Although the past decade has witnessed the development of many self-management mobile health  apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.Self-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps. Self-management mobile health  apps use mobile devices for health services and offer a sustainable means of enhancing self–health care management to achieve wellness goals, such as health monitoring, disease detection, behavior change, and emotion management, enabling individuals to independently manage their lives and activities and make appropriate decisions. They comprise different categories, ranging from simple apps for water intake tracking to complex apps that can adapt to individuals’ lives based on their activities. The power of self-management mHealth apps has increased with the use of built-in mobile technologies  and machine learning (ML) algorithms to create intelligent mobile apps. Such apps are characterized by personalized services and recommendations and by the automatic logging and recognition of individuals’ behaviors and activities.Although mobile apps are extensively used for self–health care management, the ongoing development of mobile device technologies and programming languages has increased the need for mobile app solutions to keep pace with development practices. Developing high-quality intelligent self-management mHealth apps requires substantial knowledge of mobile programming languages, app architectures, design patterns, and latest technologies. Considerable time is required from researchers and developers to learn and master such knowledge because of the different characteristics, requirements, and components of each mobile app. Furthermore, many challenges and issues may arise during app development. Therefore, we conducted a comprehensive systematic literature review (SLR) and evaluation of existing self-management mHealth apps that focus on self–health care management, based on a formal protocol, to analyze their characteristics and current challenges, including infrastructure, functionalities, user interface (UI) components, screen navigation, services and technologies, security and authentication, use of architectures and patterns, evaluation, and issues to provide a guide for self-management mHealth app infrastructure to facilitate further development.Although several SLRs on mHealth apps have been previously conducted, our SLR is distinguished by providing engineering perspectives on 3 different sources: research papers, app stores, and GitHub repositories. The SLR involved the analysis and synthesis of empirical evidence by software engineering researchers to help researchers and developers in three main aspects: (1) to identify the characteristics and challenges of existing self-management mHealth apps, (2) to understand the differences and similarities of existing self-management mHealth apps and find the gap between research papers and commercial and open-source apps, and (3) to suggest future research directions based on gaps identified in the domain. The main contributions of this study are as follows:The definition of an SLR protocol following the SLR guidelines by Kitchenham and Charters , which iExtraction, analysis, synthesis, and reporting of empirical evidence from the selected primary studiesProvision of guidance for researchers and developers to deeply understand the characteristics and challenges of self-management mHealth appsSuggestions of solutions to overcome the limitations of existing self-management mHealth appsThe principal aim of this SLR was to obtain a detailed view of existing self-management mHealth apps used for self–health care management. A specific objective was to better characterize the functional and technical aspects of these apps.This review presents the main characteristics and challenges of the self-management of mHealth apps. We targeted apps that use mobile devices for the self–health care management of general users. The study process diagram is shown in Phase 1: a comprehensive review of existing research papers on self-management mHealth apps in digital librariesPhase 2: an exploration of self-management mHealth apps available through Britain’s Apple App Store and Android Google Play, as well as an evaluation of the selected apps using the Mobile App Rating Scale (MARS) [Phase 3: analysis of open-source apps available on GitHub based on specific criteria and an automatic tool  [This section presents related literature in the knowledge base, which forms the foundation of our SLR. The SLR follows the Kitchenham and Charters  guidelinTo investigate and deduce empirical evidence of existing self-management mHealth apps, we determined 2 key research questions (RQs):RQ1: What are the main characteristics of current self-management mHealth apps?RQ2: What are the challenges and issues faced by current self-management mHealth apps?We determined some characteristics of self-management mHealth apps that we were interested in monitoring because of their importance in the development of mobile apps. We collected these characteristics based on their availability during each phase .Crucial functionalitiesEach app comprises several components that define its functionality. We have summarized the main functionalities of the reviewed apps.User interface componentsWe determined the user interface components used by the users for interaction.Navigation and structureHere, we explored the apps’ organization and methods of navigating the app screens.Services and technologiesWe determined the remote and local services that are external to the assigned app but handled by it, such as machine learning algorithms; built-in technologies; and access to other apps, frameworks, and libraries.Security featuresWe were interested in defining the security aspects and authentication mechanisms used in the self-management of mobile health  apps.Architectures and patternsThere are different architectures and patterns for building apps, such as client-server, model-view-controller, and view-interactor-presenter-entity-router. In this study, we determined the most commonly used architectures and patterns. Furthermore, we specified the architecture for implementing machine learning, including web-based inference, offline inference, or both.Logging mechanismsWe determined the apps’ method for logging data, either manually or automatically.Development approachWe explored the main development approaches which developers use to construct apps.Operating system and programming languageWe identified the operating system and programming languages used in the reviewed apps.EvaluationHere, we were concerned with the techniques for evaluating self-management mHealth apps used by researchers in phase 1. Furthermore, we evaluated the selected apps using Mobile App Rating Scale  and SonaWe identified the limitations and challenges faced by researchers and developers when developing self-management mHealth apps. In phase 1, we summarize the researcher’s challenges and issues. In phases 2 and 3, we identified potential issues that can affect the quality of apps using MARS and SonarCloud, such as design issues, bugs, code smell, and duplication.A search strategy was followed to explore the literature that could help answer the RQs. It comprises 3 main stages: defining search strings, selecting data sources, and searching the data sources. As previously mentioned, the review is divided into 3 independent phases, each of which has separate data sources and search strings. In the first phase, we followed the quasi–gold standard  approachOR and AND. On the basis of our RQs, 5 search strings were identified, as listed in The search string of the first phase was defined by combining synonymous terms using mHealth, Healthcare, and Health. However, many unrelated apps were identified. Consequently, we changed the search strategy to a manual exploration of the Top Free App under the Health and Fitness category. These apps are free to download, although many require a monthly payment or upgrade payment to access all features.In the second phase, an extensive search was performed in Britain’s Apple Store and Google Play from August 16 to August 21, 2020. Although the search was limited to Britain’s app stores because of the requirement to specify the user’s location, most of the selected apps were available in other stores. We started the automatic search by applying the following search string: mHealth, Healthcare, and Health in GitHub from October 5 to October 9, 2020, to identify open-source mHealth apps.In the third phase, we used the search strings Search strings“mHealth” AND (“app” OR “application”).“mobile” AND “health” AND (“app” OR “application”).“Personal” AND “Mobile” AND “healthcare” AND (“app” OR “application”).“Self-management” AND “healthcare” AND (“app” OR “application”).“Smartphone” AND “health” AND (“app” OR “application”).To find studies related to phase 1, we followed an automatic search using the following digital libraries: IEEE Xplore, ScienceDirect, ACM Digital library, SpringerLink, MEDLINE, PubMed, and Sage. To complement the automatic search, a manual search was conducted on relevant journals, including Pervasive and Mobile Computing and Mobile Networks and Mobile Networks and Applications. To find as many studies as possible, we used the snowballing strategy to gather additional studies from the reviewed studies. We used Google Scholar to search for further studies identified from snowballing. In phase 2, we used an automatic and manual search within the following official digital British app stores: Apple iPhone (App Store) and Android (Google Play). In the third phase, we applied an automatic search on GitHub to download open-source apps.To identify all related studies, search strings were applied to the selected digital libraries. Initially, 7982 results were retrieved within the chosen search string as follows: 2431 (30.46%) papers from IEEE, 223 (2.79%) papers from ScienceDirect, 158 (1.98%) papers from ACM, 4190 (52.49%) papers from SpringerLink, 461 (5.78%) papers from MEDLINE, 468 (5.86%) papers from PubMed, and 51 (0.64%) papers from Sage. The results were filtered based on the title and abstract, and duplicated and unrelated papers were removed. Then, of the 7982 papers, 654 (8.19%) were downloaded for examination. According to the inclusion and exclusion criteria, of the 654 studies, 44 (6.7%) studies were included from the automatic search. Furthermore, we found 2 studies from the manual search and 6 studies from snowballing. Thus, we collected 52 studies from automatic and manual searches, as well as snowballing. Manual search studies were from ScienceDirect and SpringerLink. For the 6 studies from snowballing, 3 (50%) papers were from IEEE, and 1 (17%) paper each was from SAGE, ACM, and SpringerLink.The systematic search process was applied to identify mobile apps for the general population in phase 2. Initially, 1904 apps were retrieved with the chosen search strategy: 803 (42.17%) apps from Apple’s App Store and 1101 (57.83%) apps from Android’s Google Play. The results were filtered based on the inclusion and exclusion criteria and availability in both app stores. After removing