Progress in Biomedical Engineering

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.

When light is incident to a biological tissue surface, combinations of optical processes occur, such as reflection, absorption, elastic and non-elastic scattering, and fluorescence. Analysis of these light interactions with the tissue provides insight into the metabolic and pathological state of the tissue. Furthermore, in vivo diagnosis of diseases using optical spectroscopy enables in situ rapid clinical decisions without invasive biopsies. For in vivo scenarios, incident light can be delivered in a highly localized manner to tissue via optical fibers, which are placed within the working channels of minimally invasive clinical tools, such as endoscopes. There has been extensive development in the accuracy and specificity of these optical spectroscopy techniques since the earliest in vivo examples were published in the academic literature in the early '90s, and there are now commercially available systems that have undergone medical and clinical trials. In this review, several types of optical spectroscopy techniques (elastic optical scattering spectroscopy, fluorescence spectroscopy, Raman spectroscopy, and multimodal spectroscopy) for the diagnosis and monitoring of diseases states of tissue in an in vivo setting are introduced and explored. Examples of the latest and most impactful works for each technique are then critically reviewed. Finally, current challenges and unmet clinical needs are discussed, followed by future opportunities, such as point-based spectroscopies for robot-guided surgical interventions.

Interaction control presents opportunities for contact robots physically interacting with their human user, such as assistance targeted to each human user, communication of goals to enable effective teamwork, and task-directed motion resistance in physical training and rehabilitation contexts. Here we review the burgeoning field of interaction control in the control theory and machine learning communities, by analysing the exchange of haptic information between the robot and its human user, and how they share the task effort. We first review the estimation and learning methods to predict the human user intent with the large uncertainty, variability and noise and limited observation of human motion. Based on this motion intent core, typical interaction control strategies are described using a homotopy of shared control parameters. Recent methods of haptic communication and game theory are then presented to consider the co-adaptation of human and robot control and yield versatile interactive control as observed between humans. Finally, the limitations of the presented state of the art are discussed and directions for future research are outlined.

The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish ADL. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of upper limb prostheses. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and toward the engineering of human-centered artificial limbs.

Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.

Recording from a large neuronal population of neurons is a crucial challenge to unravel how information is processed by the brain. In this review, we highlight the recent advances made in the field of 'spike sorting', which is arguably a very essential processing step to extract neuronal activity from extracellular recordings. More specifically, we target the challenges faced by newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, we cover in depth the prominent problem of drifts (movements of the neurons with respect to the recording devices) and the current solutions to circumscribe it. In addition, we also review recent contributions making use of deep learning approaches for spike sorting, highlighting their advantages and disadvantages. Next, we highlight efforts and advances in unifying, validating, and benchmarking spike sorting tools. Finally, we discuss the spike sorting field in terms of its open and unsolved challenges, specifically regarding scalability and reproducibility. We conclude by providing our personal view on the future of spike sorting, calling for a community-based development and validation of spike sorting algorithms and fully automated, cloud-based spike sorting solutions for the neuroscience community.

With the maturity of surgical robotic systems based on traditional rigid-link principles, the rate of progress slowed as limits of size and controllable degrees of freedom were reached. Continuum robots came with the potential to deliver a step change in the next generation of medical devices, by providing better access, safer interactions and making new procedures possible. Over the last few years, several continuum robotic systems have been launched commercially and have been increasingly adopted in hospitals. Despite the clear progress achieved, continuum robots still suffer from design complexity hindering their dexterity and scalability. Recent advances in actuation methods have looked to address this issue, offering alternatives to commonly employed approaches. Additionally, continuum structures introduce significant complexity in modelling, sensing, control and fabrication; topics which are of particular focus in the robotics community. It is, therefore, the aim of the presented work to highlight the pertinent areas of active research and to discuss the challenges to be addressed before the potential of continuum robots as medical devices may be fully realised.

