COVID-19 vaccine registry for pregnant women: policy to control complications of vaccination in pregnant women in 2021-2022.

BACKGROUND: Data management related to COVID-19 vaccination in pregnant women is vital to improve the treatment process and to establish preventive programs. Implementing a registry to manage data is an essential part of this process. This study aims to design a national model of the COVID-19 vaccination registry for pregnant women in Iran. METHODS: The present study is an applied descriptive study conducted in 2021 and 2022 in two stages. In the first stage, the coordinates of the National Registry of COVID-19 vaccination of pregnant women from related references and articles, as well as the comparative study of the National Registry of COVID-19 vaccination of pregnant women in the United States, Canada, and the United Kingdom was done. In the second stage, the preliminary model was designed. The model was validated using the Delphi technique and questionnaire tools and analyzing the data. RESULTS: The presented national COVID-19 vaccination registry model of pregnant women's main components consist of objectives, data sources, structure, minimum data set, standards, and registry processes, all of which received 100% expert consensus. CONCLUSION: The vaccination registry of pregnant women has a major role in managing COVID-19 vaccination data of pregnant women and can be one of the Ministry of Health and Medical Education priorities.

Machine learning applications for early detection of esophageal cancer: a systematic review.

INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. METHODS: We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review. RESULTS: The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. CONCLUSION: Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Integrated personal health record (PHR) security: requirements and mechanisms.

BACKGROUND: Personal Health Records (PHRs) are designed to fulfill the goals of electronic health (eHealth) and empower the individual in the process of self-care. Integrated PHR can improve the quality of care, strengthen the patient-healthcare provider relationship, and reduce healthcare costs. Still, the process of PHR acceptance and use has been slow and mainly hindered by people's concerns about the security of their personal health information. Thus, the present study aimed to identify the Integrated PHR security requirements and mechanisms. METHODS: In this applied study, PHR security requirements were identified with a literature review of (library sources, research articles, scientific documents, and reliable websites). The identified requirements were classified, and a questionnaire was developed accordingly. Thirty experts completed the questionnaire in a two-round Delphi technique, and the data were analyzed by descriptive statistics. RESULTS: The PHR security requirements were identified and classified into seven dimensions confidentiality, availability, integrity, authentication, authorization, non-repudiation, and right of access, each dimension having certain mechanisms. On average, the experts reached an agreement about the mechanisms of confidentiality (94.67%), availability (96.67%), integrity (93.33%), authentication (100%), authorization (97.78%), non-repudiation (100%), and right of access (90%). CONCLUSION: Integrated PHR security is a requirement for its acceptance and use. To design a useful and reliable integrated PHR, system designers, health policymakers, and healthcare organizations must identify and apply security requirements to guarantee the privacy and confidentiality of data.

Development and usability evaluation of a mHealth application for albinism self-management.

BACKGROUND: Reduced or absence of melanin poses physical, social, and psychological challenges to individuals with albinism. Mobile health (mHealth) applications have the potential to improve the accessibility of information and services while reducing time and costs. This study aimed to develop and evaluate a mHealth application for self-management of albinism. METHODS: This applied study was conducted in two stages (development and evaluation) in 2022. Initially, the functional requirements were determined, and the conceptual model of the application was then developed using Microsoft Visio 2021. In the second phase, the application was evaluated using the Mobile Application Usability Questionnaire (MAUQ) involving patients with albinism to reflect their views on the usability of the application. RESULTS: The key capabilities of the application included: reminders, alerts, educational content, useful links, storage and exchange of images of skin lesions, specialist finder, and notifications for albinism-relevant events. Twenty-one users with albinism participated in the usability testing of the application. The users were predominantly satisfied with the application (5.53 ± 1.10; Max: 7.00). CONCLUSIONS: The findings of this study suggest that the developed mobile application could assist individuals with albinism to effectively manage their condition by considering the users' requirements and services that the application should deliver.

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.

Background: Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up. Materials and methods: This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype. Results: The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals. Conclusion: This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

Predicting the mortality of patients with Covid-19: A machine learning approach.

Background and Aims: Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease. Methods: The data of this study were collected from hospitalized patients with COVID-19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results: GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion: Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

Cervical cancer survival prediction by machine learning algorithms: a systematic review.

