Personalized Healthcare Solutions Research Articles

Genetics, pharmacotherapy, and dietary interventions in childhood obesity

Childhood obesity has emerged as a major global health issue, contributing to the increased prevalence of chronic conditions and adversely affecting the quality of life and future prospects of affected individuals, thereby presenting a substantial societal challenge. This complex condition, influenced by the interplay of genetic predispositions and environmental factors, is characterized by excessive energy intake due to uncontrolled appetite regulation and a Westernized diet. Managing obesity in childhood requires specific considerations compared with adulthood, given the vulnerability of the critical juvenile–adolescent period to toxicity and developmental defects. Consequently, common treatment options for adult obesity may not directly apply to younger populations. Therefore, research on childhood obesity has focused on genetic defects in regulating energy intake, alongside pharmacotherapy and dietary interventions as management approaches, with an emphasis on safety concerns. This review aims to summarize canonical knowledge and recent findings on genetic factors contributing to childhood obesity. Additionally, it assesses the efficacy and safety of existing pharmacotherapies and dietary interventions and suggests future research directions. By providing a comprehensive understanding of the complex dynamics of childhood obesity, this review aims to offer insights into more targeted and effective strategies for addressing this condition, including personalized healthcare solutions.

Artificial intelligence and machine learning in axial spondyloarthritis.

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies

This study presents a comprehensive research methodology integrating computational approaches, statistical analysis, and visualization techniques to predict biocompatible materials for medical implants and evaluate predictive model performance (Whiting K. 2020, October). The initial phase involves data acquisition and preprocessing, organizing a representative dataset into a pandas data frame. Visualization of the dataset through bar, pie, and line charts provides insights into relationships between materials and functional attributes. The subsequent phase focuses on evaluating a predictive model using simulated datasets and key metrics such as accuracy, precision, recall, F1 score, and the receiver operating characteristics (ROC) curve with an area under the curve (AUC) value. Performance metrics are visually represented through bar charts and ROC curves, aiding stakeholders in understanding the model’s strengths and areas for improvement. The confusion matrix offers a granular examination of the model’s classification performance. The results and discussion section delves into graphical representations, emphasizing the material vs. strength/conductance/resistance/function chart, elucidating the diverse functional profiles of materials. The distribution of material functionality pie chart succinctly illustrates the proportional contribution of each material, aiding informed decision-making in material selection. The materials performance graph provides a nuanced understanding of material characteristics, guiding the development of personalized healthcare solutions. Model performance metrics and receiver operating characteristics graphs comprehensively assess the predictive model, while the confusion matrix details classification outcomes. This methodology and its visualizations contribute to predicting biocompatible materials, emphasizing the significance of advanced computational approaches for efficiently navigating the complex material space. The study’s outcomes inform both material scientists and healthcare professionals, guiding the development of personalized healthcare solutions tailored to specific patient needs.

Polymer Hydrogel‐Based Multifunctional Theranostics for Managing Diabetic Wounds

AbstractChronic, non‐healing wounds pose significant challenges for public health, particularly in the context of diabetes, and carry significant economic consequences. This article introduces a new solution in the form of a wireless theranostic patch, developed to meet the critical need for real‐time monitoring and targeted treatments to facilitate optimal healing. The patch incorporates advanced materials that are both multifunctional and electro‐responsive, leveraging a sophisticated blend of smart hydrogels and wearable bioelectronics to support diabetic wound management with unparalleled efficacy. With electro‐responsive multifunctional polymer hydrogels at its core, the patch delivers a stretchable, antimicrobial, and moist environment for the wound, with added benefits such as conductivity and visibility. The materials also allow for continuous and autonomous monitoring of glucose and pH levels, providing precise and personalized treatments like insulin delivery via iontophoresis and electrical stimulation. Animal models have demonstrated that this integrated system is highly adaptable, effectively promoting wound closure and healing. Overall, the wireless theranostic system offers an exciting prospect for personalized healthcare solutions, adopting a patient‐centric approach that prioritizes real‐time, targeted care for chronic wounds. Its incorporation of advanced materials and electro‐controlled treatments paves the way for new and innovative approaches to wound management.

Leveraging Django and Machine Learning to Craft a Health Forecasting System, Tailoring Personalized Wellness Strategies

Abstract: The goal of this thesis is to develop a health forecasting system that combines Django with cutting-edge machine learning in order to customize wellness plans according to personal health information. The study addresses model selection, data preprocessing, and user interface creation as it investigates the potential synergies between machine learning and web development. Predictive health analytics potential and gaps are identified through a review of the literature. The goal of the project is to create a real-time, scalable health forecasting system while protecting user privacy. Neural networks and decision trees are two examples of machine learning methods that are utilized. The efficacy of the system in delivering precise health projections and augmenting user involvement in proactive wellness management is evaluated through empirical assessment and case studies. In order to further personalized healthcare solutions, practitioners, researchers, and developers can benefit greatly from the research's insightful contributions.

