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Explainable Cluster-Based Predictive Framework for Early Diagnosis of Autism Spectrum Disorder Using Behavioral Biomarkers
No full textBackground/Objectives: Autism Spectrum Disorder (ASD) is a multifaceted neuropsychiatric condition characterized by early behavioral irregularities that often precede formal diagnosis. Timely and precise detection remains a major clinical challenge due to the complexity of behavioral manifestations and the limited accessibility of diagnostic resources. Methods: In this study, we present an explainable machine learning framework for the early diagnosis of ASD using behavioral biomarkers derived from toddler screening data. The framework integrates unsupervised learning (DBSCAN and K-means clustering) to identify latent behavioral patterns, followed by predictive modeling using logistic regression (LR), random forest (RF), and support vector machine (SVM). To ensure transparency and clinical interpretability, a SHAP (SHapley Additive exPlanations) analysis is employed to quantify the contribution of each behavioral feature to the model's predictions. Results: Experimental evaluations reveal that the RF model achieves the highest accuracy (98.85%), followed by SVM (97.70%) and LR (90.53%). The explainability results highlight meaningful and clinically relevant behavioral indicators associated with ASD risk. Conclusions: The proposed framework not only enhances diagnostic accuracy but also promotes interpretable AI for real-world integration into neuropsychiatric assessment pipelines
AI-embedded IoT healthcare optimization with trust-aware mobile edge computing:AI-Embedded IoT Healthcare Optimization..
No full textEmbedded technologies combined with the Internet of Things (IoT), have transformed healthcare monitoring systems into automated and responsive platforms. In recent decades, many existing approaches have been based on edge computing to reduce response time in patient monitoring and provide a reliable method for interaction among the medical team and experts during disease diagnosis. Such approaches are the interconnection of battery-powered devices and physical objects to capture the physiological data streams for medical treatment and facilitate personalized healthcare systems. However, as wireless devices have limited resources for fulfilling end-user requests, this affects the accuracy of the medical system, especially in the presence of malicious devices on the communication infrastructure. Under diverse network conditions, such solutions lower the reliability level of the devices and increase the likelihood of suspicious processes. Therefore, to keep these significant concerns in IoT-based healthcare applications, trust and security should be adopted while collecting patients' data over an insecure medium. In this research study, we propose a model referred to as Edge-Cloud Trusted Intelligence (ECTI), aiming to decrease the computing overhead on the devices. Additionally, multi-level security is implemented to ensure privacy preservation by adopting trusted behavior when communicating in a distributed environment. The edges utilize resources efficiently by employing task offloading strategies, enabling lightweight collaborative decision-making for routing in the healthcare domain. The performance results revealed notable improvement of the proposed model against related schemes in terms of various network metrics. [Abstract copyright: © 2025. The Author(s).
Personalized Eicosapentaenoic Acid Therapy for Clinical Depression
Get PDF© 2025, [Slack]. The attached document (embargoed until 26/02/2026) is an author produced version of a paper published in Psychiatric Annals uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link [https://doi.org/10.3928/00485713-20250114-03]. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
From radiomics to transformers in pancreatic cancer detection and prognosis
Get PDFIntroductionPancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, primarily due to late diagnosis and poor therapeutic response. Advances in artificial intelligence (AI), particularly in medical imaging and multi-modal data integration, have created new opportunities for improving early detection and personalized prognostication.MethodsThis systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The protocol was prospectively registered with the Open Science Framework, covering studies published between 2015 and 2025.ResultsDistinct from prior surveys that focus narrowly on specific algorithms or data types, this work introduces a generational taxonomy of AI approaches-ranging from classical radiomics-based machine learning to deep learning and contemporary transformer-based models-and maps their application to core clinical tasks such as detection, segmentation, classification, and outcome prediction. A key contribution is the integration of diverse datasets across imaging, pathology, and molecular sources; we further assess trends in availability, usage, and sample scale.DiscussionWe critically evaluate limitations in generalizability, external validation, model calibration, and translational readiness, and outline recommendations for multi-center validation, standardized reporting, domain adaptation, and clinician-centered interpretability.Systematic review registrationhttps://doi.org/10.17605/OSF.IO/2DVHJ