duplicate apps, we downloaded and explored 100% (48/48) of apps on each platform, iOS and Android. A total of 48 apps were reviewed for further refinement after applying the inclusion and exclusion criteria. Then, 6% (3/48) of apps from each store were removed as they were not used for self–health care management. Therefore, of the 42 apps, the final number of apps included on each platform was 21 (50%).In phase 3, the systematic search process was applied to GitHub to identify self-management mHealth apps for the general population using the Swift and Kotlin programming languages. Initially, 491 apps were retrieved with the chosen search strategy as follows: 370 (75.4%) apps with Swift programming language and 121 (24.6%) apps with Kotlin language. After removing duplicates and unrelated titles, of the 491 apps, we obtained 64 (13%). These apps were downloaded and analyzed based on the inclusion and exclusion criteria. After applying the inclusion and exclusion criteria, of the 64 apps, we obtained 24 (38%). These apps were analyzed using the SonarCloud tool, including 13 (54%) iOS and 11 (46%) Android apps.Phase 1Inclusion criteriaPapers presenting the design and implementation of either or both Android and iOS self–health care management appsEnglish peer-reviewed papers published from 2008, the year the App Store was announced , to 2020The most recent and complete version of a study if it had multiple versionsExclusion criteriaPapers presenting theoretical research without implementationPapers describing apps for wearable devicesPapers targeting children or people with special needsShort papers with <4 pages as they could not contain sufficient informationPhase 2Inclusion criteriaApps designed for general users, existing in both Apple and Android app storesApps that were free to download and could support the English languageApps stating the aim as self–health care managementApps rated by >10,000 users and having a score of ≥4 out of 5 to ensure that the selected apps were satisfyingThe last update of the current app had to be from January 01, 2018 to ensure that the app was up to dateExclusion criteriaApps designed for children or people with special needsApps related to an external device such as a smartwatches or shoesApps that did not clarify the date of the last updatePhase 3Inclusion criteriaApps written using either or both Swift or KotlinOpen-source apps that supported the English languageApps stating the aim as self–health care managementThe source code of the app had to exceed 1000 lines of codeExclusion criteriaApps designed for children or people with special needsApps related to external devices such as smartwatches or shoesIn phase 1, of 52 studies, 27 (52%) were from IEEE, 18 (34%) from SpringerLink, 3 (6%) from ACM, 2 (4%) from PubMed, 1 (2%) from ScienceDirect, and 1 (2%) from Sage. MEDLINE digital data sources were not included as the downloaded papers focused on analyzing user behavior rather than app development, which was outside the scope of this study. In phase 2, we reviewed 21 apps on each platform, each of which had 2 versions: 1 in the Apple App Store and 1 in Android Google Play. In phase 3, a group of 24 open-source apps was reviewed manually and automatically, including 11 (46%) Android and 13 (54%) iOS apps.A1) to represent the app name of the 2 versions, and we specify the differences if they are found on each app.In total, we reviewed 41 iOS and 77 Android apps, including 7 (17%) iOS and 45 (58%) Android apps in phase 1, 21 (51%) iOS and 21 (27%) Android apps in phase 2, and 13 (54%) iOS and 11 (46%) Android apps in phase 3. The apps in phase 2 have almost the same functionalities and structures on both platforms. Thus, we use a letter with a number  of studies. The next and back buttons were used in 10% (5/52) of studies. One of the studies included navigation through both the next and back button and the tab, whereas another study supported navigation with the next and back button, tab pages, and hamburger menu. Approximately 4% (2/52) of studies reported that the user could use the home page to navigate to other pages.Most apps  in phase 2 had, at start-up, multiple simple pages for customization with backward and forward arrows; however, the basic app was presented as a tab structure with multiple tabs. In the case of iOS, of the 42 apps, 18 (43%) were designed with a tab structure, 2 (5%) supported navigation with tabs and a hamburger menu, and the remaining 1 (2%) app had a main page with hamburger menu. For Android, of the 42 apps, 17 (40%) followed the tab structure, 3 (7%) had a page with a hamburger menu, and 1 (2%) had a main page with buttons to navigate to other pages. Consequently, 86% (36/42) of apps had a similar structure on both platforms, where 81% (34/42) of the apps followed the tabbed UI approach, and approximately 5% (2/42) of the apps had a main page with hamburger menu.In phase 3, 71% (17/24) of apps had a tabbed UI structure, including 33% (8/24) having a tab with a back arrow to navigate to the previous or main page, 29% (7/24) of apps supporting navigation through only tabs, and 8% (2/24) of apps supporting navigation through both tabs and hamburger menu. Approximately 29% (7/24) of apps had a main page structure, including 8% (2/24) supporting a main page with a back arrow, 8% (2/24) having a main page with a back arrow and menu, 8% (2/24) having a main page with navigation buttons to other pages, and 4% (1/24) having a main page with a menu.Several local and remote services were used in the reviewed studies and apps. These services included accessing remote and local libraries and technologies; accessing external libraries using an application programming interface (API) such as Clarifai, ZXing (Zebra Crossing), Edamam, Dialogflow, and ToneAnalyzerV3 (IBM Watson); web services; cloud services such as Google’s cloud computing engine (Firebase); linking to other apps such as social media; and Google Maps. In addition, some apps used developer frameworks such as HealthKit, ARKit, SceneKit, and StepCounter.Many studies and apps in the 3 phases had access to built-in technologies such as cameras, motion sensors (accelerometers and gyroscopes), location (GPS), and microphones. Phases 2 and 3 had access to photographs, vibrations, networks, audio, phones, and storage. These technologies can collect various types of data, including sensor data, images, voice, and text. The collected data can be used to process through ML algorithms to obtain useful information.Several studies in phase 1 used ML algorithms that differentiated between supervised and unsupervised learning with their branches: classification, regression, clustering, and association. Some studies evaluated multiple algorithms to determine the most accurate algorithm. Most of the investigated studies used supervised learning for classification, including naïve Bayes, support vector machines, logistic regression, K-nearest neighbor, rule-based classifiers, decision trees, ridge, AdaBoost, bagging, Gaussian processes, ensemble of nested dichotomies, rotation forest, Fisher vector representation, linear classifiers, and artificial neural networks (NNs), which included specific types such as deep NNs and deep convolutional NNs. Moreover, supervised learning was used for regression, which comprised linear regression (LR), Bayesian ridge, support vector regression, gradient boosting, and AdaBoost. Some studies used algorithms for both classification and regression, such as random forest. Other studies used unsupervised learning for clustering, such as the density-based spatial clustering of apps with noise and molecular complex detection. Other used algorithms were the kernel null Foley-Sammon transform and t-distributed stochastic neighbor embedding, the threshold method, decision tables, radial basis function kernel, ensemble extreme learning machine, and sequential minimal optimization. In contrast, many studies used measurement methods, including BMI, basal metabolic rate, Gaussian and LR functions, and general calculations.Authentication through log-in was the principal security feature in the 3 phases, which was achieved either by creating an app account using email or linking it with other accounts such as Facebook, Apple, or Google. However, only one of the studies (S16) mentioned authentication through log-in in phase 1. This limitation could be because of the focus on building and evaluating a new ML algorithm rather than a complete app. Approximately 95% (40/42) of apps in phase 2 and 42% (10/24) of apps in phase 3 supported log-in. The log-in password was hidden using points or stars.In phase 1, some studies involved the development of mobile apps to collect real data; however, they analyzed these data on a physical computer, server, or specific tool  to extract features and identify the most accurate ML algorithm. Most of these studies focused on building a suitable model using ML algorithms without providing real information about the mHealth app, which integrates the final model with implementation or architecture. These apps stored the automatically-collected data remotely on a server  using a client-server architecture (a web-based mechanism) or locally  on a mobile device (an offline mechanism). Approximately 2% (1/52) of studies used pre-existing data sets for processing on a physical computer to build the model. Other studies focused on developing complete self-management mHealth apps, in which 54% (28/52) of studies used a web-based mechanism for developing the apps. They followed a client-server architecture, where a mobile app acts as a client node to receive data and connect to a server (sometimes a cloud server) to process data or perform computations. Other studies  used an offline mechanism to process received data locally on mobile devices. One of the studies (S24) in phase 1 followed MobileNetV2 (deep NN architecture). In S13, the authors used cloud-based virtualization (hypervisor architecture), which depends on virtual swaps between mobile sessions to separate the physical resources into virtual resources on a cloud server to use computing power efficiently. Consequently, in phase 1, 58% (30/52) of the studies used a web-based mechanism, and 37% (19/52) used an offline mechanism. In phase 3, 83% (20/24) were web-based apps and 17% (4/24) were offline apps.Regarding the design pattern, 2 authors (S38 and S40) in phase 1 stated that they used the model-view-controller (MVC) design pattern, whereas, in phase 3, a total of 3 design patterns were used in the explored apps: MVC, model-view-viewmodel, and view-interactor-presenter-entity-router. MVC was used in 75% (18/24) and model-view-viewmodel in 21% (5/24) of the apps. View-interactor-presenter-entity-router was used in 4% (1/24) of apps.In terms of the architecture for implementing ML mobile apps in phase 1, of the 52 studies, 18 (35%) used web-based inference based on a pretraining model by researchers, 10 (19%) used offline inference on devices based on a pretraining model by researchers, 3 (6%) used both web-based and offline inference based on a pretraining model by researchers, and 2 (4%) used web-based inference based on ready solutions (API), whereas, 