This scoping review maps the current development landscape of wearable hand exoskeletons for motor rehabilitation, covering publications from 2010 to October 2025 across four major databases. A total of 134 articles were included, identifying 116 unique devices validated with human subjects. The results show a clear predominance of soft structures over rigid or hybrid ones, with electric motors and tendon-driven mechanisms being the most frequent actuation and transmission choices. However, there is a lack of uniformity in the information on device metrics, such as total weight and system dimensions, or usability metrics, like donning and doffing times, which hinders the direct comparison of different technologies. This review synthesizes current trends and emphasizes the need for standardized validation protocols to advance toward effective clinical solutions.
Lagrangian strain imaging requires knowledge of the prior state of the deformation at its very core and can be accomplished if all three components of the displacement vector field can be accurately estimated. However, most ultrasound imaging is still performed along two-dimensional imaging planes, with the feasibility of computing both in plane displacement vectors (axial and lateral) key to Lagrangian deformation tracking and accumulation. Higher frame-rates can mitigate some of the impacts of out-of-plane motion in the elevational direction. Our group has been engaged in developing algorithms for Lagrangian deformation tracking and strain tensor imaging for carotid, cardiac and liver ablation imaging over the last two decades. Our algorithms currently implemented on a graphics processing unit estimate both axial and lateral displacement vectors accurately and with high spatial resolution using radiofrequency (RF) echo signals. We utilize a coarse-to-fine multilevel strategy and sinc-interpolation to both interpolate RF data at the final processing level for improved lateral displacement vector estimation and for accurate and unbiased sub-sample displacement estimation.
Effective neurorehabilitation necessitates a reliable assessment of the patient's sensorimotor status. Such assessment guides clinical decision-making and supports the evaluation of novel interventions. Yet, despite technological progress, it remains limited to clinical scales that are mostly based on subjective scoring systems and measurement protocols with poor reproducibility. Research on neuromechanical biomarkers-metrics of movement derived from kinematic, kinetic and physiological data-has demonstrated significant potential to advance towards more reliable assessment. However, their clinical validation and practical adoption remain limited, impeding the development of more objective, standardized, and clinically relevant measurement of movement. This article consolidates expert perspectives from a multidisciplinary workshop held at the 2024 International Conference on NeuroRehabilitation in La Granja (Spain), focusing on the development of reliable assessment tools useful in clinical practice. The emerging central topic is the urgent need for assessment tools that are grounded in validated measurements. To address this, we structure the discussion around three pillars:standardization, to ensure methodological consistency;transferability, to support generalization across populations and technologies; andshareability, to enable collaboration, validation, and scaling. For each pillar, we outline current challenges and potential steps towards them, proposing a roadmap for developing assessment frameworks that are suitable for real-world use.
Tissue metabolism represents a complex, integrated system governing the energetic and plastic homeostasis of organs through the coordinated interaction of cellular biochemistry and systemic vascular supply. This review synthesises the fundamental concept of tissue metabolism, bridging various terminologies such as the microcirculatory-tissue system and perfusion-metabolic coupling, to emphasise its dual nature: intracellular metabolic pathways and substrate delivery via microcirculation. We detail the key stages of cellular energy metabolism (glycolysis, oxidative decarboxylation, the Krebs cycle, and oxidative phosphorylation), highlighting their plasticity and central role in health and disease. The critical importance of systemic support, particularly the function of the microcirculatory bed, is examined, including its regulatory rhythms. Further, we explore major pathological disruptions of tissue metabolism, including diabetes, mitochondrial dysfunction, neurodegenerative diseases, cancer metabolism (Warburg effect), ageing, and ischemia-hypoxia, underscoring the systemic consequences of metabolic imbalance. The second part provides a comprehensive overview of advanced instrumental methods for assessing tissue metabolism. A comparative analysis of radionuclide, magnetic resonance, mass spectrometry, optical, and other techniques is presented, outlining their physical principles, measured parameters, spatial-temporal resolutions, key advantages, limitations, and primary applications. This methodological survey demonstrates the powerful, multimodal toolkit available for non-invasive, high-resolution investigation of metabolic processes from the molecular to the organ level, offering critical insights for both fundamental research and clinical diagnostics.