Gospel, Liberation and Pluralism in Latin America
No full text© 2024, [SAGE Publications Limited]. This is an author produced version of a paper published in Transformation: An International Journal of Holistic Mission Studies uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it. Seeing the world through the eyes of the marginalised has been a consistent theme in modern Latin American theology. When Latin American Liberation Theology first emerged, evangelical theologian René Padilla expressed a shared concern over the problem of poverty. But he also warned that certain elements therein clashed with evangelical hermeneutics. Consequently, he called for a circular approach that would maintain a tension between the crisis of the moment and the authority of biblical revelation. In the 21st century, liberation discourse has expanded into postcolonial critique on indigenous culture and identity, including a stream known as Liberation Pluralism. Evangelicals share many of the same concerns. But as they engage with the discourse, there is an ongoing need to heed Padilla’s warning. This article argues that evangelicals today can engage in postcolonial and liberation discourses, but that this should be carried out with that same circular hermeneutic which Padilla first proposed
Exploring the Student Experience through a New Theoretical Lens in Mathematics and Game-Based Learning
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Edge-Driven Disability Detection and Outcome Measurement in IoMT Healthcare for Assistive Technology
No full textThe integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility
Ethnic differences in adipose tissue dysfunction and insulin resistance: a scoping review
No full textThe objective of this scoping review is to synthesize ethnic comparison studies focused on characteristics of adipose tissue dysfunction including ectopic fat, adipokines and insulin resistance in populations of south Asian (SA), black (BA) and white (WE) ethnicity. A search of the literature was conducted on MEDLINE using keywords for adipose tissue dysfunction and ethnicity. Studies were included if they compared ectopic fat in adults (>18-years) of SA, BA or WE ethnicity, with data on insulin sensitivity and adipokines extracted where present. Thirty-one studies were included in this review. Trends showed most studies were conducted in USA (n = 22); BA were the focus of most ethnic comparison studies (n = 28). Most studies focused on intrahepatic lipids (n = 26), with fewer investigating intrapancreatic lipids (n = 3) and intramyocellular lipids (n = 8). Only 2 studies investigated leptin and adiponectin alongside ectopic fat deposition by ethnicity. Current trends indicate intrahepatic lipid is lower in BA but greater in SA compared to WE populations, indicting possible ethnic disparities in the role ATD in the development of T2D. Few ethnicity studies have investigated multiple characteristics of ATD between BA and SA groups in a single study which may be needed to elucidate ethnic-specific pathophysiology of T2D. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.
Personal Journeys of Transition Beyond the Care System in England: Voices of Care-Experienced Young People from the I-CAN Programme
No full textCare-experienced young people often face considerable challenges due to a personal history of trauma and disruption and have a higher risk of homelessness, mental ill health, and involvement with the criminal justice system. A stubborn trend of achieving fewer qualifications than non-care-experienced peers persists, with greater likelihood of becoming NEET (Not in Education, Employment or Training). Accessible and sustainable pre-employment programmes should be a priority for national initiatives designed to generate improved outcomes for vulnerable youth. The I-CAN (Initiating and Supporting Care Leavers intoApprenticeships in Nursing) programme offers young people in England (aged 18–30) a person-focussed pathway to training and employment. However, robust research is needed to evidence the effectiveness of this type of small-scale and short-term funded programme. The current paper reports qualitative findings from a pilot study exploring the perceptions and experiences of (N = 27) young people who attended the 8-week I-CAN programme delivered at a Higher Education Institution. Data were collected from four focus groups and thematically analysed. The findings captured young people’s personal trajectoriesand exposed underpinning processes as well as unique, shared, and intersectional factors that can either facilitate or impede progression to education, employment and training. Crucially, care-experienced young people are not a homogenous group and capturing their authentic, diverse voices in evaluation research is essential for not only assessing if a programme works but for whom, and why. Furthermore, findings can help to inform meaningful strategies and socially valid interventions to support care-experienced young people navigate the transition ‘cliff edge’