BACKGROUND: Cervical cancer is a common malignant tumor of the female reproductive system and is considered a leading cause of mortality in women worldwide. The analysis of time to event, which is crucial for any clinical research, can be well done with the method of survival prediction. This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer. METHOD: An electronic search of the PubMed, Scopus, and Web of Science databases was performed on October 1, 2022. All articles extracted from the databases were collected in an Excel file and duplicate articles were removed. The articles were screened twice based on the title and the abstract and checked again with the inclusion and exclusion criteria. The main inclusion criterion was machine learning algorithms for predicting cervical cancer survival. The information extracted from the articles included authors, publication year, dataset details, survival type, evaluation criteria, machine learning models, and the algorithm execution method. RESULTS: A total of 13 articles were included in this study, most of which were published from 2018 onwards. The most common machine learning models were random forest (6 articles, 46%), logistic regression (4 articles, 30%), support vector machines (3 articles, 23%), ensemble and hybrid learning (3 articles, 23%), and Deep Learning (3 articles, 23%). The number of sample datasets in the study varied between 85 and 14946 patients, and the models were internally validated except for two articles. The area under the curve (AUC) range for overall survival (0.40 to 0.99), disease-free survival (0.56 to 0.88), and progression-free survival (0.67 to 0.81), respectively from (lowest to highest) received. Finally, 15 variables with an effective role in predicting cervical cancer survival were identified. CONCLUSION: Combining heterogeneous multidimensional data with machine learning techniques can play a very influential role in predicting cervical cancer survival. Despite the benefits of machine learning, the problem of interpretability, explainability, and imbalanced datasets is still one of the biggest challenges. Providing machine learning algorithms for survival prediction as a standard requires further studies.

A Mobile Self-Management App (CanSelfMan) for Children With Cancer and Their Caregivers: Usability and Compatibility Study.

BACKGROUND: Despite the increasing development of different smartphone apps in the health care domain, most of these apps lack proper evaluation. In fact, with the rapid development of smartphones and wireless communication infrastructure, many health care systems around the world are using these apps to provide health services for people without sufficient scientific efforts to design, develop, and evaluate them. OBJECTIVE: The objective of this study was to evaluate the usability of CanSelfMan, a self-management app that provides access to reliable information to improve communication between health care providers and children with cancer and their parents/caregivers, facilitating remote monitoring and promoting medication adherence. METHODS: We performed debugging and compatibility tests in a simulated environment to identify possible errors. Then, at the end of the 3-week period of using the app, children with cancer and their parents/caregivers filled out the User Experience Questionnaire (UEQ) to evaluate the usability of the CanSelfMan app and their level of user satisfaction. RESULTS: During the 3 weeks of CanSelfMan use, 270 cases of symptom evaluation and 194 questions were recorded in the system by children and their parents/caregivers and answered by oncologists. After the end of the 3 weeks, 44 users completed the standard UEQ user experience questionnaire. According to the children's evaluations, attractiveness (mean 1.956, SD 0.547) and efficiency (mean 1.934, SD 0.499) achieved the best mean results compared with novelty (mean 1.711, SD 0.481). Parents/caregivers rated efficiency at a mean of 1.880 (SD 0.316) and attractiveness at a mean of 1.853 (SD 0.331). The lowest mean score was reported for novelty (mean 1.670, SD 0.225). CONCLUSIONS: In this study, we describe the evaluation process of a self-management system to support children with cancer and their families. Based on the feedback and scores obtained from the usability evaluation, it seems that the children and their parents find CanSelfMan to be an interesting and practical idea to provide reliable and updated information on cancer and help them manage the complications of this disease.

Design and Development of a Hospital-Based Coronary Artery Disease (CAD) Registry in Iran.

Background: The incidence of coronary artery disease (CAD), the leading cause of mortality in most developed and developing countries, is increasing. The adoption of hospital registries can improve care delivery and facilitate the management of CAD through better planning, as well as help with outcome assessment through more effective data management. Objectives: The present study is aimed at designing a hospital-based CAD registry for managing CAD data. Methods: This developmental study was conducted in three phases. Initially, sources related to CAD registries were reviewed, the results of which were published in two studies. In the next phase, the prerequisites and requisites of the software were determined through a qualitative study. In this phase, the registry dataset was determined by using a questionnaire. Finally, the developed conceptual model of the software was validated. The software was then developed based on the validated conceptual model. Results: The registry data elements were classified into 13 main categories, including identification data, medical history, and risk factors. The dataset included 171 data elements, including data related to surgical and nonsurgical procedures. The conceptual model was approved by field experts, and the software was developed accordingly. Conclusion: The steps followed in the present study for developing the CAD registry can be used as an appropriate approach for designing similar hospital-based registries. Considering the pivotal role of the registry in the management of CAD, the routine and systemic use of the registry is suggested in all healthcare centers.

