A qualitative study on the facilitators and barriers to adopting the N-of-1 trial methodology as part of clinical practice: potential versus implementation challenges

PURPOSE

To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients.

METHODS

We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis.

RESULTS

The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice.

CONCLUSIONS

There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring

Continuous monitoring of blood pressure (BP) and multiparametric analysis of cardiac functions are crucial for the early diagnosis and therapy of cardiovascular diseases. However, existing monitoring approaches often suffer from bulky and intrusive apparatus, cumbersome testing procedures, and challenging data processing, hampering their applications in continuous monitoring. Here, a heterogeneously hierarchical piezoelectric composite is introduced for wearable continuous BP and cardiac function monitoring, overcoming the rigidity of ceramic and the insensitivity of polymer. By optimizing the hierarchical structure and components of the composite, the developed piezoelectric sensor delivers impressive performances, ensuring continuous and accurate monitoring of BP at Grade A level. Furthermore, the hemodynamic parameters are extracted from the detected signals, such as local pulse wave velocity, cardiac output, and stroke volume, all of which are in alignment with clinical results. Finally, the all-day tracking of cardiac function parameters validates the reliability and stability of the developed sensor, highlighting its potential for personalized healthcare systems, particularly in early diagnosis and timely intervention of cardiovascular disease.

Advances in Printed Electronic Textiles

Electronic textiles (e-textiles) have emerged as a revolutionary solution for personalized healthcare, enabling the continuous collection and communication of diverse physiological parameters when seamlessly integrated with the human body. Among various methods employed to create wearable e-textiles, printing offers unparalleled flexibility and comfort, seamlessly integrating wearables into garments. This has spurred growing research interest in printed e-textiles, due to their vast design versatility, material options, fabrication techniques, and wide-ranging applications. Here, a comprehensive overview of the crucial considerations in fabricating printed e-textiles is provided, encompassing the selection of conductive materials and substrates, as well as the essential pre- and post-treatments involved. Furthermore, the diverse printing techniques and the specific requirements are discussed, highlighting the advantages and limitations of each method. Additionally, the multitude of wearable applications made possible by printed e-textiles is explored, such as their integration as various sensors, supercapacitors, and heated garments. Finally, a forward-looking perspective is provided, discussing future prospects and emerging trends in the realm of printed wearable e-textiles. As advancements in materials science, printing technologies, and design innovation continue to unfold, the transformative potential of printed e-textiles in healthcare and beyond is poised to revolutionize the way wearable technology interacts and benefits.

Core-Sheath Heterogeneous Interlocked Conductive Fiber Enables Smart Textile for Personalized Healthcare and Thermal Management

Whereas thermal comfort and healthcare management during long-term wear are essentially required for wearable system, simultaneously achieving them remains challenge. Herein, a highly comfortable and breathable smart textile for personal healthcare and thermal management is developed, via assembling stimuli-responsive core-sheath dual network that silver nanowires(AgNWs) core interlocked graphene sheath induced by MXene. Small MXene nanosheets with abundant groups is proposed as a novel "dispersant" to graphene according to "like dissolves like" theory, while simultaneously acting as "cross-linker" between AgNWs and graphene networks by filling the voids between them. The core-sheath heterogeneous interlocked conductive fiber induced by MXene "cross-linking" exhibits a reliable response to various mechanical/electrical/light stimuli, even under large mechanical deformations(100%). The core-sheath conductive fiber-enabled smart textile can adapt to movements of human body seamlessly, and convert these mechanical deformations into character signals for accurate healthcare monitoring with rapid response(440 ms). Moreover, smart textile with excellent Joule heating and photothermal effect exhibits instant thermal energy harvesting/storage during the stimuli-response process, which can be developed as self-powered thermal management and dynamic camouflage when integrated with phase change and thermochromic layer. The smart fibers/textiles with core-sheath heterogeneous interlocked structures hold great promise in personalized healthcare and thermal management.

Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead

A significant worldwide health issue, Type 2 Diabetes Mellitus (T2DM) calls for creative solutions. This in-depth review examines the growing severity of T2DM and the requirement for individualized management approaches. It explores the use of artificial intelligence (AI) in the treatment of diabetes, highlighting its potential for diagnosis, customized treatment plans, and patient self-management. The paper highlights the roles played by AI applications such as expert systems, machine learning algorithms, and deep learning approaches in the identification of retinopathy, the interpretation of clinical guidelines, and prediction models. Examined are difficulties with individualized diabetes treatment, including complex technological issues and patient involvement. The review highlights the revolutionary potential of AI in the management of diabetes and calls for a balanced strategy in which AI supports clinical knowledge. It is crucial to pay attention to ethical issues, data privacy, and joint research initiatives.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