29% of ML apps in phase 3 used web-based inference based on ready solutions (API). Web-based and offline inferences were used for the same functionality in S30 and S51, where the web was used to accelerate the process, and offline was used when a connection was lost but with lower performance and high resource consumption. However, S1 used both web-based and offline inferences for different functionalities.Most studies and apps collected data from user information, activities, or behaviors, which could be gathered automatically from sensors, manually by users’ logging, or both. Automatic collection can be achieved either through synchronization and importing of data from other apps or by accessing built-in technologies or developer frameworks such as HealthKit on iOS or Google Fit on Android. Then, the collected data were automatically analyzed using ML algorithms or calculation methods to provide useful feedback and personalized services.In phase 1, most studies  used built-in technologies for automatic data collection. Other studies required manual input of information , image capture , or voice recording . Few studies  supported both automatic collection and manual inputs.In phase 2, manual logging was used in 62% (26/42) of apps and could be performed through barcode scanning, input of personal information, or searching the app’s internal database. Approximately 24% (10/42) of the apps supported automatic logging through built-in technologies or synchronization with other apps. Approximately 14% (6/42) of the apps supported both automatic and manual logging.Most apps  in phase 3 supported manual logging of data such as personal information, food and water consumption, sleep, and emotion. Approximately 17% (4/24) of the apps supported manual logging and synchronization. One of the apps presented real-time information from an external server, and another app supported the automatic collection of data.Prototyping was the most commonly used approach adopted in 15% (8/52) of studies. Other studies used agile (S35), extreme programming (S15), iterative development (S38), or user-centered approaches (S26 and S29). The remaining studies in phase 1 and the other phases did not mention their approach.In phase 1, 85% (44/52) of studies targeted Android, forming the majority. Approximately 13% (7/52) supported iOS, and 2% (1/52) targeted both iOS and Android. In terms of operating system (OS) versions, S2 used Android OS version 4.4.2; S6 and S28 used Android OS version 4.1.2; S22 used Android OS version 1.6; S29 used Android OS version 4.2.2 (Jellybean); S41 used Android OS version 2.3; and S39 targeted both platforms, with Android version 4.0 (Ice Cream Sandwich) and iOS version 3.2. The Java programming language was used in S1, S3, S16, S17, S21, S28, S31, S34, S40, and S38. Plain Old Java Objects were used in S42. Each selected app in phase 2 was available on both the Android and iOS OSs. We reviewed and evaluated 24 apps in phase 3, 11 (46%) of which had Android OS and 13 (54%) of which had iOS.Different evaluation techniques were used in phase 1; however, most studies  measured the performance of ML algorithms through experiments, pilot studies, or randomized controlled trials and compared the performance with state of the art. Furthermore, some studies used a specific tool to test the accuracy of various classifiers and select the most appropriate one. For example, 12% (6/52) of the studies used the Waikato Environment for Knowledge Analysis, and approximately 6% (3/52) used MATLAB. Furthermore, cross-validation was used in approximately 17% (9/52) of the studies to accurately calculate the performance metrics. Such metrics could include confusion matrix, sensitivity, specificity, and accuracy. Other studies  evaluated through a usability study, such as user acceptability and subjective surveys. Approximately 10% (5/52) of the studies combined either or both comparisons with state-of-the-art and usability studies.In phase 2, we applied a systematic quality evaluation of the apps selected from app stores. The evaluation was conducted by the first author and revised by the second author. Each app was opened on both the Android and iOS platforms for evaluation. We evaluated the apps using MARS, comprising 5 main sections , which are presented in In phase 3, we evaluated the apps using SonarCloud, as shown in Some studies in phase 1 mentioned general challenges related to mobile devices and app architecture. The first challenge was the restricted number of resources , including computational power, storage capacity, and energy efficiency. Other studies referred to the challenge of dealing with a large variety of mobile devices with different software and hardware, which complicates the development of new algorithms (S28) and causes varying levels of accuracy when data are collected from sensors (S32). Furthermore, S17 mentioned security as a challenge for mobile apps.In contrast, S23 summarized the drawbacks of cloud-based approaches (a web-based mechanism): latency, privacy, cost, and connectivity. S23 also mentioned the limitations of the offline mechanism that integrates the model in the app, which requires republishing a new version of the app with each update of the model and could be inconvenient for the user and result in a waste of time. S42 mentioned the challenge of designing mHealth apps that succeeded in attracting and sustaining users’ interests.Other studies encountered difficulties in collecting and dealing with the collected data, such as identifying the position of the device on a user’s body when collecting data in 8% (4/52) of studies; determining the set of collected sensors in S11; extracting efficient features from collected data in 8% (4/52) of studies; accurate recognition of activities in real time in 6% (3/52) of studies; and accurate detection of heart rate in 4% (2/52) of studies, which depends on the lighting conditions and location of the finger on the camera lens.In terms of image processing, the authors of S45 mentioned the limitations of mobile devices in dealing with complex images for extracting features and classifying algorithms. The authors of S23 and S15 mentioned that the properties of an image might be affected by various factors, such as the angle, brightness, focal distance, and camera resolution. The authors of S15 and S49 stated that food recognition could be difficult because of several factors, such as diversity of food size, form, color, and texture, as well as deformation and segmentation of food elements, which may affect the identification of food type and calculation of quantity and nutritional value.In phase 2, we found that most apps  used manual logging of data, such as manually inputting the type and duration of the exercise, user emotion, and the category and quantity of consumed food, without using ML algorithms such as automatic recognition of activity and food type. In addition, all explored apps targeted general users without experimental or clinical studies to support their safety, reliability, and effectiveness. We also noticed that most apps  needed the internet to access some functions, two of which supported offline working through downloading of content. Approximately 33% (14/42) of apps could work without an internet connection; and 5% (2/42) of apps could not work at all without the internet. We also checked whether the installed apps were hybrid or native by activating the developer option on the Android OS from settings and by turning on the layout bounds option. We found that most Android apps  were native. However, this method was not applicable to apps developed using react-native or flutter as they convert the language to native app code. Therefore, the number of native apps was not completely accurate. However, it clarifies to some extent that separate implementations need to be written for each platform, which requires additional time, cost, and effort.Each app in phase 3 was analyzed and evaluated using SonarCloud, as shown in In this section, we compare and synthesize the collected data and findings from the first, second, and third phases to answer our RQs.By comparing the characteristics and challenges of the 3 phases, we identified that the phases were different in terms of the use of ML, processing techniques, functionalities, inference of ML, logging mechanisms, evaluation techniques, and challenges. However, they were similar in the most frequent focus, UI components, navigation and structure, services and technologies, authentication features, and architecture and patterns. As shown in In terms of processing techniques, most studies from phase 1 used data  and image  processing, whereas calculation methods were the most used techniques in phase 2  and phase 3 . Data processing included sensor data, questionnaire answers, conversation, and specific data such as goals and preferred meals. The calculation depended on specific equations such as BMI. With respect to focus, the 3 phases were similar, with the most frequent focuses being on physical health and weight control. However, they differed for crucial functionalities, where recognition  and detection  were the most frequent functionalities in phase 1. Monitoring was the most crucial function in phases 2 and 3, representing 52% (22/42) and 54% (13/24), respectively. The 3 phases were almost similar in terms of UI components and navigation. The most commonly used UI components were labels, images, and buttons, and most apps were designed with a tab structure.Regarding services and technologies, the camera, GPS, motion sensors, and microphones were frequently used in the 3 phases. The motion included access to accelerometers and gyroscopes and had the highest percentage  in phase 1, whereas the camera  was the most frequent built-in technology in phase 2, and GPS  had the highest percentage in phase 3. Apps in all phases used log-in functionality as a security feature for authentication, which could be achieved by creating a new account with the app using a social media account. For architecture and patterns, most studies in phase 1  and apps in phase 3  used client servers. MVC was the only pattern used in phase 1 and the most used pattern in phase 3 . However, phase 2 did not contain an architecture section as there was insufficient information about it in the app stores. In terms of inference of ML, many apps  in phase 1 used web-based inference using a pretrained model by researchers. Phase 3 concentrated on web-based inference, with 29% (7/24) of apps using ready solutions, such as the IBM Watson API. Furthermore, the prototype was the most commonly used development approach in phase 1. However, phases 2 and 3 did not include the development approach section because of limited information.Most apps (46%) in phase 1 were distinguished by automating the logging mechanism using built-in technologies and ML algorithms to automatically recognize types, quantities, and calories of food and physical activities such as walking, running, or jumping with burned calories. Apps in phases 2 and 3  concentrated on monitoring functionality through manual logging of specific activities such as eating an apple or walking.We used