Challenges of implementing diagnostic-related groups and healthcare promotion in Iran: A strategic applied research.

Background and Aim: Implementing the diagnostic-related groups (DRGs) promotes the efficiency of healthcare. Therefore, the present study aimed to identify the challenges facing implementing the DRGs in Iran. Methods: The present study is a strategic applied research conducted in two phases. In the first phase, the challenges facing DRGs were extracted through a literature review. Then the collected data is entered into a checklist consisting of five sections including technological, cultural, organizational, strategic, and natural challenges. In the second phase, data were collected by purposive sampling and semistructured interviews with 10 managers of the Medical Services Organization of Tehran, Iran. Data analysis was performed by conventional content analysis using MAXQDA software and descriptive using SPSS software version 19. Results: The challenges facing the implementing DGRs from the experts' perspective included technological, organizational, nature, strategic, and cultural in order of priority. The three main fundamental challenges were reported; lack of integrating the DGRs with health information system (70%), frequent changes of management (70%), reducing the quality of care following early patient discharge (60%). Conclusion: The results of the present study showed that the DRG system faced with challenges and healthcare officials should apply policies and guidelines to reform the system before changing the reimbursement system in Iran. By considering the leading countries experiences in the nationalizing the DRG system field, the problems and solutions of the system can be identified and aid in the more successful implementation of these systems.

Experimental Investigation of Foam Flooding Using Anionic and Nonionic Surfactants: A Screening Scenario to Assess the Effects of Salinity and pH on Foam Stability and Foam Height.

Gravity override and viscous fingering are inevitable in gas flooding for improving hydrocarbon production from petroleum reservoirs. Foam is used to regulate gas mobility and consequently improve sweep efficiency. In the enhanced oil recovery process, when the foam is introduced into the reservoir and exposed to the initial saline water saturation and pH condition, selection of the stable foam is crucial. Salinity and pH tolerance of generated foams are a unique concern in high salinity and pH variable reservoirs. NaOH and HCl are used for adjusting the pH, and NaCl and CaCl2 are utilized to change salinity. Through analyzing these two factors along with surfactant concentration, we have instituted a screening scenario to optimize the effects of salinity, pH, surfactant type, and concentration to generate the most stable state of the generated foams. An anionic (sodium dodecyl sulfate) and a nonionic (lauric alcohol ethoxylate-7) surfactants were utilized to investigate the effects of the surfactant type. The results were applied in a 40 cm synthetic porous media fully saturated with distilled water to illustrate their effects on water recovery at ambient conditions. This most stable foam along with eight different stabilities and foamabilities and air alone was injected into the sand pack. The results show that in optimum surfactant concentration, the stability of LA-7 was not highly changed with salinity alteration. Also, we probed that serious effects on foam stability are due to divalent salt and CaCl2. Finally, we found the most water recovery that was obtained by the three most stable foams by the formula of 1 cmc SDS + 0.5 M NaCl, 1 cmc SDS + 0.01 M CaCl2, and LA-7@ pH ∼ 6 from porous media flooding. Total water recovery for the most stable foam increased by an amount of 65% compared to the state of air alone. A good correlation between foam stability and foamability at higher foam stabilities was observed.

An mHealth Self-management System for Support Children With Acute Lymphocytic Leukemia and Their Caregivers: Qualitative Co-design Study.

BACKGROUND: The unique features of smartphones have extended their use in different fields, especially in the health care domain. These features offer new opportunities to support patients with chronic conditions by providing them with information, education, and self-management skills. We developed a digital self-management system to support children with cancer and their caregivers in Iran (low- and middle-income country). OBJECTIVE: This study is aimed at the development and preliminary evaluation of a cancer self-management system (CanSelfMan) tailored to the needs of children with cancer and their parents or caregivers. METHODS: This study was conducted in collaboration with a multidisciplinary team between January and February 2020 at MAHAK's Pediatric Cancer Treatment and Research Center. We developed a self-management system in six stages: requirement analysis, conformity assessment, preparation of educational content, app prototyping, preliminary evaluation, and developing the final version. RESULTS: A total of 35 people (n=24, 69% parents and n=11, 31% children) volunteered to participate in the study. However, only 63% (15/24) of parents and 73% (8/11) of children were eligible to participate. By adopting a user-centered design approach, we developed a mobile app, CanSelfMan, that includes five main modules (knowledge base, self-management tips, self-assessment report, ask a question, and reminders) that provide access to reliable information about acute lymphocytic leukemia and the self-management skills required for side effect measurement and reporting. A web-based dashboard was also developed for oncologists and included a dashboard to monitor users' symptoms and answer their questions. CONCLUSIONS: The CanSelfMan app can support these groups by providing access to reliable information about cancer, facilitating communication between children or parents and health care providers, and helping promote medication adherence through a reminder function. The active participation of the target group can help identify their needs. Therefore, through the involvement of stakeholders such as patients, caregivers, and oncologists in the design process, we improved usability and ensured that the final product was useful. This app is now ready to proceed with feasibility studies.

