Hasan Alkahtani, Theyazn H. H. Aldhyani, M. Alzahrani
In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.
{"title":"Early Screening of Autism Spectrum Disorder Diagnoses of Children Using Artificial Intelligence","authors":"Hasan Alkahtani, Theyazn H. H. Aldhyani, M. Alzahrani","doi":"10.57197/jdr-2023-0004","DOIUrl":"https://doi.org/10.57197/jdr-2023-0004","url":null,"abstract":"In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90683478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Maddeh, S. Ayouni, Shaha T. Al-Otaibi, M. Alazzam, Nazik Alturki, Fahima Hajjej
A growing number of feature learning methods, particularly those based on deep learning, have been investigated to derive useful feature representations from large quantities of data. However, applying each model in real time for various research requirements can be challenging. With the common use of smartphones equipped with sensors, ensemble learning has become an area of interest among researchers. By obtaining knowledge of a patient’s mobility, a wide range of services can be provided. Therefore, in this research work, the authors endeavor to detect a patient’s state using sensors attached to the patient’s smartbed. The authors specifically create an ensemble network for greater precision and improved accuracy. This paper is based on using ensemble learning techniques to determine a patient’s state of mobility, and data are gathered from integrated devices in the smartbed. In this study, the authors use ensemble learning to distinguish between various forms of transit, including sleeping, standing, sitting, walking, and emergency states. The authors propose an ensemble network model based on deep learning to enhance the performance and resolve issues that may arise in a singular network. The characteristics generated by the neural networks are merged and relearned in this model. The data used in the trials are taken from the sensors attached to the patient and their smartbed.
{"title":"Ensemble Learning-based Smartbed System for Enhanced Patient Care","authors":"Mohamed Maddeh, S. Ayouni, Shaha T. Al-Otaibi, M. Alazzam, Nazik Alturki, Fahima Hajjej","doi":"10.57197/jdr-2023-0003","DOIUrl":"https://doi.org/10.57197/jdr-2023-0003","url":null,"abstract":"A growing number of feature learning methods, particularly those based on deep learning, have been investigated to derive useful feature representations from large quantities of data. However, applying each model in real time for various research requirements can be challenging. With the common use of smartphones equipped with sensors, ensemble learning has become an area of interest among researchers. By obtaining knowledge of a patient’s mobility, a wide range of services can be provided. Therefore, in this research work, the authors endeavor to detect a patient’s state using sensors attached to the patient’s smartbed. The authors specifically create an ensemble network for greater precision and improved accuracy. This paper is based on using ensemble learning techniques to determine a patient’s state of mobility, and data are gathered from integrated devices in the smartbed. In this study, the authors use ensemble learning to distinguish between various forms of transit, including sleeping, standing, sitting, walking, and emergency states. The authors propose an ensemble network model based on deep learning to enhance the performance and resolve issues that may arise in a singular network. The characteristics generated by the neural networks are merged and relearned in this model. The data used in the trials are taken from the sensors attached to the patient and their smartbed.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77762122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Fouly, I. Alnaser, Abdulaziz K. Assaifan, H. S. Abdo
In the context of replacing damaged artificial hip joints, a common practice involves using antibiotic-infused bone cement as a spacer. However, the mechanical properties of polymethyl methacrylate (PMMA), which is commonly used for spacers, have certain limitations. To address this issue, the present study suggests incorporating a natural filler, specifically coffee husk, as a reinforcement for PMMA. Different composite samples were prepared by varying the weight fractions of coffee husk, and their mechanical properties were assessed. The results indicated that the inclusion of coffee husk particles in PMMA led to improvements in compressive strength, hardness, and stiffness. Furthermore, a finite element model was constructed and analyzed to evaluate the stress experienced on the spacer’s surface (load-carrying capacity), yielding findings consistent with the experimental results.