Soft Bioelectronics for Therapeutics

Soft bioelectronics play an increasingly crucial role in high-precision therapeutics due to their softness, biocompatibility, clinical accuracy, long-term stability, and patient-friendliness. In this review, we provide a comprehensive overview of the latest representative therapeutic applications of advanced soft bioelectronics, ranging from wearable therapeutics for skin wounds, diabetes, ophthalmic diseases, muscle disorders, and other diseases to implantable therapeutics against complex diseases, such as cardiac arrhythmias, cancer, neurological diseases, and others. We also highlight key challenges and opportunities for future clinical translation and commercialization of soft therapeutic bioelectronics toward personalized medicine.

Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System

Smart wearable patch systems that combine biosensing and therapeutic components have emerged as promising approaches for personalized healthcare and therapeutic platforms that enable self-administered, noninvasive, user-friendly, and long-acting smart drug delivery. Sensing components can continuously monitor physiological and biochemical parameters, and the monitoring signals can be transferred to various stimuli using actuators. In therapeutic components, stimuli-responsive carrier-based drug delivery systems (DDSs) provide on-demand drug delivery in a closed-loop manner. This review provides an overview of the recent advances in smart wearable patch systems, focusing on sensing components, stimuli, and therapeutic components. Additionally, this review highlights the potential of fully integrated smart wearable patch systems for personalized medicine. Furthermore, challenges associated with the clinical applications of this system and future perspectives are discussed, including issues related to drug loading and reloading, biocompatibility, accuracy of sensing and drug delivery, and largescale fabrication.

From big data to better patient outcomes

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

[Personalized medicine and healthcare: where are we now, where should we go?]

The authors of this manuscript are representatives of different subdisciplines of medicine, all of them are experienced researchers. As of their origin, they are practicing doctors from the primary care and from the clinical/hospital setting, diagnostics experts, researchers from healthcare management, health economics, representatives of patients' rights and patient organizations. They are all devoted to the implementation of personalized medicine and personalized healthcare in Hungary. The current manuscript - also meant to be a keynote message provoking further discussion in the medical community - is devoted to correcting for two false ideas. One is that personalized medicine is not yet ready for practical applications, it is merely a research area of futurologists. The other false idea is that only (or mainly) the lack of financial resources hinders the introduction of personalized healthcare in Hungary. Orv Hetil. 2023; 164(6): 202-209.

Axillary Downstaging and the Impact of Clinical Axillary Status on Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer: A Network Meta-Analysis

Background: Lymph node downstaging and the achievement of total-pCR (ypT0/is ypN0) after neoadjuvant therapy are of great importance in HER-2 positive breast cancer. We aim to provide an overall review of neoadjuvant regimens for lymph node downstaging and to indirectly compare the total-pCR by various neoadjuvant regimens with network meta-analysis in HER2-positive patients according to their clinical lymph node status. Methods: Five English databases were searched comprehensively and systematically for relevant RCTs and case-control studies. The data extracted from the included studies were analyzed with the use of Review Manager 5.3 or STATA 15.0 software. Results: A total of 1508 published manuscripts were identified, and 17 studies including 4747 patients were finally included in our analysis. The network meta-analysis of total-pCR showed that dual-target therapy is significantly better than single-target therapy in clinically node-positive patients, and carboplatin performed significantly better than anthracycline in single-target condition. Lapatinib performed poorly in clinically node-positive patients. However, lapatinib in combination with trastuzumab was ranked at the top in the clinically node-negative group, and pertuzumab showed dissatisfied performance in contrast to the primacy of pertuzumab in clinically node-positive groups. Conclusion: In summary, different lymph node statuses led to the diverse first choice of neoadjuvant regimen. We highly recommended TCbHP as the first choice for the neoadjuvant treatment in clinically node-positive HER-2 positive breast cancer. Since lapatinib with trastuzumab ranked top in the clinically node-negative group, we looked forward to discovering the potential value of TKI in clinically node-negative patients, which needs further analysis in the future.