different techniques for the evaluation. In phase 1, we summarized the evaluation techniques used by the authors of the research papers, where 58% (30/52) evaluated performance. We used MARS evaluation in phase 2 and SonarCloud in phase 3. Thus, the results of the evaluation were different for each phase.In terms of challenges and issues, phase 1 mentioned several challenges, such as the restricted number of resources in mobile devices, mobile device fragmentation, security of mobile apps, drawbacks of web-based and offline mechanisms, designing of an attractive and sustainable mobile app, and difficulties in collecting and processing data to design an efficient ML algorithm. Phase 2 reported the importance of network connectivity, which may affect the efficiency of a mobile app in the case of a connection loss. In addition, it highlighted the issues of writing separate implementations for each platform and the absence of care provider involvement in the development and evaluation phases. Of the analyzed apps in phase 3, we found that iOS apps had more bugs than Android apps, which may, however, be because of the developers and not the platform. Most apps on both platforms had code smells, duplications, and performance issues. Only iOS apps had vulnerability issues. Furthermore, the logging mechanisms of the second and third phases were primitive and needed improvement to remain up to date with those described in research papers.As a result, we found that commercial apps in phase 2 and open-source apps in phase 3 had more common aspects than the apps of research papers in phase 1. They were similar in that most used calculation methods as processing techniques and monitoring as a crucial functionality. In addition, they were simple and complete apps that were partially supported by ML and automatic logging. In contrast, apps in phase 1 were complex and intelligent, although some of them were incomplete, presenting a gap between real and research paper apps.This research involved various studies and apps designed for the general population with the aim of self–health care management. Most of these apps were developed with a specific focus, requiring users to download several apps to cover different aspects. Therefore, multidimensional well-being apps that combine multiple focuses need more research and development as it is better to download a single app with a set of features than to download several apps.Furthermore, we found that the development of self-management mHealth apps required significant efforts from researchers to build and evaluate new algorithms and from developers to deal with different techniques and frequent updates of apps to stay up to date with the latest technologies. A parent example is developing apps with ML algorithms, which comprise several steps implemented manually, including collecting data, extracting features, and applying several ML algorithms to determine the most accurate algorithm. For example, the authors of S7 compared 7 classifiers: support vector machines, naïve Bayes, K-nearest neighbor, decision trees, LR, NNs, and rule-based classifiers. Furthermore, many other studies manually compared multiple algorithms to find the best algorithm, requiring a long time and great effort from researchers. Another example is the development of the same app in different languages and techniques to be compatible with multiple platforms.In contrast, mobile devices are handheld gadgets with limited resources , which significantly hinders the improvement of service qualities such as ML algorithms that require dealing with intensive data and heavy computations. Connection with remote services such as the cloud can address these limitations. Therefore, many apps that used ML algorithms followed a web-based inference to achieve optimal performance within a reasonable time. However, this approach is generally insufficient when the connection is lost and may pose security issues. Therefore, some apps integrated a pretrained model with the mobile app (offline inference), which may cause some difficulty when updating the models. Only 4% (2/52) of apps supported both web-based and offline inferences for the same functionality but with lower performance and high energy consumption. Consequently, many challenges still exist related to finding an adequate algorithm that fulfills the specific requirements of intelligent self-management mHealth apps, as well as an efficient architecture that supports web-based and offline inferences with the ability to reduce resource consumption and execution time and increase performance, specifically when using large ML algorithms.To ensure the consistency of our evaluation results, all evaluation processes in the second and third phases were performed in the same manner by the first author. The evaluation in phase 2 was applied to the same app on both Android and iOS platforms. Furthermore, the evaluation process of phase 3 was repeated to double check the results.To ensure that we adopted unbiased and consistent procedures in the selection, we used the quasi–gold standard approach , which iWe reviewed studies that involved the implementation of a research study from 2008 to 2020. However, the generalizability of our findings could be affected by the exclusion of studies that presented theoretical research without implementation, as well as studies and apps linked with external devices.The RQs of this review could not entirely cover all of the reviewed research papers and self-management mHealth apps. Some research papers and apps had fewer or more details than the information identified in our RQs.To comprehensively identify the characteristics and issues of the selected studies, we divided the review into 3 main phases using different data sources, including research papers, commercial apps from digital Apple and Android app stores, and open-source apps from GitHub.In phases 1 and 3, we selected mobile apps developed with either Kotlin or Swift on the Android and iOS platforms, respectively. These apps had a wide range of functionalities and purposes related to self–health care management.We extracted data from the assigned studies and self-management mHealth apps from the app stores and GitHub. To ensure the validity and consistency of the extracted data, the protocol for the data extraction strategy and format was developed by the first author and reviewed by the second author. In addition, we created a Microsoft Excel file to record and arrange the extracted data and check their relevance to our RQs.This study had some limitations. The review was prepared and reviewed by 2 authors; however, it would have been better if it had more reviewers. In the second phase, apps were limited to self–health care management and included only those available in both Android and iOS digital app stores. The review would be more comprehensive if phase 2 included other categories of self-management mHealth apps, and phases 1 and 3 included other programming languages, such as Java and Objective-C. Furthermore, the review might have been more generalizable if it included an app designed for children and people with special needs.Developing mobile apps requires significant effort because of the complexity of self-management mHealth apps. Therefore, we have started the development of a framework that accelerates and facilitates the development of mHealth apps ,83. The Several SLRs have been conducted on mHealth apps. For example, in the study by Mosa et al , the autThis study aimed to conduct a comprehensive review and evaluation of self-management mHealth apps. This study gathered empirical evidence from the literature to identify the characteristics and challenges of existing self-management mHealth apps focused on self–health care management. The main contribution of this research is its detailed analysis and synthesis of relevant literature by software engineering researchers to deeply understand state of the art and provide guidance for the development of complex self-management mHealth apps.In this research, we presented the details of an SLR on self–health care management mobile apps that consisted of three main phases. The results of this research can serve as a basis for researchers and developers to understand the characteristics of self-management mHealth apps and know the existing challenges that require further research. In phase 1, we reviewed 44 studies published between 2008 and 2020. In phase 2, 42 apps were reviewed and evaluated using the MARS. In phase 3, we reviewed and evaluated, using SonarCloud, 24 open-source apps from GitHub, including both iOS and Android platforms.The research papers in phase 1 presented many interesting ideas, used different ML algorithms, and supported automatic logging mechanisms. These algorithms were used to process data to automatically recognize physical activities; diagnose diseases; recognize the types, quantities, and calories of food; and predict the user’s emotion. However, the results of phase 1 show the need for optimization of the architecture and algorithm of intelligent self-management mHealth apps to efficiently include web-based and offline inferences, reduce resource consumption, and increase performance.In phases 2 and 3, we found that most apps in app stores and GitHub focused on monitoring and analysis functionalities that use calculation methods to create progress reports and charts. However, the quantity of food consumed, exercise, and emotions should be entered manually. They lack automatic recognition of the type and quantity of food or activities. As a result, some advanced features exist in research papers but not in app stores and open-source apps, which may indicate that these features are still under research and development. Subsequently, the apps of phases 2 and 3 might need some improvement to keep pace with the advancement of research.