For decades, diabetes mellitus has been of wide concern with its high global prevalence, resulting in increasing social and financial burdens for individuals, clinical systems and governments. Continuous glucose monitoring (CGM) has become a popular alternative to the portable finger-prick glucometers available in the market for the convenience of diabetic patients. Hence, it has attracted much interest in various glucose sensing technologies to develop novel glucose sensors with better performance and longer lifetime, especially non-invasive or minimally invasive glucose sensing. Effort has also been put into finding biocompatible materials for implantable applications to achieve effective in vivo CGM. Here, we review the state-of-the-art researches in the field of CGM. The currently commercially available CGM technologies have been analyzed and a summary is provided of the potential types of recently researched non-invasive glucose monitors. Furthermore, the challenges and advances towards implantable applications have also been introduced and discussed, especially the novel biocompatible hydrogel aimed at minimizing the adverse impact from foreign-body response. In addition, a large variety of promising glucose-sensing technologies under research have been reviewed, from traditional electrochemical-based glucose sensors to novel optical and other electrical glucose sensors. The recent development and achievement of the reviewed glucose sensing technologies are discussed, together with the market analysis in terms of the statistical data for the newly published patents in the related field. Thus, the promising direction for future work in this field could be concluded.

Wearable robotics, also called exoskeletons, have been engineered for human-centered assistance for decades. They provide assistive technologies for maintaining and improving patients' natural capabilities towards self-independence and also enable new therapy solutions for rehabilitation towards pervasive health. Upper limb exoskeletons can significantly enhance human manipulation with environments, which is crucial to patients' independence, self-esteem, and quality of life. For long-term use in both in-hospital and at-home settings, there are still needs for new technologies with high comfort, biocompatibility, and operability. The recent progress in soft robotics has initiated soft exoskeletons (also called exosuits), which are based on controllable and compliant materials and structures. Remarkable literature reviews have been performed for rigid exoskeletons ranging from robot design to different practical applications. Due to the emerging state, few have been focused on soft upper limb exoskeletons. This paper aims to provide a systematic review of the recent progress in wearable upper limb robotics including both rigid and soft exoskeletons with a focus on their designs and applications in various pervasive healthcare settings. The technical needs for wearable robots are carefully reviewed and the assistance and rehabilitation that can be enhanced by wearable robotics are particularly discussed. The knowledge from rigid wearable robots may provide practical experience and inspire new ideas for soft exoskeleton designs. We also discuss the challenges and opportunities of wearable assistive robotics for pervasive health.

Untethered mobile microrobots have the potential to transform medicine radically. Their small size and wireless mobility can enable access to and navigation in confined, small, hard-to-reach, and sensitive inner body sites, where they can provide new ways of minimally invasive interventions and targeted diagnosis and therapy down to the cellular length scales with high precision and repeatability. The exponential recent progress of the field at the preclinical level raises anticipations for their near-future clinical prospects. To pave the way for this transformation to happen, however, the formerly proposed microrobotic system designs need a comprehensive review by including essential aspects that a microrobot needs to function properly and safely in given in vivo conditions of a targeted medical problem. The present review provides a translational perspective on medical microrobotics research with an application-oriented, integrative design approach. The blueprint of a medical microrobot needs to take account of microrobot shape, material composition, manufacturing technique, permeation of biological barriers, deployment strategy, actuation and control methods, medical imaging modality, and the execution of the prescribed medical tasks altogether at the same time. The incorporation of functional information pertaining each such element to the physical design of the microrobot is highly dependent on the specific clinical application scenario. We discuss the complexity of the challenges ahead and the potential directions to overcome them. We also throw light on the potential regulatory aspects of medical microrobots toward their bench-to-bedside translation. Such a multifaceted undertaking entails multidisciplinary involvement of engineers, materials scientists, biologists and medical doctors, and bringing their focus on specific medical problems where microrobots could make a disruptive or radical impact.

Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.