Biosensors for point-of-care testing and personalized monitoring of gastrointestinal microbiota

The gastrointestinal (GI) microbiota is essential in maintaining human health. Alteration of the GI microbiota or gut microbiota (GM) from homeostasis (i.e., dysbiosis) is associated with several communicable and non-communicable diseases. Thus, it is crucial to constantly monitor the GM composition and host-microbe interactions in the GI tract since they could provide vital health information and indicate possible predispositions to various diseases. Pathogens in the GI tract must be detected early to prevent dysbiosis and related diseases. Similarly, the consumed beneficial microbial strains (i.e., probiotics) also require real-time monitoring to quantify the actual number of their colony-forming units within the GI tract. Unfortunately, due to the inherent limitations associated with the conventional methods, routine monitoring of one's GM health is not attainable till date. In this context, miniaturized diagnostic devices such as biosensors could provide alternative and rapid detection methods by offering robust, affordable, portable, convenient, and reliable technology. Though biosensors for GM are still at a relatively preliminary stage, they can potentially transform clinical diagnosis in the near future. In this mini-review, we have discussed the significance and recent advancements of biosensors in monitoring GM. Finally, the progresses on future biosensing techniques such as lab-on-chip, smart materials, ingestible capsules, wearable devices, and fusion of machine learning/artificial intelligence (ML/AI) have also been highlighted.

Smart Wearable Systems for the Remote Monitoring of Selected Vascular Disorders of the Lower Extremity: A Systematic Review

This systematic review aims at providing an overview of the state of the art regarding smart wearable systems (SWS) applications to monitor the status of patients suffering from vascular disorders of the lower extremity. Peer-reviewed literature has been analyzed to identify employed data collection methods, system characteristics, and functionalities, and research challenges and limitations to be addressed. The Medline (PubMed) and SCOPUS databases were considered to search for publications describing SWS for remote or continuous monitoring of patients suffering from intermittent claudication, venous ulcers, and diabetic foot ulcers. Publications were first screened based on whether they describe an SWS applicable to the three selected vascular disorders of the lower extremity, including data processing and output to users. Information extracted from publications included targeted disease, clinical parameters to be measured and wearable devices used; system outputs to the user; system characteristics, including capabilities of remote or continuous monitoring or functionalities resulting from advanced data analyses, such as coaching, recommendations, or alerts; challenges and limitations reported; and research outputs. A total of 128 publications were considered in the full-text analysis, and 54 were finally included after eligibility criteria assessment by four independent reviewers. Our results were structured and discussed according to three main topics consisting of data collection, system functionalities, and limitations and challenges.

A Personalized Electronic Tattoo for Healthcare Realized by On-the-Spot Assembly of an Intrinsically Conductive and Durable Liquid-Metal Composite

Conventional electronic (e-) skins are a class of thin-film electronics mainly fabricated in laboratories or factories, which is incapable of rapid and simple customization for personalized healthcare. Here a new class of e-tattoos is introduced that can be directly implemented on the skin by facile one-step coating with various designs at multi-scale depending on the purpose of the user without a substrate. An e-tattoo is realized by attaching Pt-decorated carbon nanotubes on gallium-based liquid-metal particles (CMP) to impose intrinsic electrical conductivity and mechanical durability. Tuning the CMP suspension to have low-zeta potential, excellent wettability, and high-vapor pressure enables conformal and intimate assembly of particles directly on the skin in 10 s. Low-cost, ease of preparation, on-skin compatibility, and multifunctionality of CMP make it highly suitable for e-tattoos. Demonstrations of electrical muscle stimulators, photothermal patches, motion artifact-free electrophysiological sensors, and electrochemical biosensors validate the simplicity, versatility, and reliability of the e-tattoo-based approach in biomedical engineering.

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring

Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor network is reported, which can overcome different user facial contours and environmental interferences to collect highly accurate respiratory signals. Inspired by cribellate silk, Rayleigh-instability-induced spindle-knot fibers are knitted for the fabrication of permeable and moisture-proof textile triboelectric sensors that hold a decent signal-to-noise ratio of 51.2 dB, a response time of 0.28 s, and a sensitivity of 0.46 V kPa^-1 . With the assistance of deep learning, the on-mask sensor network can realize the respiration pattern recognition with a classification accuracy up to 100%, showing great improvement over a single respiratory sensor. Additionally, a customized user-friendly cellphone application is developed to connect the processed respiratory signals for real-time data-driven diagnosis and one-click health data sharing with the clinicians. The deep-learning-assisted on-mask sensor network opens a new avenue for personalized respiration management in the era of the Internet of Things.

Lab-on-a-Contact Lens: Recent Advances and Future Opportunities in Diagnostics and Therapeutics

The eye is one of the most complex organs in the human body, containing rich and critical physiological information (e.g., intraocular pressure, corneal temperature, and pH) as well as a library of metabolite biomarkers (e.g., glucose, proteins, and specific ions). Smart contact lenses (SCLs) can serve as a wearable intelligent ocular prosthetic device capable of noninvasive and continuous monitoring of various essential physical/biochemical parameters and drug loading/delivery for the treatment of ocular diseases. Advances in SCL technologies and the growing public interest in personalized health are accelerating SCL research more than ever before. Here, the current status and potential of SCL development through a comprehensive review from fabrication to applications to commercialization are discussed. First, the material, fabrication, and platform designs of the SCLs for the diagnostic and therapeutic applications are discussed. Then, the latest advances in diagnostic and therapeutic SCLs for clinical translation are reviewed. Later, the established techniques for wearable power transfer and wireless data transmission applied to current SCL devices are summarized. An outlook, future opportunities, and challenges for developing next-generation SCL devices are also provided. With the rise in interest of SCL development, this comprehensive and essential review can serve as a new paradigm for the SCL devices.