Clinostomum, some groups of these vertebrate parasites remain poorly studied. This is the case of species of the enigmatic genus Ithyoclinostomum Witenberg, 1926, until recently monotypic and restricted to South America, but with its occurrence expanded to North America after the description of I. yamagutii Rosser et al., 2020. Nevertheless, molecular data for the type species of the genus, Ithyoclinostomum dimorphum , is lacking so far. In the present study, large clinostomid metacercariae morphologically indistinguishable from I. dimorphum were obtained from two erythrinid fishes from the Rio Doce River, Southeast Brazil. Samples of the parasites were subjected to a multigene  molecular characterization followed by phylogenetic reconstructions. Phylogenies based on single-gene and concatenated datasets revealed unequivocally that I. dimorphum falls in a well-supported clade together with species of the genus Clinostomum. Moreover, the molecular divergences observed in relation to Clinostomum spp.  are compatible with a congeneric status with these species. Therefore, the genus Ithyoclinostomum is here synonymized with Clinostomum Leidy, 1856 and C. dimorphum  Braun, 1899 re-established. In the phylogenetic analysis, the recently described 'Ithyoclinostomum' yamagutii, presented as an isolated, independent lineage, showing significant molecular divergences to C. dimorphum . However, given the complex scenario raised in the morphology-based taxonomy of Clinostomidae, we took a conservative approach by not proposing a new genus to 'I.' yamagutii until molecular data of other clinostomid genus from birds, Clinostomatopsis, become available. Data here presented reveals that body size is not a useful criterion for higher-level classification in Clinostomidae. Finally, we highlighted the importance of the availability of molecular data for the type species of trematode genera proposed from South America to support a trans- or intercontinental distribution.Despite the recent advances raised in the molecular era to the taxonomic knowledge of species of the family Clinostomidae, especially those belonging to the specious genus   •One of the largest trematodes ever known was sequenced for the first time.•Clinostomum.Despite remarkable difference in body size, sequenced specimens belong to •Ithyoclinostomum is synonymized with Clinostomum and C. dimorphum re-established.•Data presented pave the way towards a more natural classification of Clinostomidae.•Sequences for type-species of trematode genera described from South America is needed. Since then, the finding of birds and fish harboring very large clinostomids identified as I. dimorphum became common in South America, and most reports were from Brazil  was one of the differential traits for including the specimens in the genus Ithyoclinostomum. Although robust phylogenetic data have revealed I. yamagutii as distinct from other clinostomid genera, the congeneric status with I. dimorphum still needs to be confirmed. Moreover, the systematic value of this impressive large-sized body, traditionally used in the clinostomid classification, remains an open question for investigation. In the present study, the phylogenetic position of I. dimorphum was evaluated for the first time and the results revealed it as a member of Clinostomum.Despite advances in the phylogenetic classification of trematodes, especially Clinostomidae, there are still no sequences available to 2Hoplias malabaricus (Bloch) and Hoplias intermedius (Günther), between December 2020 and July 2021. Metacercariae were found encysted and so mechanically excysted, ringed in physiological saline, gently compressed dorsoventrally between two glass slides, killed with hot water and fixed in 10% formalin. Subsequently, the helminths were stained with alum acetocarmine, dehydrated in ethanol series, diaphanized in beechwood creosote and mounted in Canada balsam.During a long-term project aimed at evaluating the helminth fauna of fish from the Rio Doce, in its portion in the state of Espírito Santo, Southeast Brazil, large clinostomid metacercariae were collected from cox1 (primers Dice-1 and Dice-11) were amplified by PCR, using the conditions previously described , were sequenced for the molecular study . DNA extescribed . PCR reaescribed . Chromatde novo were assembled into four alignments using default parameters of MAFFT algorithm (cox1), (ii) using only representatives with available sequences for all markers (concatenated dataset). Unreliable positions in the single-gene alignments were identified and removed using the Gblock web server  were found; no gaps were allowed in the Gblock settings for the concatenated dataset. Missing ends in the alignments were replaced with 'N's.The sequences generated lgorithm  implemenlgorithm  as follocox1 dataset, respectively (3 partitions); GTR+F+G4, K2P+I, TVM+F+G4, TVM+F+I+G4, TN+F+G4, K3Pu+F+G4, TPM2+F+G4, for the ITS1, 5.8S, ITS2, 28S, cox1 1st + 2nd + 3rd codon positions of the concatenated dataset (7 partitions). Outgroups were chosen based on previous phylogenetic studies (Phylogenetic reconstructions were performed with the maximum likelihood (ML) criterion using the evolutionary models implemented in ModelFinder  within I studies .p-distances were calculated using MEGA 7.0 (cox1)].The ML trees were generated via IQTREE and clade supports were estimated with 10,000 replicates of the ultrafast bootstrap UFBoot— and an SMEGA 7.0 . Newly g3I. dimorphum  comprising 798 positions in the final dataset. The concatenated alignment of the partitioned ITS + 28S + partitioned cox1 included 29 sequences (one as outgroup) comprising 2532 positions.The ribosomal cluster alignment (ITS1-5.8S-ITS2) included 33 sequences (two as outgroup) comprising 981 positions in the final dataset, while the other nuclear marker, 28S, included 30 sequences (one as outgroup) totaling 1245 positions. The mitochondrial Ithyoclinostomum dimorphum is deeply nested among the species-rich genus Clinostomum, clustering with the New World species but with an uncertain position in this clade. The only other species of Ithyoclinostomum known, I. yamagutii, appear in all tree reconstructions as an earlier diverging, independent lineage, yet its position in relation to other clinostomids varies among datasets. Whereas in the partitioned cox1 alone I. yamagutii is the earliest diverging taxon, in all remaining phylogenies it is placed as sister  of a large clade including representatives of Euclinostomum and Clinostosmum; in the latter topology, Odhneriotrema incommodum  appears as sister to all other clinostomid taxa. Regarding the classical, morphology-based subfamilial classification of Clinostomidae, the monophyly of Nephrocephalinae cannot be confidently assessed as sequences of Nephrocephalus spp. are missing, while the unique molecular signature of isolates of the monogeneric subfamily Euclinostominae, i.e, represented only by Euclinostomum, makes its monophyly plausible. Lastly, the reciprocal monophyly of Clinostominae and Ithyoclinostominae was not supported by any of the analyses with the nesting of I. dimorphum within Clinostomum. Additionally, cox1 data confirmed the conspecificity between metacercariae from different fish species .The trees resulting from concatenated  and the Ithyoclinostomum Witenberg, 1926 with Clinostomum Leidy, 1856 (ii) re-establishment of Clinostomum dimorphum , (iii) amendment of the genus Clinostomum, (iv) synonymization of Ithyoclinostominae Yamaguti, 1958 with Clinostominae Lühe, 1901. Such nomenclatural acts are formally proposed below.Phylogenetic results obtained in this study reveals the necessity of the following nomenclatural acts (i) synonymization of 3.1Subfamily Clinostominae Lühe, 1901..Clinostomum Leidy, 1856.Genus Diagnosis: As in Clinostomum dimorphum  Braun,1901.Ithyoclinostomum dimorphum). and Hoplias intermedius .Prevalence of infection: 1/1 (100%) for H. malabaricus, and 1/11 (9%) for H. intermedius.Intensity of infection: 5  and 1 (H. intermedius).Site of infection: encysted at gill, heart and pericardial cavity , and liver (H. intermedius).Locality: Doce River, municipalities of Colatina   and Baixo Guandu , State of Espirito Santo, Brazil.Representative DNA sequences: Two nearly complete sequences of the ribosomal gene cluster ITS1-5.8S-ITS2 ; a partial sequence of the 28S gene ; and two partial cox1 sequences .Voucher material: Four whole-mounted metacercariae (2 hologenophores and 2 paragenophores) deposited at the Collection of Trematodes of the Universidade Federal de Minas Gerais (UFMG-TRE 125–126).Clinostomoides brieni Dollfus, 1950 within Clinostomum; besides the large size, C. brieni further differs from the 'typical' Clinostomum species by having the genital pore post-testicular and the whole genital complex extremely close to the posterior end. These findings depict an opposing scenario to recent molecular phylogenetic studies  and C. dimorphum , the phylogenetic analyses herein presented revealed there is no support for these species be considered as belonging to distinct clinostomid genera. To the best of our knowledge, such an enormous difference among congeneric trematodes, has never been reported for any other genus of trematodes, making the quote "looks can deceiving" well appropriated to describe our findings.Data presented in this study reflects the complexity and problematic nature of the morphologically based classification involving clinostomids, complementing the recent study of  studies  that havstominae . HoweverIthyoclinostomum yamagutii presented as an early diverging and isolated lineage from other clinostomids, as previously shown by Ithyoclinostomum primarily based on: the large body size, the position of the cirrus-sac (pre-testicular), the testes shape (deeply lobed), the position of gonads in the posterior fourth of the body, and the large free area  between the ventral sucker and anterior testis; these features are found in the type species of the genus, Ithyoclinostomum dimorphum, now C. dimorphum. On the other hand, they assertively comment that obtaining molecular data from the type species of Ithyoclinostomum is crucial either to confirm or reject their hypothesis as well as to assess the interrelationships among other clinostomid genera, which was done in this study. The phylogenies and the high molecular divergences verified between C. dimorphum and I. yamagutii  reveal unequivocally these species are not congeneric. Based on the synonym between Clinostomum and Ithyoclinostomum required after our results, the taxon studied by Ithyoclinostomum [using quotation marks ('Ithyoclinostomum' or 'I.') to refer to the genus] until key taxa (see below) are sequenced and compared with available data , we chose not to erect a new genus for 'I.' yamagutii until molecular data on other clinostomid genera are available, especially for Clinostomatopsis sorbens , the type and only species of the genus Clinostomatopsis Dollfus, 1932 , the type species of the genus Odhneriotrema Travassos, 1928, described from Brazil. Thus, despite morphological similarities between these species, the possibility they correspond to distinct genera cannot be ruled out. Such delay in the generation of molecular data is also verified for the genus Nephrocephalus Odhner, 1902, found in African reptiles, which is the type genus of Nephrocephalinae Travassos, 1928. Therefore, the phylogenetic position of its members in relation to the other clinostomids is unknown. A similar scenario can be found even at the species level, which can be evidenced by the case of Clinostomum marginatum . Despite the fact that this species has been sequenced from isolates obtained from birds and fish in North America  still need to be sequenced to test the traditional morphology-based classification system. For instance, in reptile clinostomids, from USA . However America , its spe America .Overall, the taxonomic issues raised in this study highlight the importance of the availability of molecular data for the type species of trematode genera described from South America. Since the XIX century, dozens of trematode genera were proposed from this continent, initially from material collected in Brazil by the naturalist Johann Natterer and described by European helminthologists. Later in the XX century, renowned trematode taxonomists such as Szidat, Travassos, Thatcher, and their disciples described several other trematode genera, especially in Brazil and Argentina . UnfortuThe authors declare no conflicts of interest.