The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

Platelet rich plasma (PRP) is a suspension of bioactive factors and chemokine enriched plasma. Platelets are a distinctive source of membrane bound and soluble proteins that are released upon their activation. The higher count of platelets renders PRP with an array of tissue regenerative abilities. PRP can be employed in the form of platelet containing plasma, platelet lysate plasma, or in the form of a pre-gelled fibrin matrix. PRP has been an essential alternative source of growth factors in the healing and regeneration of various tissues, such as musculoskeletal, cardiovascular, and dermal tissue, with additional applications in other tissues, such as hepatic and neural. A wide range of preparative and isolation strategies have been developed for various forms of PRP at laboratory and commercial scales. Concomitantly, PRP has found its applicability as an active component in several tissue regenerative approaches, including 3D printed/bioprinted constructs, injectable hydrogels, and crosslinked scaffolds. This review focuses on the various forms of PRP and their preparation methods, the latest tissue engineering applications of PRP, and the various tissue-specific clinical trials and findings conducted using PRP. We have further discussed the optimizations required in the methods of preparation, delivery, and long-term storage of PRP. Therefore, this review seeks to benefit the scope of research on PRP-based therapeutic agents in tissue engineering by providing comprehensive insights into the widespread application. We envisage PRP could be instrumental in future patient-specific tissue engineering applications in both pre-clinical and clinical settings.

In the past decade, the use of three-dimensional (3D) bioprinting technology for the development of in vitro tissue models has attracted a great deal of attention. This is due to its remarkable precision in constructing different functional tissues and organs, enabling studies of their biology. In addition, this high-throughput technology has been extended to therapeutics, as it provides an alternative functional platform for rapid drug screening and disease modelling. Functional tissue models fabricated using 3D bioprinting mimic native tissues and help in the development of platforms for personalized drug screening and disease modelling due to their high throughput and ease of customization. Moreover, bioprinted 3D tissue models mimic native tissues more closely and provide added advantages over earlier conventional tissue models, such as monoculture, co-culture, explants, etc. In this context, this review article provides an overview of different bioprinted in vitro tissue models of skin, bone, neural tissue, vascular tissue, cartilage, liver and cardiac tissue. This article explores advancements and innovations in these models in terms of developing improved therapeutic interventions. Herein, we provide an insight into the development of different bioprinted tissue models for applications in drug screening and disease modelling. The needs and advantages of bioprinted tissue models as compared with conventional in vitro models are discussed. Furthermore, the different biomaterials, cell sources and bioprinting techniques used to develop tissue models are briefly reviewed. Thereafter, different bioprinted tissue models, namely skin, liver, vascular, cardiac, cartilage, bone and neural tissue, are discussed in detail with a special emphasis on drug screening and disease modelling. Finally, challenges and future prospects are highlighted and discussed. Taken together, this review highlights the different approaches and strategies used for the development of different 3D bioprinted in vitro tissue models for improved therapeutic interventions.

Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

Platelet rich plasma (PRP) is a suspension of bioactive factors and chemokine enriched plasma. Platelets are a distinctive source of membrane bound and soluble proteins that are released upon their activation. The higher count of platelets renders PRP with an array of tissue regenerative abilities. PRP can be employed in the form of platelet containing plasma, platelet lysate plasma, or in the form of a pre-gelled fibrin matrix. PRP has been an essential alternative source of growth factors in the healing and regeneration of various tissues, such as musculoskeletal, cardiovascular, and dermal tissue, with additional applications in other tissues, such as hepatic and neural. A wide range of preparative and isolation strategies have been developed for various forms of PRP at laboratory and commercial scales. Concomitantly, PRP has found its applicability as an active component in several tissue regenerative approaches, including 3D printed/bioprinted constructs, injectable hydrogels, and crosslinked scaffolds. This review focuses on the various forms of PRP and their preparation methods, the latest tissue engineering applications of PRP, and the various tissue-specific clinical trials and findings conducted using PRP. We have further discussed the optimizations required in the methods of preparation, delivery, and long-term storage of PRP. Therefore, this review seeks to benefit the scope of research on PRP-based therapeutic agents in tissue engineering by providing comprehensive insights into the widespread application. We envisage PRP could be instrumental in future patient-specific tissue engineering applications in both pre-clinical and clinical settings.