Assisting Personalized Healthcare of Elderly People: Developing a Rule-Based Virtual Caregiver System Using Mobile Chatbot

To assist personalized healthcare of elderly people, our interest is to develop a virtual caregiver system that retrieves the expression of mental and physical health states through human-computer interaction in the form of dialogue. The purpose of this paper is to implement and evaluate a virtual caregiver system using mobile chatbot. Unlike the conventional health monitoring approach, our key idea is to integrate a rule-based virtual caregiver system (called "Mind Monitoring" service) with the physical, mental, and social questionnaires into the mobile chat application. The elderly person receives one question from the mobile chatbot per day, and answers it by pushing the optional button or using a speech recognition technique. Furthermore, a novel method is implemented to quantify the answers, generate visual graphs, and send the corresponding summaries or advice to the specific elder. In the experimental evaluation, we applied it to eight elderly subjects and 19 younger subjects within 14 months. As main results, its effects were significantly improved by the proposed method, including the above 80% in the response rate, the accurate reflection of their real lives from the responses, and high usefulness of the feedback messages with software quality requirements and evaluation. We also conducted interviews with subjects for health analysis and improvement.

A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine

Precision medicine can be defined as the comparison of a new patient with existing patients that have similar characteristics and can be referred to as patient similarity. Several deep learning models have been used to build and apply patient similarity networks (PSNs). However, the challenges related to data heterogeneity and dimensionality make it difficult to use a single model to reduce data dimensionality and capture the features of diverse data types. In this paper, we propose a multi-model PSN that considers heterogeneous static and dynamic data. The combination of deep learning models and PSN allows ample clinical evidence and information extraction against which similar patients can be compared. We use the bidirectional encoder representations from transformers (BERT) to analyze the contextual data and generate word embedding, where semantic features are captured using a convolutional neural network (CNN). Dynamic data are analyzed using a long-short-term-memory (LSTM)-based autoencoder, which reduces data dimensionality and preserves the temporal features of the data. We propose a data fusion approach combining temporal and clinical narrative data to estimate patient similarity. The experiments we conducted proved that our model provides a higher classification accuracy in determining various patient health outcomes when compared with other traditional classification algorithms.

Wearable Pressure Sensors for Pulse Wave Monitoring

Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for long-term and real-time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pressure sensors for wearable health monitoring. Herein, an overview of wearable pressure sensors for human pulse wave monitoring is presented, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. The conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system are outlined. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. Exemple applications of pulse wave measurement based on microengineering structured devices are then summarized. Finally, a discussion of the opportunities and challenges that wearable pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare is given in conclusion.

Rehabilitation of Total Knee Arthroplasty by Integrating Conjoint Isometric Myodynamia and Real-Time Rotation Sensing System

As the world population structure has already exhibited an inevitable trend of aging, technical advances that can provide better eldercare are highly desired. Knee osteoarthritis, one of the most common age-associated diseases, can be effectively treated via total knee arthroplasty (TKA). However, patients are suffering from the recovery process due to inconvenience in post-hospital treatment. Here, a portable, modular, and wearable brace for self-assessment of TKA patients' rehabilitation is reported. This system mainly consists of a force transducer for isometric muscle strength measurement and an active angle sensor for knee bending detection. Clinical experiments on TKA patients demonstrate the feasibility and significance of the system. Specifically, via brace-based personalized healthcare, the TKA patients' rehabilitation process is quantified in terms of myodynamia, and a definite rehabilitation enhancement is obtained. Additionally, new indicators, that is, isometric muscle test score, for evaluating TKA rehabilitation are proposed. It is anticipated that, as the cloud database is employed and more rehabilitation data are collected in the near future, the brace system can not only facilitate rehabilitations of TKA patients, but also improve life quality for geriatric patients and open a new space for remote artificial intelligence medical engineering.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high-fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal-to-noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa^-1 . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built-in algorithm is developed for one-click health data sharing and data-driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.