{"title":"Creating Customized Hip-Spacers Using PMMA-Based Green Composites to Fulfill Specific Needs of Individuals with Disabilities","authors":"A. Fouly, I. Alnaser, Abdulaziz K. Assaifan, H. S. Abdo","doi":"10.57197/jdr-2023-0008","DOIUrl":"https://doi.org/10.57197/jdr-2023-0008","url":null,"abstract":"In the context of replacing damaged artificial hip joints, a common practice involves using antibiotic-infused bone cement as a spacer. However, the mechanical properties of polymethyl methacrylate (PMMA), which is commonly used for spacers, have certain limitations. To address this issue, the present study suggests incorporating a natural filler, specifically coffee husk, as a reinforcement for PMMA. Different composite samples were prepared by varying the weight fractions of coffee husk, and their mechanical properties were assessed. The results indicated that the inclusion of coffee husk particles in PMMA led to improvements in compressive strength, hardness, and stiffness. Furthermore, a finite element model was constructed and analyzed to evaluate the stress experienced on the spacer’s surface (load-carrying capacity), yielding findings consistent with the experimental results.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82721022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Alabdulkreem, Radwa Marzouk, Mesfer Alduhayyem, M. Al-Hagery, Abdelwahed Motwakel, M. A. Hamza
Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.
{"title":"Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People","authors":"E. Alabdulkreem, Radwa Marzouk, Mesfer Alduhayyem, M. Al-Hagery, Abdelwahed Motwakel, M. A. Hamza","doi":"10.57197/jdr-2023-0020","DOIUrl":"https://doi.org/10.57197/jdr-2023-0020","url":null,"abstract":"Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82483099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Maashi, M. Al-Hagery, Mohammed Rizwanullah, A. Osman
Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.
{"title":"Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data","authors":"M. Maashi, M. Al-Hagery, Mohammed Rizwanullah, A. Osman","doi":"10.57197/jdr-2023-0019","DOIUrl":"https://doi.org/10.57197/jdr-2023-0019","url":null,"abstract":"Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90878318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Support organised through a personal budget aims to promote people’s choices in how they arrange their support. Participation in choices of people who use little or no verbal speech to express themselves requires that support workers use personalised communication. This article explores how support workers use personalised communication to prioritise the choices of people with intellectual disabilities about organising support through a personal budget. It applies Gormley and Fager’s framework of dimensions for personalising communication to analyse ethnographic data from four people with intellectual disabilities using personal budgets and their support workers. The analysis found that workers promoted people’s participation in choices about their support when they focused on how people preferred to express themselves. Support practice, policy and research that target people’s communication preferences in making support arrangements can have direct impact on their satisfaction with the arrangements and the quality of their personalised support.
{"title":"Personalising Support through Communication between People with Intellectual Disabilities and their Support Workers","authors":"Deborah Luise Lutz, Karen Raewyn Fisher","doi":"10.16993/sjdr.980","DOIUrl":"https://doi.org/10.16993/sjdr.980","url":null,"abstract":"Support organised through a personal budget aims to promote people’s choices in how they arrange their support. Participation in choices of people who use little or no verbal speech to express themselves requires that support workers use personalised communication. This article explores how support workers use personalised communication to prioritise the choices of people with intellectual disabilities about organising support through a personal budget. It applies Gormley and Fager’s framework of dimensions for personalising communication to analyse ethnographic data from four people with intellectual disabilities using personal budgets and their support workers. The analysis found that workers promoted people’s participation in choices about their support when they focused on how people preferred to express themselves. Support practice, policy and research that target people’s communication preferences in making support arrangements can have direct impact on their satisfaction with the arrangements and the quality of their personalised support.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134889341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"‘I Dare to Be Myself.’ The Value of Peer Communities in Adapted Physical Activity Interventions for Young People and Adults with Cerebral Palsy","authors":"Mie M. Andersen, H. Winther","doi":"10.16993/sjdr.806","DOIUrl":"https://doi.org/10.16993/sjdr.806","url":null,"abstract":"","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"129 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67469433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Minimising Restrictive Interventions for People with an Intellectual Disability: Documentary Analysis of Decisions to Reduce Coercion in Norway","authors":"Monica Røstad, R. Whittington, E. Søndenaa","doi":"10.16993/sjdr.984","DOIUrl":"https://doi.org/10.16993/sjdr.984","url":null,"abstract":"","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67470737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many people with intellectual disabilities in Norway attend municipal day centers where they engage in activities and work-tasks with support from staff. The purpose of day centers is to offer meaningful activities for individuals who are not included in ordinary work. Little research has been done on day centers, and we have limited knowledge of which social and cultural norms apply in such a sheltered context. This article focuses on how employees facilitated the participation of workers with intellectual disabilities through social support and in interaction. This study has a qualitative ethnographic design. Data were collected through participatory observation and interviews and analyzed thematically. We found that the participants alternated between roles and frames of interaction: a work frame and a care frame. Each frame had different norms for interaction and role performance. This study adds to our knowledge about day centers for people with intellectual disabilities
{"title":"Exploring Day Center Activities in Norway: How do Employees Facilitate Participation for Workers with Intellectual Disabilities through Interaction and Social Support? An Ethnographic Study","authors":"Lise Ellingsen Langemyhr, H. Hem, Heidi Haukelien","doi":"10.16993/sjdr.986","DOIUrl":"https://doi.org/10.16993/sjdr.986","url":null,"abstract":"Many people with intellectual disabilities in Norway attend municipal day centers where they engage in activities and work-tasks with support from staff. The purpose of day centers is to offer meaningful activities for individuals who are not included in ordinary work. Little research has been done on day centers, and we have limited knowledge of which social and cultural norms apply in such a sheltered context. This article focuses on how employees facilitated the participation of workers with intellectual disabilities through social support and in interaction. This study has a qualitative ethnographic design. Data were collected through participatory observation and interviews and analyzed thematically. We found that the participants alternated between roles and frames of interaction: a work frame and a care frame. Each frame had different norms for interaction and role performance. This study adds to our knowledge about day centers for people with intellectual disabilities","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67470784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tayyaba Afsar, Ahmed Waqas, A. Nayab, S. Abbas, Arif Mahmood, Muhammad Umair, S. Razak
A genetically diverse condition, maturity-onset diabetes of the young (MODY), frequently develops before the age of 25. MODY is caused by disease-causing sequence variations in the PAX4 gene, which is found on chromosome 7q32.1. Additionally, it has also been observed that variants in PAX4 have also been associated with neurodevelopmental disability. Whole exome sequencing (WES) followed by Sanger sequencing was performed for all the available affected and unaffected members of the family. Data analysis revealed a novel heterozygous nonsense variant (c.61C>T; p.Gln21*) in the PAX4 gene in the affected individuals, which segregated perfectly with the disease phenotype. The present study adds to the PAX4 mutation spectrum and reports on the first case of MODY associated with neurodevelopmental disorders in humans.
{"title":"A Novel Variant in the PAX4 Gene Causes Maturity-Onset Diabetes of the Young (MODY), Type IX with Neurodevelopmental Disorder","authors":"Tayyaba Afsar, Ahmed Waqas, A. Nayab, S. Abbas, Arif Mahmood, Muhammad Umair, S. Razak","doi":"10.57197/jdr-2023-0018","DOIUrl":"https://doi.org/10.57197/jdr-2023-0018","url":null,"abstract":"A genetically diverse condition, maturity-onset diabetes of the young (MODY), frequently develops before the age of 25. MODY is caused by disease-causing sequence variations in the PAX4 gene, which is found on chromosome 7q32.1. Additionally, it has also been observed that variants in PAX4 have also been associated with neurodevelopmental disability. Whole exome sequencing (WES) followed by Sanger sequencing was performed for all the available affected and unaffected members of the family. Data analysis revealed a novel heterozygous nonsense variant (c.61C>T; p.Gln21*) in the PAX4 gene in the affected individuals, which segregated perfectly with the disease phenotype. The present study adds to the PAX4 mutation spectrum and reports on the first case of MODY associated with neurodevelopmental disorders in humans.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74174783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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