[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119092-en":3,"doc-seo-119092-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119092,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning-Assisted 3D Flexible Organic Transistor for High Accuracy Metabolites Analysis and Clinical Applications","Advanced diagnostic technologies are vital for accurate and efficient detection and management of metabolic disease. This paper introduces a machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensing strategy for high-accuracy metabolite analysis and related clinical use. Machine learning enhances analytical capability by processing complex sensor data, extracting patterns, and producing 100% high-accuracy diagnostic predictions. The study covers device fabrication and operation, the ML role in metabolite analysis, and performance validation using practical human blood samples, including hypernatremia syndrome, supporting more personalized diagnostics.","Machine Learning-Assisted 3D Flexible Organic Transistor for  \nHigh Accuracy Metabolites Analysis and other Clinical Applications  \nCaizhi Liao *1,2, Huaxing, Wu2, and Luigi G. Occhipinti1*  \n1. Department of Engineering, The University of Cambridge, Cambridge, UK  \n2. Department of Bioengineering, Sun Yat sen University, Guangzhou, China  \nCorresponding author  \nCaizhi Liao, Email: [cl2006@cam.ac.uk](cl2006@cam.ac.uk)  \nLuigi G. Occhipinti, Email: [lgo23@cam.ac.uk](lgo23@cam.ac.uk)  \nAbstract  \nThe integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100 % high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potentials for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.  \nKey Words: Machine Learning (ML), Fiber-based Organic Transistors (FOTs), Metabolic Analysis, Electrolyte Ions, Clinical Diagnostics  \n1. Introduction  \nThe precise and efficient diagnosis of metabolic disorders is pivotal in clinical medicine, as these conditions often have profound impacts on a patient’s overall health and wellbeing[1-3] . Metabolic ions, such as hydrogen ion (H+), sodium (Na+), potassium (K+), calcium (Ca2+), and magnesium (Mg2+) play critical roles in maintaining cellular function, signal transduction, and homeostasis[4-7] . Imbalances in these ions can indicate the occurrence of various metabolic disorders, including but not limited to, electrolyte imbalances, renal dysfunction, endocrine disorders, and acid-base disturbances. Traditional methods for diagnosing these metabolic ion imbalances often involve a combination of blood tests, urine tests, and sometimes even invasive procedures. However, these methods can be time-consuming, costly, and occasionally lack the sensitivity needed for early detection[8-10] .  \nRecent advancements in technology have led to the development of more sophisticated diagnostic tools that can provide rapid and accurate measurements of metabolic ions. Techniques such as advanced biosensors, mass spectrometry, and ion- selective electrodes, are now being integrated into clinical practice[11-13] . These innovations not only improve the speed and accuracy of diagnoses but also enhance the ability to monitor the effectiveness of treatment regimes in real time[14-16] . Tubular and vertical organic transistors are three dimensional (3D) devices designed to optimize surface area and integration density in flexible electronics. Tubular transistors feature a cylindrical structure, enabling flexibility and stretchability, while vertical transistors stack layers vertically, enhancing current flow and reducing the footprint[17, 18] . However, 3D fiber-based organic transistors (FOTs) offer superior attributes, including enhanced mechanical flexibility, greater surface area for charge transport, and the ability to be woven into fabrics, making them ideal for wearable electronics and other advanced applications where integration with textiles and high mechanical resilience  \nare crucial.  \n3D flexible FOTs represent a significant innovation in the field of biosensor and bioelectronics, combining the advantages of organic semiconductor materials with the mechanical flexibility necessary for 3D wearable and implanta","cbCaifIxCREwrAJQ","https://ap.wps.com/l/cbCaifIxCREwrAJQ","pdf",2115934,1,26,"English","en",105,"# Introduction\n## Metabolic ion diagnosis and clinical relevance\n## Emerging biosensing and measurement technologies\n## 3D flexible FOT devices and sensing advantages\n## Machine learning integration for metabolite analysis\n## Scope and contributions of this work","[{\"question\":\"What is the core idea of the proposed system?\",\"answer\":\"The system combines 3D flexible fiber-based organic transistor (FOT) sensors with machine learning to analyze metabolites with high diagnostic accuracy for clinical applications.\"},{\"question\":\"How does machine learning improve metabolite analysis?\",\"answer\":\"Machine learning processes complex, high-dimensional sensor data to identify patterns and predict diagnostic outcomes, enabling high-accuracy metabolite level estimation.\"},{\"question\":\"What evidence is used to evaluate performance in clinical contexts?\",\"answer\":\"The paper evaluates sensing performance using both artificial solutions and practical human blood samples, including cases related to hypernatremia syndrome.\"}]","Machine Learning-Assisted 3D Flexible Organic Transistor for High Accuracy Metabolites Analysis and Clinical Applications | 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