Triboelectric Nanogenerators for Self-Powered Wound Healing

Wound healing, one of the most complex processes in the human body, involves the spatial and temporal synchronization of a variety of cell types with distinct roles. Slow or nonhealing skin wounds have potentially life-threatening consequences, ranging from infection to scar, clot, and hemorrhage. Recently, the advent of triboelectric nanogenerators (TENGs) has brought about a plethora of self-powered wound healing opportunities, owing to their pertinent features, including wide range choices of constitutive biocompatible materials, simple fabrication, portable size, high output power, and low cost. Herein, a comprehensive review of TENGs as an emerging biotechnology for wound healing applications is presented and covered from three unique aspects: electrical stimulation, antibacterial activity, and drug delivery. To provide a broader context of TENGs applicable to wound healing applications, state-of-the-art designs are presented and discussed in each section. Although some challenges remain, TENGs are proving to be a promising platform for human-centric therapeutics in the era of Internet of Things. Consequently, TENGs for wound healing are expected to provide a new solution in wound management and play an essential role in the future of point-of-care interventions.

Self-Powered Respiration Monitoring Enabled By a Triboelectric Nanogenerator

In mammals, physiological respiration involves respiratory cycles of inhaled and exhaled breaths, which has traditionally been an underutilized resource potentially encompassing a wealth of physiologically relevant information as well as clues to potential diseases. Recently, triboelectric nanogenerators (TENGs) have been widely adopted for self-powered respiration monitoring owing to their compelling features, such as decent biocompatibility, wearing comfort, low-cost, and high sensitivity to respiration activities in the aspect of low frequency and slight amplitude body motions. Physiological respiration behaviors and exhaled chemical regents can be precisely and continuously monitored by TENG-based respiration sensors for personalized health care. This article presents an overview of TENG enabled self-powered respiration monitoring, with a focus on the working principle, sensing materials, functional structures, and related applications in both physical respiration motion detection and chemical breath analysis. Concepts and approaches for acquisition of physical information associated with respiratory rate and depth are covered in the first part. Then the sensing mechanism, theoretical modeling, and applications related to detection of chemicals released from breathing gases are systemically summarized. Finally, the opportunities and challenges of triboelectric effect enabled self-powered respiration monitoring are comprehensively discussed and criticized.

Wearable Biosensors for Non-Invasive Sweat Diagnostics

Recent advances in microfluidics, microelectronics, and electrochemical sensing methods have steered the way for the development of novel and potential wearable biosensors for healthcare monitoring. Wearable bioelectronics has received tremendous attention worldwide due to its great a potential for predictive medical modeling and allowing for personalized point-of-care-testing (POCT). They possess many appealing characteristics, for example, lightweight, flexibility, good stretchability, conformability, and low cost. These characteristics make wearable bioelectronics a promising platform for personalized devices. In this paper, we review recent progress in flexible and wearable sensors for non-invasive biomonitoring using sweat as the bio-fluid. Real-time and molecular-level monitoring of personal health states can be achieved with sweat-based or perspiration-based wearable biosensors. The suitability of sweat and its potential in healthcare monitoring, sweat extraction, and the challenges encountered in sweat-based analysis are summarized. The paper also discusses challenges that still hinder the full-fledged development of sweat-based wearables and presents the areas of future research.

Afirma Genomic Sequencing Classifier and Xpression Atlas Molecular Findings in Consecutive Bethesda III-VI Thyroid Nodules

CONTEXT

Broad genomic analyses among thyroid histologies have been described from relatively small cohorts.

OBJECTIVE

Investigate the molecular findings across a large, real-world cohort of thyroid fine-needle aspiration (FNA) samples.

DESIGN

Retrospective analysis of RNA sequencing data files.

SETTING

Clinical Laboratory Improvement Amendments laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing.

PARTICIPANTS

A total of 50 644 consecutive Bethesda III-VI nodules.

INTERVENTION

None.

MAIN OUTCOME MEASURES

Molecular test results.

RESULTS

Of 48 952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or noninvasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, whereas for BRAF and ALK fusions it was 81% and 67%, respectively. At least 1 genomic alteration was identified by the expanded Afirma XA panel in 70% of medullary thyroid carcinoma classifier-positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs.

CONCLUSIONS

This large study demonstrates that almost one-half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

Evaluation of a strategy for difficult embryo transfers from a prospective series of 2,046 transfers

Objective

To evaluate an embryo transfer strategy for difficult transfers (DiTs).

Design

Prospective, nonrandomized, observational, cohort study

Setting

A hospital fertility center in France.

Patient(s)

Data were collected on all embryo transfers conducted using the strategy between February 2014 and February 2020.

Intervention(s)

Anatomical characteristics that could cause DiT were identified by transvaginal ultrasound and the catheter was adapted accordingly. Transfer was guided by transvaginal ultrasound. After passage through the cervix, a rest period was introduced to allow any contractions to stop before embryo deposition in the uterus.