Detection Of Stress And Its Levels Through Sleep Using AI

In this work, stress level detection during sleep using AI and physiological signals is explored. Stress and sleep, as well as their interplay in maintaining physical and mental health, are investigated. The potential of AI models in sleep monitoring and stress level detection is examined. Wearable technology is used to gather physiological signals such as breathing, heart rate, body temp, and others. To assure data quality, careful data preparation is carried out. and extraction of relevant features necessary for accurate stress level prediction. Two AI models, namely K-Nearest Neighbors (KNN) and Random Forest classifiers, are utilized for stress level prediction on a balanced dataset. To pinpoint the most important factors in stress level estimation, a feature significance analysis is also carried out. Remarkable accuracy is achieved by the AI models, with stress level prediction accuracy reaching approximately 99%. The potential of AI and physiological signals in revolutionizing stress assessment methodologies is highlighted. Broader implications of the findings, particularly the relationship between sleep and stress, are discussed, and ethical considerations, including data privacy and responsible AI implementation, are addressed. The integration of AI-based sleep analysis into wearable devices is envisioned, empowering individuals to manage stress and optimize their well-being. Valuable insights into stress level detection during sleep are contributed by this thesis, propelling forward the field of AI-driven healthcare applications. The groundwork for future advancements in stress management strategies and personalized healthcare solutions is laid by this research.

Nanometric Hydroxyapatite Particles as Active Ingredient for Bioinks: A Review

Additive manufacturing (AM), frequently cited as three-dimensional (3D) printing, is a relatively new manufacturing technique for biofabrication, also called 3D manufacture with biomaterials and cells. Recent advances in this field will facilitate further improvement of personalized healthcare solutions. In this regard, tailoring several healthcare products such as implants, prosthetics, and in vitro models, would have been extraordinarily arduous beyond these technologies. Three-dimensional-printed structures with a multiscale porosity are very interesting manufacturing processes in order to boost the capability of composite scaffolds to generate bone tissue. The use of biomimetic hydroxyapatite as the main active ingredient for bioinks is a helpful approach to obtain these advanced materials. Thus, 3D-printed biomimetic composite designs may produce supplementary biological and physical benefits. Three-dimensional bioprinting may turn to be a bright solution for regeneration of bone tissue as it enables a proper spatio-temporal organization of cells in scaffolds. Different types of bioprinting technologies and essential parameters which rule the applicability of bioinks are discussed in this review. Special focus is made on hydroxyapatite as an active ingredient for bioinks design. The goal of such bioinks is to reduce the constraints of commonly applied treatments by enhancing osteoinduction and osteoconduction, which seems to be exceptionally promising for bone regeneration.

Computational modeling of bicuspid aortopathy: Towards personalized risk strategies

This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.

Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment

There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.

Psychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform

It is increasingly recognized that stress has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysiological parameter during daily life can be greatly helpful in stress evaluation. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors.In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed mobile platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality, offer interesting opportunities for innovative personal health-care solutions to stress.

Global systems biology, personalized medicine and molecular epidemiology

Mol Syst Biol. 2: 52 One of the great challenges for 21st century medicine is to deliver effective therapies that are tailored to the exact biology or biological state of an individual to enable so‐called ‘personalized healthcare solutions’. Ideally, this would involve a system of patient evaluation that would tell clinicians the correct drug, dose or intervention for any individual before the start of therapy. A practical approach to this evaluation is the concept of patient stratification in which individuals are biologically subclassified (classically according to some genetic features) and biofeatures modelled in relation to outcome. In principle, such stratification for personalized therapy can be applied to drug safety and efficacy modelling and to more general healthcare paradigms involving optimized nutrition and lifestyle management. Of course, truly personalized treatments, even if they can be developed and applied widely, will lamentably always be a luxury of the worlds' richest citizens and nations. So in some respects, personalized healthcare might appear to be at the opposite end of the medical spectrum to the subject of epidemiology in which disease risk factors and disease incidence are studied in populations rich and poor alike. Systems biology provides us with a common language for both describing and modelling the integrated action of regulatory networks at many levels of biological organization from the subcellular through cell, tissue and organ right up to the whole organism. The relatively new science of molecular epidemiology concerns the measurement of the fundamental biochemical factors that underlie population disease demography and understanding ‘the health of nations’ and this subject naturally lends it to systems biology approaches. Hence, systems biology is certain to have in future a major role in both the development of personalized medicine and in molecular epidemiological studies. Populations are, of course, made up of individuals and, in …