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.

Operationalizing and digitizing person-centered daily functioning: a case for functionomics.

An ever-increasing amount of data on a person's daily functioning is being collected, which holds information to revolutionize person-centered healthcare. However, the full potential of data on daily functioning cannot yet be exploited as it is mostly stored in an unstructured and inaccessible manner. The integration of these data, and thereby expedited knowledge discovery, is possible by the introduction of functionomics as a complementary 'omics' initiative, embracing the advances in data science. Functionomics is the study of high-throughput data on a person's daily functioning, that can be operationalized with the International Classification of Functioning, Disability and Health (ICF).A prerequisite for making functionomics operational are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper illustrates a step by step application of the FAIR principles for making functionomics data machine readable and accessible, under strictly certified conditions, in a practical example. Establishing more FAIR functionomics data repositories, analyzed using a federated data infrastructure, enables new knowledge generation to improve health and person-centered healthcare. Together, as one allied health and healthcare research community, we need to consider to take up the here proposed methods.

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.

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.

Single-arm diagnostic electrocardiography with printed graphene on wearable textiles.

Stimulated by the COVID-19 outbreak, the global healthcare industry better acknowledges the necessity of innovating novel methods for remote healthcare monitoring and treating patients outside clinics. Here we report the development of two different types of graphene textile electrodes differentiated by the employed fabrication techniques (i.e., dip-coating and spray printing) and successful demonstration of ergonomic and truly wearable, single-arm diagnostic electrocardiography (SADE) using only 3 electrodes positioned on only 1 arm. The performance of the printed graphene e-textile wearable systems were benchmarked against the "gold standard" silver/silver chloride (Ag/AgCl) "wet" electrodes; achieving excellent correlation up to ∼ 96% and ∼ 98% in ECG recordings (15 s duration) acquired with graphene textiles fabricated by dip-coating and spray printing techniques, respectively. In addition, we successfully implemented automatic detection of heartrate of 8 volunteers (mean value: 74.4 bpm) during 5 min of static and dynamic daily activities and benchmarked their recordings with a standard fingertip photoplethysmography (PPG) device. Heart rate variability (HRV) was calculated, and the root means successive square difference (rMMSD) metric was 30 ms during 5 min of recording. Other cardiac parameters such as R-R interval, QRS complex duration, S-T segment duration, and T-wave duration were also detected and compared to typical chest ECG values.

How Technical Innovations May Help to Prevent Drug Shortages in Switzerland.

In this work, we investigated the technical feasibility of 'on-demand' production of selected drugs to cover their demand for a time window of 90 days. We focused on two sub-processes 'automated chemical synthesis' and 'formulation in micropellets'  to enable personalized dosing. The production of drugs 'on-demand' is challenging, important, but also attractive. Switzerland could thus gain access to an additional instrument for increasing resilience for supply-critical drugs. The biggest challenge in the case study presented here is the scalability of automated chemical synthesis and the application range of micropellet formulations.

The future of personalized care for vascular patients: an industry perspective.

Healthcare has entered a brave new world in the early part of the 21st century: the landscape has changed and continues to change rapidly, evolving at a rate as never seen before. Fuelled by technological advancement, big data analytics, and the explosion of apps and sensors, as well as by telemedicine and remote monitoring needs driven by the COVID-19 pandemic, the healthcare ecosystem is metamorphosing literally before our eyes. So, what is the role for the Medtech industry as healthcare systems reshape themselves to address emerging patients' needs and desires, and how can the use of data and novel technologies be leveraged to bring about the kind of change needed to deliver truly holistic patient care?

Personalized vascular healthcare: insights from a large international survey.

Fragmentation of healthcare systems through limited cross-speciality communication and intermittent, intervention-based care, without insight into follow-up and compliance, results in poor patient experiences and potentially contributes to suboptimal outcomes. Data-driven tools and novel technologies have the capability to address these shortcomings, but insights from all stakeholders in the care continuum remain lacking. A structured online questionnaire was given to respondents (n = 1432) in nine global geographies to investigate attitudes to the use of data and novel technologies in the management of vascular disease. Patients with coronary or peripheral artery disease (n = 961), physicians responsible for their care (n = 345), and administrators/healthcare leaders with responsibility for commissioning/procuring cardiovascular services (n = 126) were included. Narrative themes arising from the survey included patients' desire for more personalized healthcare, shared decision-making, and improved communication. Patients, administrators, and physicians perceived and experienced deficiencies in continuity of care, and all acknowledged the potential for data-driven techniques and novel technologies to address some of these shortcomings. Further, physicians and administrators saw the 'upstream' segment of the care journey-before diagnosis, at point of diagnosis, and when determining treatment-as key to enabling tangible improvements in patient experience and outcomes. Finally, despite acceptance that data sharing is critical to the success of such interventions, there remains persistent issues related to trust and transparency. The current fragmented care continuum could be improved and streamlined through the adoption of advanced data analytics and novel technologies, including diagnostic and monitoring techniques. Such an approach could enable the refocusing of healthcare from intermittent contacts and intervention-only focus to a more holistic patient view.

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.

Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.

Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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.

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.

Wearable Technology and How This Can Be Implemented into Clinical Practice.

PURPOSE OF REVIEW: Our day-to-day life is saturated with health data that was previously out of reach. Over the last decade, new devices and fitness technology companies are attempting to tap into this data, uncovering a treasure trove of useful information that, when applied correctly, has the potential to revolutionize the way we approach healthcare and chronic conditions like asthma, especially in the wake of the COVID-19 pandemic. RECENT FINDINGS: By harnessing exciting developments in personalization, digitization, wellness, and patient engagement, care providers can improve health outcomes for our patients in a way we have never been able to do in the past. While new technologies to capture individual health metrics are everywhere, how can we use this information to make a real difference in our patients' lives? Navigating the complicated landscape of personal wearable devices, asthma inhaler sensors, and exercise apps can be daunting to even the most tech savvy physician. This manuscript will give you the tools necessary to make lasting changes in your patients' lives by exposing them to a world of usable, affordable, and relatable health technology that resonates with their personal fitness and wellness goals. These tools will be even more important post-COVID-19, as the landscape of clinical outpatient care changes from mainly in-person visits to a greater reliance on telemedicine and remote monitoring.