Main Outcome Measure(s)

The primary criterion was the percentage of pregnancies per transfer (P/T) after an easy transfer (EaT) or a DiT. The secondary criteria included the anatomical causes of DiT and the patients’ levels of discomfort.

Result(s)

Of 2,046 transfers, 257 (12%) were DiTs: minor difficulties (n = 152; 7.4%), major difficulties (n = 96; 4.7%), very significant difficulties (n = 7; 0.3%), or impossible (n = 2; 0.1%). The most common causes of DiTs were endocervical crypts (54%), tortuous cervical canal (36%), and marked uterine anteversions (30%). Several causes were often responsible for DiTs. There was no statistically significant difference in the P/T between the EaTs (n = 1,789, 41%) and all degrees of DiT (n = 257, 37%). In addition, there was no statistically significant difference between the level of patient-reported discomfort in the EaT and DiT groups.

Conclusion(s)

This study demonstrated that an adapted embryo transfer strategy, monitored by transvaginal ultrasound, led to similar pregnancy rates regardless of whether the transfer was easy or difficult.

Triboelectric Nanogenerators for Therapeutic Electrical Stimulation

Current solutions developed for the purpose of in and on body (IOB) electrical stimulation (ES) lack autonomous qualities necessary for comfortable, practical, and self-dependent use. Consequently, recent focus has been placed on developing self-powered IOB therapeutic devices capable of generating therapeutic ES for human use. With the recent invention of the triboelectric nanogenerator (TENG), harnessing passive human biomechanical energy to develop self-powered systems has allowed for the introduction of novel therapeutic ES solutions. TENGs are especially effective at providing ES for IOB therapeutic systems given their bioconformability, low cost, simple manufacturability, and self-powering capabilities. Due to the key role of naturally induced electrical signals in many physiological functions, TENG-induced ES holds promise to provide a novel paradigm in therapeutic interventions. The aim here is to detail research on IOB TENG devices applied for ES-based therapy in the fields of regenerative medicine, neurology, rehabilitation, and pharmaceutical engineering. Furthermore, considering TENG-produced ES can be measured for sensing applications, this technology is paving the way to provide a fully autonomous personalized healthcare system, capable of IOB energy generation, sensing, and therapeutic intervention. Considering these grounds, it seems highly relevant to review TENG-ES research and applications, as they could constitute the foundation and future of personalized healthcare.

Veterinary Big Data: When Data Goes to the Dogs

Simple Summary

Big data has created many opportunities to improve both preventive medicine and medical treatments. In the field of veterinary medical big data, information collected from companion animals, primarily dogs, can be used to inform healthcare decisions in both dogs and other species. Currently, veterinary medical datasets are an underused resource for translational research, but recent advances in data collection in this population have helped to make these data more accessible for use in translational studies. The largest open access dataset in the United States is part of the Dog Aging Project and includes detailed information about individual dog participant’s physical and chemical environments, diet, exercise, behavior, and comprehensive health history. These data are collected longitudinally and at regular intervals over the course of the dog’s lifespan. Large-scale datasets such as this can be used to inform our understanding of health, disease, and how to increase healthy lifespan.

Abstract

Dogs provide an ideal model for study as they have the most phenotypic diversity and known naturally occurring diseases of all non-human land mammals. Thus, data related to dog health present many opportunities to discover insights into health and disease outcomes. Here, we describe several sources of veterinary medical big data that can be used in research. These sources include medical records from primary medical care centers or referral hospitals, medical claims data from animal insurance companies, and datasets constructed specifically for research purposes. No data source provides information that is without limitations, but large-scale, prospective, longitudinally collected data from dog populations are ideal for further research as they offer many advantages over other data sources.

The evolution of personalized healthcare and the pivotal role of European regions in its implementation

Personalized medicine (PM) moves at the same pace of data and technology and calls for important changes in healthcare. New players are participating, providing impulse to PM. We review the conceptual foundations for PM and personalized healthcare and their evolution through scientific publications where a clear definition and the features of the different formulations are identifiable. We then examined PM policy documents of the International Consortium for Personalised Medicine and related initiatives to understand how PM stakeholders have been changing. Regional authorities and stakeholders have joined the race to deliver personalized care and are driving toward what could be termed as the next personalized healthcare. Their role as a key stakeholder in PM is expected to be pivotal.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

Textile Triboelectric Nanogenerators for Wearable Pulse Wave Monitoring

Arterial pulse waves are regarded as vital diagnostic tools in the assessment of cardiovascular disease (CVD). Because of their high sensitivity, rapid response time, wearability, and low cost, textile triboelectric nanogenerators (TENGs) are emerging as a compelling biotechnology for wearable pulse wave monitoring. We discuss sensing mechanisms for pulse-to-electricity conversion, analytical models for calculating cardiovascular parameters, and application scenarios for textile TENGs. We provide a prospective on the challenges that limit the wider application of this technology and suggest some future research directions. In the future, textile TENGs are expected to make an impact in the fields of wearable pulse wave monitoring and CVD diagnosis.