In the past decade, the use of three-dimensional (3D) bioprinting technology for the development of in vitro tissue models has attracted a great deal of attention. This is due to its remarkable precision in constructing different functional tissues and organs, enabling studies of their biology. In addition, this high-throughput technology has been extended to therapeutics, as it provides an alternative functional platform for rapid drug screening and disease modelling. Functional tissue models fabricated using 3D bioprinting mimic native tissues and help in the development of platforms for personalized drug screening and disease modelling due to their high throughput and ease of customization. Moreover, bioprinted 3D tissue models mimic native tissues more closely and provide added advantages over earlier conventional tissue models, such as monoculture, co-culture, explants, etc. In this context, this review article provides an overview of different bioprinted in vitro tissue models of skin, bone, neural tissue, vascular tissue, cartilage, liver and cardiac tissue. This article explores advancements and innovations in these models in terms of developing improved therapeutic interventions. Herein, we provide an insight into the development of different bioprinted tissue models for applications in drug screening and disease modelling. The needs and advantages of bioprinted tissue models as compared with conventional in vitro models are discussed. Furthermore, the different biomaterials, cell sources and bioprinting techniques used to develop tissue models are briefly reviewed. Thereafter, different bioprinted tissue models, namely skin, liver, vascular, cardiac, cartilage, bone and neural tissue, are discussed in detail with a special emphasis on drug screening and disease modelling. Finally, challenges and future prospects are highlighted and discussed. Taken together, this review highlights the different approaches and strategies used for the development of different 3D bioprinted in vitro tissue models for improved therapeutic interventions.

In this article we review the microfabrication approaches, with a focus on bioprinting and organ-on-chip technologies, used to engineer cardiac tissue. First, we give a brief introduction to heart anatomy and physiology, and the developmental stages of the heart from fetal stages to adulthood. We also give information on the cardiac tissue microenvironment, including the cells residing in the heart, the biochemical composition and structural organization of the heart extracellular matrix, the signaling factors playing roles in heart development and maturation, and their interactions with one another. We then give a brief summary of both cardiovascular diseases and the current treatment methods used in the clinic to treat these diseases. Second, we explain how tissue engineering recapitulates the development and maturation of the normal or diseased heart microenvironment by spatially and temporally incorporating cultured cells, biomaterials, and growth factors (GF). We briefly expand on the cells, biomaterials, and GFs used to engineer the heart, and the limitations of their use. Next, we review the state-of-the-art tissue engineering approaches, with a special focus on bioprinting and heart-on-chip technologies, intended to (i) treat or replace the injured cardiac tissue, and (ii) create cardiac disease models to study the basic biology of heart diseases, develop drugs against these diseases, and create diagnostic tools to detect heart diseases. Third, we discuss the recent trends in cardiac tissue engineering, including the use of machine learning, CRISPR/Cas editing, exosomes and microRNAs, and immune modeling in engineering the heart. Finally, we conclude our article with a brief discussion on the limitations of cardiac tissue engineering and our suggestions to engineer more reliable and clinically relevant cardiac tissues.

The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.

In this article we review the microfabrication approaches, with a focus on bioprinting and organ-on-chip technologies, used to engineer cardiac tissue. First, we give a brief introduction to heart anatomy and physiology, and the developmental stages of the heart from fetal stages to adulthood. We also give information on the cardiac tissue microenvironment, including the cells residing in the heart, the biochemical composition and structural organization of the heart extracellular matrix, the signaling factors playing roles in heart development and maturation, and their interactions with one another. We then give a brief summary of both cardiovascular diseases and the current treatment methods used in the clinic to treat these diseases. Second, we explain how tissue engineering recapitulates the development and maturation of the normal or diseased heart microenvironment by spatially and temporally incorporating cultured cells, biomaterials, and growth factors (GF). We briefly expand on the cells, biomaterials, and GFs used to engineer the heart, and the limitations of their use. Next, we review the state-of-the-art tissue engineering approaches, with a special focus on bioprinting and heart-on-chip technologies, intended to (i) treat or replace the injured cardiac tissue, and (ii) create cardiac disease models to study the basic biology of heart diseases, develop drugs against these diseases, and create diagnostic tools to detect heart diseases. Third, we discuss the recent trends in cardiac tissue engineering, including the use of machine learning, CRISPR/Cas editing, exosomes and microRNAs, and immune modeling in engineering the heart. Finally, we conclude our article with a brief discussion on the limitations of cardiac tissue engineering and our suggestions to engineer more reliable and clinically relevant cardiac tissues.

Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy and diseased status and examine briefly the role of augmentation methods. Finally, we discuss the trade-offs of four approaches for sampling digital cohorts and the associated potential for study bias with selecting specific patient distributions.

Polyurethanes (PUs) have properties that make them promising in biomedical applications. PU is recognized as one of the main families of blood and biocompatible materials. PU plays a vital role in the design of medical devices in various medical fields. The structure of PU contains two segments: soft and hard. Its elastomeric feature is due to its soft segment, and its excellent and high mechanical property is because of its hard segment. It is possible to achieve specific desirable and targeted properties by changing the soft and hard chemical structures and the ratio between them. The many properties of PU each draw the attention of different medical fields. This work reviews PU highlighted properties, such as biodegradability, biostability, shape memory, and improved antibacterial activity. Also, because PU has a variety of applications, this review restricts its focus to PU's prominent applications in tissue engineering, cardiovascular medicine, drug delivery, and wound healing. In addition, it contains a brief review of PU's applications in biosensors and oral administration.

Biomedical imaging is a fascinating, rich and dynamic research area, which has huge importance in biomedical research and clinical practice alike. The key technology behind the processing, and automated analysis and quantification of imaging data is mathematics. Starting with the optimisation of the image acquisition and the reconstruction of an image from indirect tomographic measurement data, all the way to the automated segmentation of tumours in medical images and the design of optimal treatment plans based on image biomarkers, mathematics appears in all of these in different flavours. Non-smooth optimisation in the context of sparsity-promoting image priors, partial differential equations for image registration and motion estimation, and deep neural networks for image segmentation, to name just a few. In this article, we present and review mathematical topics that arise within the whole biomedical imaging pipeline, from tomographic measurements to clinical support tools, and highlight some modern topics and open problems. The article is addressed to both biomedical researchers who want to get a taste of where mathematics arises in biomedical imaging as well as mathematicians who are interested in what mathematical challenges biomedical imaging research entails.

Facial muscles play an important role in a vast range of physiological functions, ranging from mastication to communication. Any disruption in their normal function may lead to serious negative effects on human well-being. A very wide range of medical disorders and conditions in psychology, neurology, psychiatry, and cosmetic surgery are related to facial muscles, and scientific explorations spanning over decades exposed many fascinating phenomena. For example, expansive evidence implicates facial muscle activation with the expression of emotions. Yet, the exact manner by which emotions are expressed is still debated: whether facial expressions are universal, how gender and cultural differences shape facial expressions and if and how facial muscle activation shape the internal emotional state. Surface electromyography (EMG) is one of the best tools for direct investigation of facial muscle activity and can be applied for medical and research purposes. The use of surface EMG has been so far restricted, owing to limited resolution and cumbersome setups. Current technologies are inconvenient, interfere with the subject normal behavior, and require know-how in proper electrode placement. High density electrode arrays based on soft skin technology is a recent development in the realm of surface EMG. It opens the door to perform facial EMG (fEMG) with high signal quality, while maintaining significantly more natural environmental conditions and higher data resolution. Signal analysis of multi-electrode recordings can also reduce crosstalk to achieve single muscle resolution. This perspective paper presents and discusses new opportunities in mapping facial muscle activation, brought about by this technological advancement. The paper briefly reviews some of the main applications of fEMG and presents how these applications can benefit from a more precise and less intrusive technology.