The attributes of hospital-based coronary artery diseases registries with a focus on key registry processes: A systematic review.

BACKGROUND: The management of data on coronary artery disease (CAD) plays a significant role in controlling the disease and reducing the mortality of patients. The diseases registries facilitate the management of data. OBJECTIVE: This study aimed to identify the attributes of hospital-based CAD registries with a focus on key registry processes. METHOD: In this systematic review, we searched for studies published between 2000 and 2019 in PubMed, Scopus, EMBASE and ISI Web of Knowledge. The search terms included coronary artery disease, registry and data management (MeSH terms) at November 2019. Data gathering was conducted using a data extraction form, and the content of selected studies was analysed with respect to key registry processes, including case finding, data gathering, data abstracting, data quality control, reporting and patient follow-up. RESULTS: A total of 17,604 studies were identified in the search, 55 of which were relevant studies that addressed the 21 registries and were selected for the analysis. Results showed that the most common resources for case finding included admission and discharge documents, physician's reports and screening results. Patient follow-up was mainly performed through direct visits or via telephone calls. The key attributes used for checking the data quality included data accuracy, completeness and definition. CONCLUSION: CAD registries aim to facilitate the assessment of health services provided to patients. Putting the key registry processes in place is crucial for developing and implementing the CAD registry. The data quality control, as a CAD registry process, requires developing standard tools and applying appropriate data quality attributes. IMPLICATIONS: The findings of the current study could lay the foundation for successful design and development of CAD registries based on the key registry processes for effective data management.

Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation.

INTRODUCTION AND GOAL TO BACKGROUND: Due to the importance of segmentation of MRI images in identifying brain tumors, various methods including deep learning have been introduced for automatic brain tumor segmentation. On the other hand, using a combination of methods can improve their performance. Among them is the use of wavelet transform as an auxiliary element in deep networks. The analysis of the requirements of such combinations has been addressed in this study. METHOD: In this developmental study, different wavelet functions were used to compress brain MRI images and finally as an auxiliary element in improving the performance of the convolutional neural network in brain tumor segmentation. RESULTS: Based on the results of the tests performed, the Daubechies1 function was most effective in enhancing network performance in segmenting MRI images and was able to balance the performance and computational overload. CONCLUSION: Choosing the wavelet function to optimize the performance of a convolutional neural network should be based on the requirements of the problem, also taking into account some considerations such as computational load, processing time, and performance of the wavelet function in optimizing CNN output in the intended task.

Functions and Outcomes of Personal Health Records for Patients with Chronic Diseases: A Systematic Review.

Introduction: The personal health record (PHR) makes it possible for patients to access, manage, track, and share their health information. By engaging patients in chronic disease care, they will be active members in decision-making and healthcare management. Objectives: This study aimed to identify the functions and outcomes of PHR for patients with four major groups of chronic diseases (cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases). Method: A systematic review was conducted on studies published in PubMed, Scopus, Web of Science, and Embase. Searching and screening were performed using the keyword of "Personal Health Record" without time limitation, and ended in August 2018. Results: In total, 3742 studies were retrieved, 35 of which met the inclusion criteria. Out of these 35, 18 studies were conducted in the United States, 24 studies were related to patients with diabetes, and 32 studies focused on tethered PHRs. Moreover, in 25 studies, the function of viewing and reading medical records and personal health information was provided for three groups of chronic patients. Results showed that the use of PHRs helps the management and control of chronic diseases (10 studies). Conclusion: It is recommended that integrated PHRs with comprehensive functions and features were designed in order to support patient independence and empowerment in self-management, decrease the number of referrals to health centers, and reduce the costs imposed on families and society.

The Features of Mobile-Based Software in Self-Management of Patients with Asthma: A Review Article.