Pathogenic viral infections have become a serious public health issue worldwide. Viruses can infect all cell-based organisms and cause varying injuries and damage, resulting in diseases or even death. With the prevalence of highly pathogenic viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is urgent to develop efficient and safe approaches to inactivate pathogenic viruses. Traditional methods of inactivating pathogenic viruses are practical but have several limitations. Electromagnetic waves, with high penetration capacity, physical resonance, and non-contamination, have emerged as a potential strategy to inactivate pathogenic viruses and have attracted increasing attention. This paper reviews the recent literature on the effects of electromagnetic waves on pathogenic viruses and their mechanisms, as well as promising applications of electromagnetic waves to inactivate pathogenic viruses, to provide new ideas and methods for this inactivation. Many viruses are fast-spreading, long-lasting, and highly pathogenic, with the potential to cause global pandemics and severe human health hazards. Prevention, detection, inspection, elimination, and treatment are critical steps to block viral spreading. Pathogenic viruses’ rapid and effective elimination includes preventative, protective, and source-site elimination. Inactivating pathogenic viruses by physiological destruction to reduce their infective, pathogenic, and reproductive abilities is a powerful approach to their elimination. Traditional methods, including high temperatures, chemical agents, and ionizing radiation, can effectively inactivate pathogenic viruses. However, these methods remain subject to several limitations. Therefore, there is still an urgent need to develop innovative strategies to inactivate pathogenic viruses.Electromagnetic wave radiation has potential as a practical way to inactivate pathogenic viruses due to its high penetrating ability, rapid and homogeneous heating, resonance with microorganisms, and plasma release –3. The aThe morphological characteristics of viruses can reflect function, such as survival and infectious ability. It has been demonstrated that electromagnetic waves can disrupt the morphology of viruses, especially ultrahigh-frequency (UHF) and extremely high-frequency (EHF) electromagnetic waves.2 for 0.1 s. Large holes could be detected on the rough spherical envelope of the virus, which caused loss of their content. Electromagnetic wave exposure can be destructive to viral morphology. However, the changes in morphological properties, such as shape, diameter, and surface smoothness, after the exposure of a virus to electromagnetic radiation are not yet well understood. Therefore, it is important to analyze the relationship between the disruption of morphological features and functions, which might provide a valuable and convenient indicator for evaluating virus inactivation [Bacteriophage MS2 (MS2) is frequently used in a variety of research fields, such as the disinfection evaluation, kinetic modeling (aqueous), and biological characterization of viral molecules , 6. Wu fThe viral structure generally consists of the internal nucleic acid (RNA or DNA) and the external capsid. The nucleic acid determines the genetic and replication properties of the virus. The capsid, the outer layer of regularly arranged protein subunits, is the main scaffolding structure and an antigenic component of the viral particle and protects the nucleic acids. Most viruses have an outer envelope structure consisting of lipids and glycoproteins. Furthermore, envelope proteins determine receptor specificity and act as primary antigens, which the host immune system can recognize. An intact structure ensures the integrity and genetic stability of the virus.Studies have reported that electromagnetic waves, especially UHF electromagnetic waves, can destroy the RNA of pathogenic viruses. Wu  directlyRecent studies suggested that the effect of electromagnetic waves on pathogenic viral proteins is predominantly based on their indirect thermal effects on the medium and their indirect impact on protein synthesis by disrupting nucleic acids , 3, 8, 9The envelope of pathogenic viruses is closely associated with the infectious or pathogenic ability. Several studies have reported that UHF and super-high-frequency (SHF) electromagnetic waves can disrupt the envelope of pathogenic viruses. As described above, obvious holes could be detected on the viral envelope of coronavirus 229E after exposure to a 95 GHz millimeter wave at a power density between 70 and 100 W/cm² for 0.1 s . The resThe activity of a virus can be characterized by its ability to infect, replicate, transcribe, and so on. Viral infectivity or activity is usually evaluated by measuring viral titers using quantitative plaque assay analysis, median tissue culture infectious dose (TCID50), or luciferase reporter gene activity. However, it can also be directly evaluated by the isolation of live virus or the analysis of viral antigens, virus particles density, viral survival rate, and so on.It has been reported that UHF, SHF, and EHF electromagnetic waves can directly inactivate viral aerosols or water-borne viruses. Wu  exposed 2 for 2 s. There two pathogenic viruses were inactivated with efficiencies of 99.98% and 99.375%, respectively, which indicated that EHF electromagnetic waves had promise in the field of virus inactivation.Kaczmarczyk  first deThe efficiency of UHF electromagnetic wave-mediated virus inactivation was also evaluated on different media, such as breast milk and some materials commonly used in life. Researchers exposed anesthesia masks contaminated with adenovirus (ADV), poliovirus type 1 (PV-1), herpes virus 1 (HV-1), and rhinovirus (RHV) to electromagnetic radiation at 2450 MHz and 720 W. They reported that ADV and PV-1 antigen detection changed to negative, and the titers of HV-1, PIV-3, and RHV decreased to zero, indicating that all of the viruses were completely inactivated after exposure for more than 4 min , 16. Elh14 m− 3, generated by virus-susceptible Madin Darby canine kidney (MDCK) cells, was directly exposed to electromagnetic waves at 8 GHz and 820 W/m² for 15 min. The inactivation rate of the H3N2 virus was up to 100%. However, only 38% of the H3N2 virus was inactivated at the theoretical threshold of 82 W/m2, which indicated that the efficiency of electromagnetic radiation-mediated inactivation of the virus was closely related to the power density. Based on this study, Barbora [14 m− 3 SARS-CoV-2 virus particles to electromagnetic waves with frequencies of 10–17 GHz and a power density of 14.5 ± 1 W/m2 for approximately 15 min would result in 100% inactivation. A recent study by Wang [It is also noteworthy that Yang  predicte by Wang  clarifieIn summary, electromagnetic waves can affect virus activity both in aerosols and in suspensions, as well as on the surfaces of objects. The inactivation efficiency was found to be closely associated with the frequency and power of the electromagnetic waves and with the medium used for virus growth. Furthermore, physical resonance-based electromagnetic frequencies are prominent in the field of virus inactivation , 13. UntThe mechanisms underlying the inactivation of viruses by electromagnetic waves, which are closely related to the type of virus, the frequency and power of the electromagnetic waves, and the viral growth medium, are still largely unexplored. Recent studies have mainly focused on the mechanisms of thermal, nonthermal, and structural resonance energy transfer effects.The thermal effect refers to the temperature increase induced by the high-speed rotation, collision, and friction of polar molecules in tissues under electromagnetic waves. Due to this property, electromagnetic waves can raise the temperature of the virus beyond the physiological tolerance threshold, resulting in viral death. However, viruses contain few polar molecules, which suggests that direct thermal effects on viruses are rare . In compSeveral groups have reported that electromagnetic waves can reduce the infectivity of viruses through thermal effects , 3, 8. KThe above results indicated that the thermal effect of electromagnetic waves is a critical factor affecting the infectivity or activity of pathogenic viruses. Moreover, numerous studies have demonstrated that the thermal effect of electromagnetic exposure produces higher inactivation efficiency on pathogenic viruses than UV-C and conventional heating , 20–24.In addition to thermal effects, electromagnetic waves can alter the polarity of molecules, such as microbial proteins and nucleic acids, and cause the rotation and vibration of molecules, which results in reduced viability or even death . It is bThe nonthermal effects of electromagnetic waves on the inactivation of viruses are still controversial, but most studies find positive results , 25. We The resonance phenomenon refers to the tendency of a physical system to absorb more energy from its surroundings at its natural vibration frequency and wavelength. Resonance occurs in many parts of nature. Viruses are known to resonate in the confined-acoustic dipolar mode with microwaves of the same frequency, which is a resonance phenomenon , 13, 26.The physical resonance effect of electromagnetic waves has played a pivotal role in the inactivation of enveloped viruses, which are surrounded by a bilayer membrane embedded with viral proteins. Researchers found that the inactivation of H3N2 by electromagnetic waves with a frequency of 6 GHz and a power density of 486 W/m² was mainly caused by the physical rupture of the envelope through the resonance effect . The temCompared to the thermal effect of electromagnetic waves, the inactivation of viruses by physical resonance requires lower dose parameters, below the microwave safety standards set by the Institute of Electrical and Electronics Engineers (IEEE) , 13. TheElectromagnetic waves are highly effective against virus aerosols based on the realization of virus inactivation in the liquid phase and on the surfaces of different media , 26, whiCompared with traditional virus elimination technology, electromagnetic waves show the simplicity, high efficiency, practicality, and environmental friendliness of killing viruses with their own unique physical characteristics , 13. HowIn the future, the technologies of electromagnetic wave-mediated inactivation of viruses need further improvement. Basic science research should aim to clarify the mechanisms underlying the electromagnetic wave-mediated inactivation of viruses. For example, the mechanisms of energy utilization during the exposure of viruses to electromagnetic waves, the detailed mechanisms of nonthermal effects in the killing of pathogenic viruses, and the mechanisms of the SRET effect between electromagnetic waves and different types of viruses should be systematically clarified. Studies on application should focus on how to prevent the excessive absorption of radiation energy by polar molecules, explore the effects of electromagnetic waves at different frequencies on the various pathogenic viruses, and investigate the nonthermal effect of electromagnetic waves in the killing of pathogenic viruses.Electromagnetic waves have emerged as a promising approach for the inactivation of pathogenic viruses. Electromagnetic wave technology could overcome the limitations of traditional antivirus technology due to its excellent advantages, including low contamination, low cost, and high efficiency in inactivating pathogenic viruses. However, further investigations should be conducted to define the parameters of electromagnetic wave technology and to clarify the mechanisms of viral inactivation.Electromagnetic wave radiation at a specific dose can destroy the structure and activity of multiple pathogenic viruses. The efficiency of viral inactivation is closely associated with the frequency, power density, and exposure time. Moreover, the underlying mechanisms include thermal effects, nonthermal effects, and structural resonance energy transfer effects. Compared with traditional antiviral technologies, electromagnetic wave-based viral inactivation has several advantages, such as simplicity, high efficiency, and low pollution. Therefore, electromagnetic wave-mediated viral inactivation has emerged as a promising antiviral technology for future application.