Respiratory healthcare by design: Computational approaches bringing respiratory precision and personalised medicine closer to bedside.

Precision medicine represents a potentially powerful means to alleviate the growing burden of chronic respiratory diseases. To realise its potential, however, we need a systems level understanding of how biological events (signalling pathways, cell-cell interactions, tissue mechanics) integrate across multiple spatial and temporal scales to give rise to pathology. This can be achieved most practically in silico: a paradigm that offers tight control over model parameters and rapid means of testing and generating mechanistic hypotheses. Patient-specific computational models that can enable identification of pathological mechanisms unique to patients' (omics, physiological, and anatomical) profiles and, therefore, personalised drug targets represent a major milestone in precision medicine. Current patient-based models in literature, especially medical devices, cardiac modelling, and respiratory medicine, rely mostly on (partial/ordinary) differential equations and have reached relatively advanced level of maturity. In respiratory medicine, patient-specific simulations mainly include subject scan-based lung mechanics models that can predict pulmonary function, but they treat the (sub)cellular processes as "black-boxes". A recent advance in simulating human airways at a cellular level to make clinical predictions raises the possibility of linking omics and cell level data/models with lung mechanics to understand respiratory pathology at a systems level. This is significant as this approach can be extended to understanding pathologies in other organs as well. Here, I will discuss ways in which computational models have already made contributions to personalised healthcare and how the paradigm can expedite clinical uptake of precision medicine strategies. I will mainly focus on an agent-based, asthmatic virtual patient that predicted the impact of multiple drug pharmacodynamics at the patient level, its potential to develop efficacious precision medicine strategies in respiratory medicine, and the regulatory and ethical challenges accompanying the mainstream application of such models.

Data to diagnosis in global health: a 3P approach.

BACKGROUND: With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. METHODS: To address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is Physician Assist Filters (PAF) that transform unwieldy multi-sensor time series data into summarized patient/disease specific trends in steps of progressive precision as demanded by the doctor for patient's personalized condition at hand and help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient's medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using SVM machine learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 min of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India. RESULTS: The results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer. CONCLUSION: The RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps, thereby providing the advantages of three A's: availability, affordability, and accessibility in the global health scenario.

Security controls in an integrated Biobank to protect privacy in data sharing: rationale and study design.

BACKGROUND: With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement. METHODS: Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis. RESULTS: We established the T ohoku M edical M egabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections. CONCLUSION: The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.

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.

[The significance of biobanks for clinical development]. / Die Bedeutung von Biobanken für die klinische Entwicklung.

Access to samples in biobanks and collection of samples for evaluation of biomarkers in clinical trials are an essential basis for the identification and development of biomarkers. From the perspective of a research-based pharmaceutical company identification of biomarkers and the accompanying diagnostics are an essential prerequisite for the further evolution of personalised healthcare-and the key to more effective and efficient healthcare. Research-based pharmaceutical companies can basically use four types of biobanks: biobanks of university hospitals, commercial providers, collaborative groups and company-owned biobanks. Areas of application, arising from the use of biobanks in the context of clinical development, are collection of prevalence data, evaluation of biomarker stability in different disease stages, technical validation of assays, an optimized course of clinical studies by focusing on defined, biomarker-stratified groups of patients and pharmacogenetic research. Challenges are, in particular, the availability of clinically annotated samples and tissue matching blood samples, in addition to sample quality, number and amount. An acceptable legal and regulatory framework, as well as the positive perception of biomarker data by politicians and the public, are important prerequisites for translational research for identification of biomarkers in clinical studies. Also, the early establishment of research alliances between academia and the pharmaceutical industry are required to transfer research results in new strategies for prevention, diagnosis and treatment of patients.

Differential microfluidic sensor on printed circuit board for biological cells analysis.

Coulter principal based resistive pulse sensor has been demonstrated as an important platform in biological cell detection and enumeration since several decades ago. Recently, the miniaturized micro-Coulter counter has attracted much attention due to its advantages in point of care diagnostics for on chip detection and enumeration of rare cells, such as circulating tumor cells. In this paper, we present a microfluidic cytometer with differential amplifier based on Coulter principle on a SU-8 coated printed circuit board substrate. The electrical current changes induced by the blockage of the microparticles in the sensing aperture are calibrated by polystyrene particles of standard size. Finally, HeLa cells are used to evaluate the performance of the proposed device for enumeration of biological samples. The proposed cytometer is built upon the cheap and widely available printed circuit board substrate and shows its great potential as personalized healthcare monitor.

Soluble biomarkers development in osteoarthritis: from discovery to personalized medicine.

CONTEXT: Specific soluble biomarkers could be a precious tool for diagnosis, prognosis and personalized management of osteoarthritic (OA) patients. OBJECTIVE: To describe the path of soluble biomarker development from discovery to clinical qualification and regulatory adoption toward OA-related biomarker qualification. METHODS AND RESULTS: This review summarizes current guidance on the use of biomarkers in OA in clinical trials and their utility at five stages, including preclinical development and phase 1 to phase 4 trials. It also presents all the available regulatory requirements. CONCLUSIONS: The path through the adoption of a specific soluble biomarker for OA is steep but is worth the challenge due to the benefit that it can provide.