BACKGROUND: The use of mobile-based software for the self-management of patients with asthma improves the quality of life, reduces healthcare costs, provides effective health care interventions in asthma, and supports the patients in self-management. The current study was performed to identify the features of mobile-based self-management software for patients with asthma (MSSPA). MATERIALS AND METHODS: The present review study was performed in 2018. Four databases including PubMed, Scopus, Emerald, and Google Scholar were screened by the combination of selected keywords. Data were collected using a data extraction form. Data were analyzed using the content analysis method. Results were abstracted and reported based on the study objectives. RESULTS: Of the 297 articles retrieved during the first round of search, 24 were selected; 15 of which were the original articles (62.5%). As the most important applications of MSSPA, it could be used as a tool to support patients in self-management, provide them with educational information, and self-observation. Also, 75% of the studies (n=18) emphasized the effectiveness of MSSPA. Identification of the required field of the software was the most important requirement in using MSSPA. Nevertheless, some of the studies reported the low quality and compatibility of some designed apps compared with those of the available information systems. CONCLUSION: Identification of MSSPA features and considering them in new versions can promote the quality of MSSPA. However, according to the results of the study, in addition to identifying the software features, more attention should be paid to the users' needs in software design.

Developing a Model for National Health Information Governance Program in Iran.

With regard to the importance of health Information Governance (IG) programs in improving the quality and reducing the cost of healthcare services and the lack of a coherent health IG program in Iran's health system, this study aimed to develop a model for national health information governance program in Iran. The present research was an applied, cross-sectional descriptive study that was done in three steps, including literature review, development of a model for national health IG program in Iran, and model validation. In the third step, we used a questioner to validate the model through the Delphi method. Data analysis was done by descriptive statistics. The model for the national IG program in Iran was developed in 3 main sections consisting of 13 components, 12 principles, natural and judicial authorities of the health IG program, and their job description. Findings from the validation of the initial model showed that most experts (93%) confirmed the components and sub-components, principles, and natural and legal bodies supervising the national health IG program and their job description in the proposed model. Considering the structure of the Iranian health system, it was recommended to establish a health IG council in the Ministry of Health and Medical Education in order to develop guidelines and give advice to health care providers. Based on the proposed model, directors and staff of different departments of health care centers, especially those involved in health IG, are also responsible for the better implementation of the national health IG program.

Smartphone apps to help children and adolescents with cancer and their families: a scoping review.

Introduction: Considering the importance of empowering patients and their families by providing appropriate information and education, it seems smartphone apps provide a good opportunity for this group. The purpose of this review was to identify studies which used smartphone apps to help children and adolescents with cancer and their families. Method: Arksey and O'Malley's framework was employed in this review. To examine the evidence on the design and use of smartphone apps for the target group, PubMed, Embase, Scopus and Web of Science databases were searched from 2007 to November 2018. Results: Twenty-four articles met the inclusion criteria, with 33% being conducted in the USA and 21% in Canada. Moreover, in 20 studies (83%), app was specifically designed for children and adolescents, with only three studies (13%) for parents and one study (4%) for both. The main modules of smartphone apps in these studies included symptom assessment (90%), provision of information and education (74%), communication with caregivers (57%), social support (30%) and calendar and reminder (21%). Conclusions: Due to the easy access to smartphones without a costly infrastructure compared to landline phones, the use of mobile health (m-Health) has become a suitable method of providing healthcare services, especially for cancer. Use of smartphone apps, increases patient and families' access to reliable and suitable education and information regarding the disease. Thus, healthcare policy-makers in developing or underdeveloped countries can exploit the health-related potentials of m-Health following the experience of developed countries.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PURPOSE: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. MATERIALS AND METHODS: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. RESULTS: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. CONCLUSION: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Presentation of a Novel Model for Evaluation of Commercialization of Research and Development: Case Study of the Pharmaceutical Biotechnology Industry.

The current situation in Iran suggests an appropriate basis for developing biotechnology industries, because the patents for the majority of hi-tech medicines registered in developed countries are ending. Biosimilar and technology-oriented companies which do not have patents will have the opportunity to enter the biosimilar market and move toward innovative initiatives. The present research proposed a model by which one can evaluate commercialization of achievements obtained from research with a focus on the pharmaceutical biotechnology industry. This is a descriptive-analytic study where mixed methodology is followed by a heuristic approach. The statistical population was pharmaceutical biotechnology experts at universities and research centers in Iran. Structural equations were employed in this research. The results indicate that there are three effective layers within commercialization in the proposed model. These are a general layer (factors associated with management, human capital, legal infrastructure, communication infrastructure, a technical and executive infrastructures, and financial factors), industrial layer (internal industrial factors and pharmaceutical industry factors), and a third layer that included national and international aspects. These layers comprise 6 domains, 21 indices, 41 dimensions, and 126 components. Compilation of these layers (general layer, industrial layer, and national and international aspects) can serve commercialization of research and development as an effective evaluation package.