Acromegaly is a systemic disease that requires multidisciplinary treatment to achieve the best clinical outcome. This study aimed to evaluate the outcomes of the endoscopic transsphenoidal approach (TSA) as the primary treatment for somatotroph adenomas and further investigate patients who had suboptimal surgical results. This retrospective study included 83 patients with somatotroph adenomas treated by TSA at our institution from 1999 to 2010. Biochemical remission was defined as hGH <1 and <2.5 ng/ml. Factors associated with failure of TSA and strategy of secondary treatments for refractory and recurrent disease were analyzed. The mean age of patients was 41.1 ± 11.3 years, and the mean follow-up time was 54.2 ± 44.3 months. Approximately 44.5% of patients had residual tumors after TSA. Larger tumor size, higher GH level before the operation, and the existence of residual tumors were associated with TSA failure. Forty-one patients had an inadequate response to TSA or a recurrent lesion, and of these patients, 37 had residual tumor after TSA. Octreotide results in good outcomes in the treatment of DGSA patients, and SRS/EXRT generates good results in treating patients who receive second treatments when remission cannot be reached 6 months after TSA operation. The pituitary gland can be considered the master hormone gland because it regulates most of the body’s hormone production, which in turn regulates overall metabolism . PituitaSomatotroph adenomas account for approximately 20% of pituitary tumors , 5. GrowBased on the pattern of hormone and transcription factor production, somatotroph adenoma can be divided into four subtypes. They are densely granulated somatotroph adenoma (DGSA), sparsely granulated somatotroph adenoma (SGSA), mammosomatotrophs, and mixed somatotroph–lactotroph tumors , 11. DGSAlthough radiosurgery techniques and chemotherapy have been refined in the past decades, surgical removal is the primary treatment for somatotroph adenomas , 13–16. Medical therapy of acromegaly includes taking somatostatin receptor ligands, cabergoline, and pegvisomant . SomatosThis study aims to evaluate the outcomes of TSA as the primary treatment for GH-secreting pituitary adenomas and to examine the responses of secondary treatments in patients who failed primary TSA operation and those with recurrent or refractory adenomas.All acromegalic patients were evaluated and treated by an acromegalic team composed of neurosurgeons, endocrinologists, radiologists, and radiation oncologists at our institution from 1999 to 2010. The records of patients with somatotroph adenomas treated by mono-nostril endoscopic transsphenoidal surgery, one kind of TSA, were retrospectively reviewed. This study was approved by the Institutional Review Board of our hospital (201901259B0), and because of the retrospective nature of the study, the requirement of informed patient consent was waived.The inclusion criteria were (1) somatotroph adenoma; (2) primary treatment with TSA; and (3) follow-up for more than 1 year. Patients were excluded if the primary treatment was a transcranial pituitary adenectomy and if follow-up was less than 1 year.All patients received preoperative magnetic resonance imaging (MRI) of the brain with a 1.5- or 3.0-Tesla system . Sagittal, coronal, and axial MR images, including T1- and T2-weighted imaging, were acquired for all patients. T1-weighted sequences were performed after intravenous injection of gadolinium at a dose of 0.1 mmol/kg. The diameter of the pituitary tumor was measured, and tumors were classified by size as microadenoma (<1 cm), macroadenoma (1–2 cm), or giant pituitary adenoma (>2 cm). The Knosp classification was used to evaluate parasellar extension and cavernous sinus invasion of the pituitary tumor on preoperative MR images . ImmediaSerum GH levels were measured by an immunoradiometric assay. A random GH level, also known as basal GH, was measured in the morning with patients fasting without any glucose load. All GH assays for the same patient were performed in the same manner throughout the follow-up period. Since GH level was measured without any glucose load, the expression level of IGF-1 was under full control of GH and was not included in our study.The remission criteria are a random GH <2.5 or <1.0 ng/ml at 6 months after TSA as other studies –28. BaseDuring the follow-up, the GH level was measured every 3 months. Tumor recurrence or refractory condition was defined as a nonremission GH level by 6 months after surgery. Patients who did not achieve remission were either observed or received secondary treatment. Secondary treatments included second TSA operation, 20 mg (maximum quantity) octreotide daily, TSA operation plus octreotide, Novalis stereotactic radiosurgery, or observation.Data extracted from the medical records included patient demographic information, GH level at diagnosis, preoperative GH level, tumor size, response to treatment, follow-up GH levels, and management of recurrence or inadequate response to the primary TSA operation.t-test. Data without normal distribution were presented as median and interquartile range (IQR) and performed by Wilcoxon rank-sum test. Categorical data were presented as numbers (percentage) and performed by the Chi-square or Fisher’s exact test, as appropriate. Variables including age, sex, tumor size, GH level at diagnosis, GH level at preoperation, residual tumor, and histology subtypes were analyzed with univariate and multivariate analyses. In the multivariate, Cox regression models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) of the probability for achieving GH <1 and <2.5 ng/ml. The time to achieving a fasting GH level <1 or <2.5 ng/ml was drawn as a cumulative incidence function (CIF) using the Kaplan–Meier method. Statistical analyses were performed with SAS version 9.4 software , and data were graphed using GraphPad Prism . Values of p < 0.05 were considered to indicate statistical significance.Continuous data with normal distribution were presented as mean ± standard deviation and performed by unpaired As shown in Histologically, 57 patients were categorized to DGSA, and the other 22 patients were SGSA. After the first TSA operation, 37 patients (44.6%) had a residual tumor, 19 patients (22.9%) received a secondary TSA operation, and 26 patients (31.3%) received medical treatment after the first TSA operation.Three patients died after the primary TSA operation. Two deaths were due to a central nervous system (CNS) infection with sepsis, and the others were due to upper gastrointestinal (UGI) bleeding. Morbidities after the first TSA operation included 24 cases of transient diabetes insipidus (DI) (resolved in 3 months), 3 CNS infections, 10 cases of hypogonadism, 4 cases of adrenal insufficiency, 4 cases of hypothyroidism, 7 cases of cerebrospinal fluid leakage, 3 cases of sinusitis, 3 cases of panhypopituitarism, and 1 patient who developed seizures Table 2.The figure of CIF shows about 40% and 83% of overall patients achieved GH level <2.5 ng/ml at 6 and 100 months after the operation, respectively . In the multivariate, no residual tumor is the only factor that has a significant effect for achieving GH <1 ng/ml .p = 0.003), presurgical tumor size , with DI syndrome (p = 0.036), and had histology subtype of SGSA (p = 0.005) were all significantly correlated with residual tumor , tumor size , and treatment  had significant effect for achieving GH <1 ng/ml. For achieving GH <2.5 ng/ml, age is the only factor with significance.Further analysis of the factors associated with remission at the final follow-up time of patients who failed the first TSA operation is shown in p = 0.025).We further investigated the association between histology subtypes and final remission after different treatments for patients who failed the first TSA operation Table 6.In this study, about 78.2% (61/78) of patients had somatotroph adenoma categorized in macroadenoma and giant pituitary adenoma. The Knosp classification of these somatotroph adenomas was grade 3 or 4. This indicates that they are hard to be removed by TSA completely. Micko et al. reported that the postoperational gross total resection ratios were 85%, 64%, and 0% and the remission rates within 2 years were 67%, 0%, and 0% for grades 3A, 3B, and 4, respectively . CompareThe results of this study showed that with a more stringent standard, which is GH level <1 ng/ml, about 28% of patients achieve this goal 6 months after TSA operation, and the number increased to nearly 59% at 100 months after receiving TSA. If the standard is set as GH level <2.5 ng/ml, about half of patients achieved this goal 6 months after the operation. This suggests that overall outcomes of TSA for the first TSA treatment of somatotroph adenomas are favorable. TSA has become the primary treatment for most cases of functioning pituitary adenomas , 5. In mFactors associated with remission at 6 months after TSA operation are lower GH level at diagnosis and preoperation, smaller tumor size, no residual tumor, and DGSA subtype. This seems obvious logically. The interesting part is at the time of the last follow-up, only age, residual tumor, and DGSA subtype are associated with remission.Recurrent disease, aggressive tumors, and those that do not respond to primary treatment can be difficult to treat , 19, 20.While TSA is considered a relatively safe procedure, it is not without complications. In this study, 3 patients died as a result of complications of the surgery. Two patients had a long history of diabetes and expired because of poor wound healing induced CSF leakage and severe CNS infection complicated with sepsis. Despite both of the patients receiving several sellar floor reconstructions including fat graft, naso-septal flap, lumbar CSF drainage, and bed rest. The other patient died due to stress ulcer-related GI bleeding after the surgery, despite the high-dose proton pump inhibitor and gastric endoscopic hemostasis procedure. Morbidities after the first operation included 24 cases of transient DI, 3 CNS infections, 10 cases of hypogonadism, 4 cases of adrenal insufficiency, 4 cases of hypothyroidism, 7 cases of CNS leakage, 3 cases of sinusitis, 3 cases of panhypopituitarism, and 1 patient who developed seizures. Not including the 3 deaths, the overall complication rate was 73.8% (59/80). Chowdhury et al.  prospectThere are limitations of this study. The study was retrospective, and the number of patients was relatively small. Moreover, the follow-up is not homogeneous. The time point of remission cannot be judged correctly. In the endocrinological assessment of GH, we only measured GH levels with patients fasting and did not perform an oral glucose tolerance test (OGTT) and IGF-1 level measurement. In the medical therapy, only octreotide, a first-generation somatostatin receptor ligand, was used. Since SGSA is not sensitive to this kind of compound, there will be little effect in the designed treatment. A study with more patients involved and applied subtype-specific therapeutic methods should be conducted in the future.TSA is a relatively safe and effective option for the primary treatment of GH-secreting pituitary adenomas. However, a large tumor size, high GH level at diagnosis and preoperation, and residual tumor are associated with a lower success rate. Octreotide results in good outcomes for DGSA patients, and SRS/EXRT generates good results in treating patients who receive secondary treatments when remission cannot be reached 6 months after TSA operation.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.This study was approved by the Institutional Review Board of our hospital (201901259B0). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.J-LY: conception and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, final approval of the manuscript, guarantor of integrity of the entire study, statistical analysis, definition of intellectual content, literature research, and clinical studies. M-YC: acquisition of data, analysis and interpretation of data, drafting of the manuscript, final approval of the manuscript, definition of intellectual content, and literature research. Y-LC: conception and design, acquisition of data, drafting of the manuscript, final approval of the manuscript, and clinical studies. P-WH: conception and design, acquisition of data, drafting of the manuscript, final approval of the manuscript, clinical studies, and Supervision. P-WH: conception and design, acquisition of data, drafting of the manuscript, final approval of the manuscript, and clinical studies. K-CW: conception and design, acquisition of data, drafting of the manuscript, final approval of the manuscript, and clinical studies. C-NC: conception and design, acquisition of data, drafting of the manuscript, final approval of the manuscript, clinical studies, supervision, and guarantor of integrity of the entire study.This research was funded by grants from Keelung Chang Gung Memorial Hospital .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.Alzheimer’s disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1–5% of the total number of cases. It is categorized as early onset  and presents genetic mutations in  Alzheimer’s disease (AD) is a neurodegenerative disease and represents the most common form of dementia  . At presAD is characterized by chronic and acquired memory impairment and cognitive deficits in domains, such as language, spatio-temporal orientation, and executive capacity, as well as behavior alterations, all of which lead to progressive loss of personal autonomy . Histopapresenilin 1 gene , identified in up to 70% of cases with familial AD; the presenilin 2 gene  and the Amyloid precursor protein gene . The sporadic presentation is late-onset (LOAD) and occurs in individuals older than 65 years of age. The main risk factor is considered to be age [APOE, 19q13.32) [Two forms of AD have been characterized : familiao be age ,12, but o be age ,13 and ge PSEN1, q24.2, idMultiple hypotheses have been formulated to explain the development of AD, including the amyloidogenic cascade, tauopathy, vascular theory, oxidative stress, neuroinflammation, and bacterial infections theory, among others ,11,15. HRecently, genome-wide association studies (GWAS) have allowed the identification of several genes associated with AD; however, the relationship of many loci to the risk of developing AD has not been elucidated . For thiPSEN1, PSEN2, and APP, which interfere with the physiological processing of the Aβ peptide [AD is a complex and multigenic disease. Familial AD is associated with the genes that encode  peptide .PSEN1 gene and 80 mutations (19 pathogenic) of the PSEN2 gene have been identified [PSEN genes includes missense mutations, small insertions, deletions, and genomic deletions, specifically in PSEN1 [PSEN1 mutations cause the most severe forms of AD with complete penetrance, and the onset of the disease can occur as early as 25 years of age. Missense mutations in the PSEN2 gene may show incomplete penetrance, and carriers show an older age of disease onset than those with PSEN1 mutations [More than 300 mutations (221 pathogenic) of the entified . These min PSEN1 . PSEN1 mAPP gene encoding this protein consists of a total of 17 exons and encodes several isoforms resulting from the alternative splicing of exons 7 and 8 [APP gene cause an increase in the Aβ42/Aβ40 ratio, as well as an increase in levels of total Tau and phosphorylated Tau in neurons [APP, which has also been observed in cases of familial AD [The  7 and 8 , three o 7 and 8 . The las 7 and 8 . Interes 7 and 8 ,20; the  neurons ,25. Most neurons ; howeverilial AD ,28, doesAPOE, which increases the risk for AD three- to eightfold [On the other hand, the main gene that is consistently associated with sporadic AD is ightfold ,29:APOE gene consist of alleles that translate the isoforms of the protein that differ only by a single amino acid substituted at positions 112 and 158: (i) ApoE-ε2 isoform (Cis112Cis158) is associated with the presentation of hyperlipoproteinemia type III; (ii) ApoE-ε4 isoform (Arg112Arg158) has been implicated in atherosclerosis [APOE3ch variant may have a regionally specific role in modifying the effect of Tau and thus in the severity, progression, and clinical presentation of AD [APOE polymorphisms would also affect Tau protein processing and strongly predispose one to AD.The physiological isoform, or ApoE-ε3 Cis112Arg158), is present in 50 to 90% of the healthy population. The main polymorphisms of the clerosis ,31, and 12Arg158,clerosis ,9. Growiclerosis . For exaon of AD . This suA wide variety of genes in the development of AD have been identified as possible early biomarkers and therapeutic targets. Together, these genes could explain up to 70% of LOAD cases and point to new disease-related phenomena , such asdisintegrin and metalloproteinase domain-containing protein 10 (ADAM10), which is the most important α-secretase in the brain that could cause increases in the Aβ42/Aβ40 ratio [phosphatidylinositol-binding clathrin assembly protein (PICALM) initiates the polymerization of clathrin, which contributes to NP formation [clustered mitochondria protein homolog (CLU), a chaperone that prevents the aggregation of foreign proteins, has been found to inhibit the formation of amyloid fibrils by APP and other genes involved [According to the amyloid hypothesis, a wide variety of genes identified are involved in pathological Aβ processing , such as40 ratio ,34,35. Iormation ,37,38; cinvolved ,40,41.transcription factor PU.1 (SPI1), involved in the expression of immune-related genes in myeloid cells, could contribute to AD by regulating key pathways associated with the immune response and altering the epigenetic landscape [myeloid cell surface antigen CD33, expressed on the surface of microglial cells, is involved in the negative regulation of cytokine production and, therefore, could play a role in AD by modulating microglial activation [bridging integrator 1 (BIN1), which participates in membrane tubulation through an interaction with microtubule-associated proteins, such as Tau, and other functions, such as endocytosis and intracellular endosome trafficking [Triggering receptor expressed on myeloid cells 2 (TREM2) has been associated with cerebrospinal fluid Tau levels [CD2-associated protein (CD2AP), which is involved in intracellular trafficking and cytoskeletal reorganization, has also been related to Tau-induced neurotoxicity, abnormal neurite structure modulation, and BBB disruption [However, the pathological Aβ processing is not the only mechanism involved, and it does not explain the complexity of AD. More than 29 risk loci have been identified, as well as more than 215 potential causative genes. These genes are strongly expressed in immune-related tissues and cell types, including the spleen, liver, and microglia cells . In thisandscape ,48. Furttivation ,50. In tu levels . Likewissruption .paired immunoglobulin-like type 2 receptor alpha (PILRA), an inhibitory immunoglobulin receptor involved in the regulation of the immune system and a co-receptor of herpes simplex virus type 1 (HSV-1), is related to dysfunctions in microglial activation and may represent a link to infectious factors that have been connected to AD [APO-ε4 has also been associated with HSV-1 infections [Similar to the immunomodulation hypothesis, there is the infectious one, which could also explain the pathological processing of both Tau and Aβ. The infectious hypothesis proposes that a pathogen  triggers an inflammatory response and promotes the aggregation of Tau and Aβ, which in turn can cause further inflammation . In thised to AD ,54,55. Lfections . This issolute carrier family 10 member 2 (SLC10A2) plays an important role in the regulation of cholesterol metabolism, and it has been shown that cholesterol accumulation in neurons leads to neuronal death, memory impairment, and increased Aβ generation [zinc finger CW-type PWWP domain protein 1 (ZCWPW1), a protein involved in the positive regulation of the DNA metabolic process, could decrease the risk of LOAD by suppressing insulin resistance [methylenetetrahydrofolate reductase (MTHFR), a rate-limiting enzyme in the methyl cycle, can be linked, as high levels of homocysteine have been found in AD patients. Homocysteine is associated with vascular damage, increased inflammation, and endothelial cell dysfunction [In addition, the pathological processing of Tau and Aβ, such as other neuropathological events, could be triggered by some genes involved in metabolic disorders ; mainly neration ,59. Sincneration , it is psistance . Furthersistance , autophasistance , and altsistance . Correspfunction . TherefoCD33, DSG2, MEF2C, MINK1, PTK2B, CD2AP, PICALM, BIN1, MTHFR, PLD3, and TOMM40 are highly expressed in the hippocampus. They are related to various pathological processes  and their objective is to associate certain genes with multiple pathologies or disorders ,146. UntSince genetic variations have been evidenced between different ethnic groups , it is eOn the other hand, GWAS have allowed the identification of genetic components that are not related to the neuropathological processes mentioned above , affectiAlthough GWAS have been invaluable in identifying candidate genetic variants associated with the disease, the AD risk loci identified to date explain only a small fraction of the heritability of AD ; therefoHowever, the increase in sample size is due to the use of a Russian doll-like design where larger studies include all smaller studies , which cTherefore, future GWAS must adopt a better definition of cases and controls, as well as designs based on the comparison of ethnic groups, sex, and age, since there may be various environmental, exposure, and genetic factors that modify the risk load conferred by each locus. Likewise, these associations should be replicated and validated in multiple populations and followed by a downstream functional dissection to benefit knowledge of pathophysiology .ABCA7, BIN1, SORL1, and SLC24A4  theDespite these limitations, as has been analyzed throughout the manuscript, GWAS have multiple benefits given their diverse clinical applications and allow the identification of new biological mechanisms as well as ethnic variations in the health-disease process. Similarly, GWAS should be promoted in understudied populations, with a different methodological and study design, larger sample sizes, with more clinical data, and extrapolating the associations identified to in silico and preclinical models to take full advantage of the potential this technology offers us.Notwithstanding the significant advances in our understanding of AD pathobiology through GWAS technology, we have yet to identify a disease-modifying therapy that has demonstrated efficacy in humans. Most of the research conducted over the past 30 years has focused on anti-Aβ therapies ; howeverPICALM, CLU, and CD2AP, among others (The multiple genes identified by GWAS support the various theories that have been postulated to explain the development of AD, some of them involved in more than one mechanism, such as g others . This suOn the other hand, it should be noted that genetics is only one factor that can influence the treatment of AD, since other factors, such as age, general state of health, and stage of the disease, can also influence the efficacy of the treatment. Similarly, the use of more general approaches to prevent or delay the onset of AD, such as lifestyle interventions or changes to prevent or delay the onset of AD , could bIn conclusion, this review shows the substantial role that genetics plays in the development of not only EOAD, but also LOAD. The GWAS and other genetic analyses will facilitate the identification of possible biomarkers that allow an early diagnosis of the disease or the risk of developing it in susceptible people, as well as its relationship with other pathological processes. Furthermore, post-GWAS functional experiments can elucidate new targets and avenues for therapeutic intervention. Therefore, the selection of genetically supported drug targets could reduce time and costs and improve the success rate of new drug development.