vitro and animal studies are needed to ensure the potency and safety of this vaccine candidate.

Designing a novel multi­epitope vaccine against Ebola virus using reverse vaccinology approach.

Ebola virus (EBOV) is a dangerous zoonotic infectious disease. To date, more than 25 EBOV outbreaks have been documented, the majority of which have occurred in Central Africa. The rVSVG-ZEBOV-GP vaccine (ERVEBO), a live attenuated vaccine, has been approved by the US Food and Drug Administration (FDA) to combat EBOV. Because of the several drawbacks of live attenuated vaccines, multi-epitope vaccines probably appear to be safer than live attenuated vaccines. In this work, we employed immunoinformatics tools to design a multi-epitope vaccine against EBOV. We collected sequences of VP35, VP24, VP30, VP40, GP, and NP proteins from the NCBI database. T-cell and linear B-cell epitopes from target proteins were identified and tested for antigenicity, toxicity, allergenicity, and conservancy. The selected epitopes were then linked together in the vaccine's primary structure using appropriate linkers, and the 50S ribosomal L7/L12 (Locus RL7 MYCTU) sequence was added as an adjuvant to the vaccine construct's N-terminal. The physicochemical, antigenicity, and allergenicity parameters of the vaccine were all found to be satisfactory. The 3D model of the vaccine was predicted, refined, and validated. The vaccine construct had a stable and strong interaction with toll-like receptor 4 (TLR4) based on molecular docking and molecular dynamic simulation (MD) analysis. The results of codon optimization and in silico cloning revealed that the proposed vaccine was highly expressed in Escherichia coli (E. coli). The findings of this study are promising; however, experimental validations should be carried out to confirm these findings.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study.

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method. In this regard, structural proteins from SARS-COV-2, including the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, were selected as target antigens for epitope prediction. A total of five helper T lymphocytes (HTL) and five cytotoxic T lymphocytes (CTL) epitopes were selected after screening the predicted epitopes for antigenicity, allergenicity, and toxicity. Subsequently, the selected HTL and CTL epitopes were fused via flexible linkers. Next, the cholera toxin B-subunit (CTxB) as an adjuvant was linked to the N-terminal of the chimeric structure. The proposed vaccine was analyzed for the properties of physicochemical, antigenicity, and allergenicity. The 3D model of the vaccine construct was predicted and docked with the Toll-like receptor 4 (TLR4). The molecular dynamics (MD) simulation was performed to evaluate the stable interactions between the vaccine construct and TLR4. The immune simulation was also conducted to explore the immune responses induced by the vaccine. Finally, in silico cloning of the vaccine construct into the pET-28 (+) vector was conducted. The results obtained from all bioinformatics analysis stages were satisfactory; however, in vitro and in vivo tests are essential to validate these results.

Plasma Levels of miR-27a, miR-130b, and miR-301a in Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a gynecological endocrine disorder in women of reproductive age. There is adequate evidence that suggests several microRNAs (miRNAs) are of great importance for PCOS. It seems that dysregulated expression of miR-27a, miR-130b, and miR-301a are associated with PCOS. The aim of this study was to investigate whether plasma levels of these miRNAs are different between patients with PCOS and healthy controls. Fifty-three women with a definite diagnosis of PCOS, and 53 healthy controls were enrolled. MiRNAs expression levels in plasma were evaluated by real-time PCR. The diagnostic values of each miRNA were calculated by the receiver operating characteristic (ROC) curve and areas under the curves (AUC). The main clinical characteristics were not significantly different between the two groups. The circulating plasma expression levels of miR-27a and miR-301a had a significant increase (P = 0.0008 and P <0.0001, respectively) but miR-130b expression level decreased in the patient group (P <0.0001). The AUC for miR-27a, miR-130b, and miR-301a were 0.71, 0.77, and 0.66, respectively. A positive exponential was observed for miR-27a and miR-301a in multiple logistic regression. Changes in the plasma expressions of the studied miRNAs are likely to be associated with PCOS phenotypes. MiR-27a has a potential to serve as a diagnostic biomarker of PCOS.

Management of the essential data element in the differential diagnosis of oral medicine: An effective step in promoting oral health.

BACKGROUND: Oral soft tissue diseases include a broad spectrum, and the wide array of patient data elements need to be processed in their diagnosis. One of the biggest and most basic challenges is the analysis of this huge amount of complex patient data in an increasing number of complicated clinical decisions. This study seeks to identify the necessary steps for collecting and management of these data elements through establishing a consensus-based framework. METHODS: This research was conducted as a descriptive, cross-sectional study from April 2016 to January 2017, which has been performed in several steps: literature review, developing the initial draft (v. 0), submitting the draft to experts, validating by an expert panel, applying expert opinions and creating version v.i, performing Delphi rounds, and creating the final framework. RESULTS: The administrative data category with 17 and the historical data category with 23 data elements were utilized in recording data elements in the diagnosis of all of the different oral diseases. In the paraclinical indicator and clinical indicator categories, the necessary data elements were considered with respect to the 6 main axes of oral soft tissue diseases, according to Burket's Oral Medicine: ulcerative, vesicular, and bullous lesions; red and white lesions of the oral mucosa; pigmented lesions of the oral mucosa; benign lesions of the oral cavity and the jaws; oral and oropharyngeal cancer; and salivary gland diseases. CONCLUSIONS: The study achieved a consensus-based framework for the essential data element in the differential diagnosis of oral medicine using a comprehensive search with rich keywords in databases and reference texts, providing an environment for discussion and exchange of ideas among experts and the careful use of the Delphi decision technique.

Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach.

OBJECTIVE: Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases. MATERIALS AND METHODS: First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire. RESULTS: The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence. CONCLUSIONS: System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other.

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients' medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD.

Designing and Implementation of Fuzzy Case-based Reasoning System on Android Platform Using Electronic Discharge Summary of Patients with Chronic Kidney Diseases.

INTRODUCTION: Case-based reasoning (CBR) systems are one of the effective methods to find the nearest solution to the current problems. These systems are used in various spheres as well as industry, business, and economy. The medical field is not an exception in this regard, and these systems are nowadays used in the various aspects of diagnosis and treatment. METHODOLOGY: In this study, the effective parameters were first extracted from the structured discharge summary prepared for patients with chronic kidney diseases based on data mining method. Then, through holding a meeting with experts in nephrology and using data mining methods, the weights of the parameters were extracted. Finally, fuzzy system has been employed in order to compare the similarities of current case and previous cases, and the system was implemented on the Android platform. DISCUSSION: The data on electronic discharge records of patients with chronic kidney diseases were entered into the system. The measure of similarity was assessed using the algorithm provided in the system, and then compared with other known methods in CBR systems. CONCLUSION: Developing Clinical fuzzy CBR system used in Knowledge management framework for registering specific therapeutic methods, Knowledge sharing environment for experts in a specific domain and Powerful tools at the point of care.

Semantic-Web Architecture for Electronic Discharge Summary Based on OWL 2.0 Standard.

INTRODUCTION: Patients' electronic medical record contains all information related to treatment processes during hospitalization. One of the most important documents in this record is the record summary. In this document, summary of the whole treatment process is presented which is used for subsequent treatments and other issues pertaining to the treatment. Using suitable architecture for this document, apart from the aforementioned points we can use it in other fields such as data mining or decision making based on the cases. MATERIAL AND METHODS: In this study, at first, a model for patient's medical record summary has been suggested using semantic web-based architecture. Then, based on service-oriented architecture and using Java programming language, a software solution was designed and run in a way to generate medical record summary with this structure and at the end, new uses of this structure was explained. RESULTS: in this study a structure for medical record summaries along with corrective points within semantic web has been offered and a software running within Java along with special ontologies are provided. DISCUSSION AND CONCLUSION: After discussing the project with the experts of medical/health data management and medical informatics as well as clinical experts, it became clear that suggested design for medical record summary apart from covering many issues currently faced in the medical records has also many advantages including its uses in research projects, decision making based on the cases etc.

Designing and Implementation of Retina Image Drawing System and Automatic Report Generation from Retina Examinations.

INTRODUCTION: Electronic medical records as one of major parts of electronic health records is an important application of Medical Informatics. EMR includes different types of data, Graphical items being one of these data types. To this end, a standard structure for storing and recovering and finally exchanging this data type is required. In order to standardize information items in this research, UMLS standard is used. In this research, graphical information from fondues designing in retina surgery forms is used for the task of implementation. IMPLEMENTATION: Three-layer software architecture is used for implementation of this system, which includes user interface, data base access and business logic. XML database is used for storing and exchanging of data. User interface is designed by the means of Adobe Flash. Also in the user interface for eye examinations, appropriate icons compatible with current pathologies in retina examinations are considered and UMLS codes are used for standardizations purposes. RESULTS: As this project is independently implemented in Adobe Flash, it can be run in most of electronic patient records software. For evaluation purposes of this research, an EMR system for eye clinics is used. Tree structure is used for data entry and finally a text report based on the entered data will be generated. By storing graphical items in this software editing and searching in medical concepts and also comparing features will be available. CONCLUSION: One of the data items that we encounter in various medical records is graphical data. In order to cover the patient's complete electronic medical records, the Electronic Implementation of this information is important. For this purpose, graphical items in retina surgery forms were used and finally a software application for drawing retina picture was developed. Also, XML files were used for the purpose of storing valuable medical data from the pictures, and also UMLS were applied for the standardization purpose. The developed software is currently being used in some of eye clinics in Iran.

The Usage of Association Rule Mining to Identify Influencing Factors on Deafness After Birth.

BACKGROUND: Providing complete and high quality health care services has very important role to enable people to understand the factors related to personal and social health and to make decision regarding choice of suitable healthy behaviors in order to achieve healthy life. For this reason, demographic and clinical data of person are collecting, this huge volume of data can be known as a valuable resource for analyzing, exploring and discovering valuable information and communication. This study using forum rules techniques in the data mining has tried to identify the affecting factors on hearing loss after birth in Iran. MATERIALS AND METHODS: The survey is kind of data oriented study. The population of the study is contained questionnaires in several provinces of the country. First, all data of questionnaire was implemented in the form of information table in Software SQL Server and followed by Data Entry using written software of C # .Net, then algorithm Association in SQL Server Data Tools software and Clementine software was implemented to determine the rules and hidden patterns in the gathered data. FINDINGS: Two factors of number of deaf brothers and the degree of consanguinity of the parents have a significant impact on severity of deafness of individuals. Also, when the severity of hearing loss is greater than or equal to moderately severe hearing loss, people use hearing aids and Men are also less interested in the use of hearing aids. CONCLUSION: In fact, it can be said that in families with consanguineous marriage of parents that are from first degree (girl/boy cousins) and 2(nd) degree relatives (girl/boy cousins) and especially from first degree, the number of people with severe hearing loss or deafness are more and in the use of hearing aids, gender of the patient is more important than the severity of the hearing loss.