A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users' biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients' data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework's performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.

A Self-Powered Angle Sensor at Nanoradian-Resolution for Robotic Arms and Personalized Medicare

As the dominant component for precise motion measurement, angle sensors play a vital role in robotics, machine control, and personalized rehabilitation. Various forms of angle sensors have been developed and optimized over the past decades, but none of them would function without an electric power. Here, a highly sensitive triboelectric self-powered angle sensor (SPAS) exhibiting the highest resolution (2.03 nano-radian) after a comprehensive optimization is reported. In addition, the SPAS holds merits of light weight and thin thickness, which enables its extensive integrated applications with minimized energy consumption: a palletizing robotic arm equipped with the SPAS can precisely reproduce traditional Chinese calligraphy via angular data it collects. In addition, the SPAS can be assembled in a medicare brace to record the flexion/extension of joints, which may benefit personalized orthopedic recuperation. The SPAS paves a new approach for applications in the emerging fields of robotics, sensing, personalized medicare, and artificial intelligence.

The Afirma Xpression Atlas for thyroid nodules and thyroid cancer metastases: Insights to inform clinical decision-making from a fine-needle aspiration sample

Recent analytical and clinical validation of the Afirma Xpression Atlas (XA) demonstrates test reliability and the identification of genomic alterations that may inform patient management. The updated Afirma Genomic Sequencing Classifier and XA reports aim to optimize the understanding of these contributions, including decisions about observation versus surgery, the need for disease‐specific preoperative testing, associated neoplasm types, prognostics, the identification of molecular targets for systemic therapy, and the recognition of potential hereditary syndromes.

Alveolus-Inspired Active Membrane Sensors for Self-Powered Wearable Chemical Sensing and Breath Analysis

Fossil fuel internal combustion engines generate and release a huge amount of nitrogen dioxide, leading to respiratory and allergic diseases such as asthma, pneumonia, and possibly tuberculosis. Here we develop an alveolus-inspired membrane sensor (AIMS) for self-powered wearable nitrogen dioxide detection and personal physiological assessment. The bionic AIMS exhibits an excellent sensitivity up to 452.44%, a good linearity of 0.976, and superior selectivity under a NO₂ concentration of 50 ppm. Furthermore, the AIMS can also be employed to diagnose human breath behaviors for breath analysis. The fundamental sensing mechanism is established using a combination of thermodynamic analysis, finite-element analysis, and phase-field simulations. It is found that the depolarization field inside the sensitive materials plays a crucial role in the self-powered gas-sensing performance. This work not only provides an efficient, low-cost, portable, and environmentally friendly means for active environmental assessment and personal biomonitoring but also provides a deep understanding of the gas-sensing mechanisms.

Establishment of Integrated Biobank for Precision Medicine and Personalized Healthcare: The Tohoku Medical Megabank Project

The Tohoku Medical Megabank (TMM) project was established to provide creative reconstruction of the Tohoku area that suffered from a huge earthquake and ensuing tsunami (the Great East Japan Earthquake, GEJE). TMM aims to establish two large-scale genome cohorts and an integrated biobank managing biospecimen and related information. It supports community medicine by establishing next-generation medical systems through a combination of the prospective genome cohort studies with a total of 150,000 participants and genomic medicine. The strategies for genome analyses in TMM are to develop an elaborate genome reference panel by means of high-fidelity Japanese whole-genome sequence, to design custom single nucleotide polymorphism (SNP) arrays based on the reference panel, and to obtain genotype data for all the TMM cohort participants subsequently. Disease-associated genomic information and omics data, including metabolomics and microbiome analysis, provide an essential platform for precision medicine and personalized healthcare (PHC). Ethical, legal, and social issues (ELSI) and education are important for implementing genomic medicine. The major considerations of ELSI regarding each participant of the cohort studies are the respect for the autonomy and the protection of privacies. Moreover, developing and provide human resources not only for the TMM project but also for the social implementation of precision medicine and PHC is required. We started a pilot study of the return of genomic results for familial hypercholesterolemia (FH) as a target disease. TMM aims to establish solid platforms that support precision medicine and PHC based on the genomic and omics information and environmental and lifestyle factors of the individuals, which is one of the most advanced medical care beyond the evidenced-based medicine in the near future.