While radioguided surgery (RGS) traditionally relied on detecting gamma rays, direct detection of beta particles could facilitate the detection of tumour margins intraoperatively by reducing radiation noise emanating from distant organs, thereby improving the signal-to-noise ratio of the imaging technique. In addition, most existing beta detectors do not offer surface sensing or imaging capabilities. Therefore, we explore the concept of a stretchable scintillator to detect beta-particles emitting radiotracers that would be directly deployed on the targeted organ. Such detectors, which we refer to as imaging skins, would work as indirect radiation detectors made of light-emitting agents and biocompatible stretchable material. Our vision is to detect scintillation using standard endoscopes routinely employed in minimally invasive surgery. Moreover, surgical robotic systems would ideally be used to apply the imaging skins, allowing for precise control of each component, thereby improving positioning and task repeatability. While still in the exploratory stages, this innovative approach has the potential to improve the detection of tumour margins during RGS by enabling real-time imaging, ultimately improving surgical outcomes.

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012–1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In $75\%$ of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in $19\%$ of ISCTs. The specific software used was not reported in $14\%$ of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with $28\%$ of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only $19\%$ of the studies. In $97\%$ of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Wearable robotics, also called exoskeletons, have been engineered for human-centered assistance for decades. They provide assistive technologies for maintaining and improving patients' natural capabilities towards self-independence and also enable new therapy solutions for rehabilitation towards pervasive health. Upper limb exoskeletons can significantly enhance human manipulation with environments, which is crucial to patients' independence, self-esteem, and quality of life. For long-term use in both in-hospital and at-home settings, there are still needs for new technologies with high comfort, biocompatibility, and operability. The recent progress in soft robotics has initiated soft exoskeletons (also called exosuits), which are based on controllable and compliant materials and structures. Remarkable literature reviews have been performed for rigid exoskeletons ranging from robot design to different practical applications. Due to the emerging state, few have been focused on soft upper limb exoskeletons. This paper aims to provide a systematic review of the recent progress in wearable upper limb robotics including both rigid and soft exoskeletons with a focus on their designs and applications in various pervasive healthcare settings. The technical needs for wearable robots are carefully reviewed and the assistance and rehabilitation that can be enhanced by wearable robotics are particularly discussed. The knowledge from rigid wearable robots may provide practical experience and inspire new ideas for soft exoskeleton designs. We also discuss the challenges and opportunities of wearable assistive robotics for pervasive health.

In vitro neuronal models have become an important tool to study healthy and diseased neuronal circuits. The growing interest of neuroscientists to explore the dynamics of neuronal systems and the increasing need to observe, measure and manipulate not only single neurons but populations of cells pushed for technological advancement. In this sense, micro-electrode arrays (MEAs) emerged as a promising technique, made of cell culture dishes with embedded micro-electrodes allowing non-invasive and relatively simple measurement of the activity of neuronal cultures at the network level. In the past decade, MEAs popularity has rapidly grown. MEA devices have been extensively used to measure the activity of neuronal cultures mainly derived from rodents. Rodent neuronal cultures on MEAs have been employed to investigate physiological mechanisms, study the effect of chemicals in neurotoxicity screenings, and model the electrophysiological phenotype of neuronal networks in different pathological conditions. With the advancements in human induced pluripotent stem cells (hiPSCs) technology, the differentiation of human neurons from the cells of adult donors became possible. hiPSCs-derived neuronal networks on MEAs have been employed to develop patient-specific in vitro platforms to characterize the pathophysiological phenotype and to test drugs, paving the way towards personalized medicine. In this review, we first describe MEA technology and the information that can be obtained from MEA recordings. Then, we give an overview of studies in which MEAs have been used in combination with different neuronal systems (i.e. rodent 2D and three-dimensional (3D) neuronal cultures, organotypic brain slices, hiPSCs-derived 2D and 3D neuronal cultures, and brain organoids) for biomedical research, including physiology studies, neurotoxicity screenings, disease modeling, and drug testing. We end by discussing potential, challenges and future perspectives of MEA technology, and providing some guidance for the choice of the neuronal model and MEA device, experimental design, data analysis and reporting for scientific publications.