Value-Based Pricing and Reimbursement in Personalised Healthcare: Introduction to the Basic Health Economics

‘Value-based’ outcomes, pricing, and reimbursement are widely discussed as health sector reforms these days. In this paper, we discuss their meaning and relationship in the context of personalized healthcare, defined as receipt of care conditional on the results of a biomarker-based diagnostic test. We address the question: “What kinds of pricing and reimbursement models should be applied in personalized healthcare?” The simple answer is that competing innovators and technology adopters should have incentives that promote long-term dynamic efficiency. We argue that—to meet this social objective of optimal innovation in personalized healthcare—payers, as agents of their plan participants, should aim to send clear signals to their suppliers about what they value. We begin by revisiting the concept of value from an economic perspective, and argue that a broader concept of value is needed in the context of personalized healthcare. We discuss the market for personalized healthcare and the interplay between price and reimbursement. We close by emphasizing the potential barrier posed by inflexible or cost-based reimbursement systems, especially for biomarker-based predictive tests, and how these personalized technologies have global public goods characteristics that require global value-based differential pricing to achieve dynamic efficiency in terms of the optimal rate of innovation and adoption.

Dynamic access control model for privacy preserving personalized healthcare in cloud environment

When sharing and storing healthcare data in a cloud environment, access control is a central issue for preserving data privacy as a patient's personal health data may be accessed without permission from many stakeholders. Specifically, dynamic authorization for the access of data is required because personal health data is stored in cloud storage via wearable devices. Therefore, we propose a dynamic access control model for preserving the privacy of personal healthcare data in a cloud environment. The proposed model considers context information for dynamic access. According to the proposed model, access control can be dynamically determined by changing the context information; this means that even for a subject with the same role in the cloud, access permission is defined differently depending on the context information and access condition. Furthermore, we experiment the ability of the proposed model to provide correct responses by representing a dynamic access decision with real-life personalized healthcare system scenarios.

3D Printed Dry EEG Electrodes

Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise.

Advantages of Array-Based Technologies for Pre-Emptive Pharmacogenomics Testing

As recognised by the National Institutes of Health (NIH) Precision Medicine Initiative (PMI), microarray technology currently provides a rapid, inexpensive means of identifying large numbers of known genomic variants or gene transcripts in experimental and clinical settings. However new generation sequencing techniques are now being introduced in many clinical genetic contexts, particularly where novel mutations are involved. While these methods can be valuable for screening a restricted set of genes for known or novel mutations, implementation of whole genome sequencing in clinical practice continues to present challenges. Even very accurate high-throughput methods with small error rates can generate large numbers of false negative or false positive errors due to the high numbers of simultaneous readings. Additional validation is likely to be required for safe use of any such methods in clinical settings. Custom-designed arrays can offer advantages for screening for common, known mutations and, in this context, may currently be better suited for accredited, quality-controlled clinical genetic screening services, as illustrated by their successful application in several large-scale pre-emptive pharmacogenomics programs now underway. Excessive, inappropriate use of next-generation sequencing may waste scarce research funds and other resources. Microarrays presently remain the technology of choice in applications that require fast, cost-effective genome-wide screening of variants of known importance, particularly for large sample sizes. This commentary considers some of the applications where microarrays continue to offer advantages over next-generation sequencing technologies.

Life data monitoring and analysis model for personalized healthcare

As the focus of personal healthcare shifts from patient treatment to early detection and prevention, it is becoming increasingly important to manage personal wellness in our daily lives. Personal health monitoring of physical activities and status can be used to show users the distribution of their daily activities, making it easier for people to assess their health, adopt better lifestyles, and potentially decrease the occurrence of chronic diseases. In this paper, we propose a CA5W1HOnto-based life data monitoring model that provides basic monitored information from various devices and ensures preventive and proactive service for personalized healthcare. Additionally, we propose a life data analysis method to correlate the self-monitoring of activities with the status of the human body.

A shift toward personalized healthcare: does the Affordable Care Act provide enough incentive for change?

Personalized healthcare, which uses individual characteristics to better predict and prevent disease and customize therapies, is a potential solution to our current healthcare crisis. Personalized care aims to improve quality of care and reduce overall healthcare costs. Despite its potential, adoption of personalized healthcare has been slow for several reasons, one of which is related to financial incentives toward change. This perspective piece discusses how the Affordable Care Act (ACA), through support for preventive care and comparative effectiveness research, begins to align the right incentives toward innovation around personalized risk prediction, innovation that is much needed as we aim to improve the health of individuals and communities.