Recent advancements in wearable, non-invasive neurophysiological sensors have increased interest in applying Human factors (HF) research beyond controlled laboratory settings. HF research aims to objectively quantify individuals' (alone or working in team) mental and emotional states to ensure safety, maintain good performances and prevent risks. These devices enable real time, unobtrusive monitoring of individuals in real-world environments, overcoming the limitations of traditional subjective assessments. Unlike self-reports, neurophysiological signals provide objective, real-time data, allowing for a more accurate and continuous understanding of mental states. This review examines the latest advancements over the past ten years in real-time monitoring of the most studied and operationally relevant mental states, including mental workload (MW), stress, attention, fatigue, drowsiness, and teamwork, by analyzing studies that contribute to research advancing toward real-world application direction. While wearable biosensors were used as one criterion for selecting studies, this review extends beyond device usage to explore crucial methodological aspects relevant to real-world applications, such as data quality, customized processing steps, and the robustness of results compared to traditional laboratory-grade devices. Thus, this review analyzes 123 articles exploring current advancements in real-time monitoring of MW, stress, attention, mental fatigue, drowsiness, and teamwork using wearable devices. The findings provide a comprehensive overview of methodology advancement toward real-time, objective individuals monitoring in real world settings.
Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.
Gliomas are heterogeneous primary central nervous system tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in histopathology is underway as samples are digitized into whole-slide images (WSIs). Deep learning (DL) shows great potential for glioma diagnosis, subtyping, grading, and prognosis. This review summarizes recent advances in WSI-based DL models, covering data preprocessing, model architectures, and translational performance. Model evolution has progressed from CNNs (e.g. ResNet, capturing local features via convolution) to Transformers (e.g. Vision transformer, modeling global dependencies via self-attention), hybrid architectures, and large language model. Typical pipelines include quality control, stain normalization, patch extraction, feature integration, and prediction. Public datasets such as The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium serve as key resources. On various datasets (public and institutional), a glioma-specific model ResNet-50 achieved AUC 0.983 for subtype classification; a Vision Transformer model reached AUC 0.960 for molecular typing; a hybrid model, ROAM, attained AUC 0.990; and the pan-cancer hybrid model called CHIEF achieved AUC 0.9397 for diagnosis. Attention heatmaps and Shapley Additive exPlanations provide interpretability by linking model outputs to histologic regions. Novel multimodal fusion integrates genomic, proteomic, radiologic, and clinical data to enhance prediction and uncover biological relevance. Future research may focus on enhancing model generalizability and predictive accuracy via advanced architectures, developing lightweight models for resource-limited settings to facilitate clinical translation.
Deployable diagnostics are necessary for the control and treatment of infectious diseases, with significant unmet needs revealed during the COVID-19 pandemic. Nucleic acid diagnostics remain among the most sensitive and specific forms of detection, yet their reliance on laboratory equipment and trained personnel limits their deployment in resource limited settings. CRISPR-based diagnostics are uniquely positioned to enable rapid, affordable, and highly accurate nucleic acid testing at both the point-of-care and the point-of-need. In this review, we discuss advances toward deployable CRISPR-based diagnostics. We begin by examining innovations in sample processing methods, emphasizing strategies that reduce equipment requirements and enhance compatibility across diverse sample types and pathogens. We then explore developments in one-pot isothermal and amplification-free approaches, comparing the benefits and tradeoffs associated with each, as well as multiplexing strategies for simultaneous detection of multiple pathogens. Finally, we consider additional factors that impact assay deployability, including reagent lyophilization to minimize cold chain dependence and readout technologies that enable detection in resource-limited settings. We conclude by outlining remaining challenges and opportunities for future progress.
Microgravity research has revealed significant alterations in cancer and stem cell biology with direct implications for therapeutic development. Cancer cells exposed to real or simulated microgravity undergo cytoskeletal disorganization, reduced adhesion, and spontaneous three-dimensional spheroid formation that closely mirrorsin vivotumor microenvironments, including hypoxia and drug resistance. These tumor models provide physiologically relevant systems for studying metastasis, therapeutic sensitivity, and tumor-stroma interactions. Simultaneously, microgravity modulates stem cell behavior, enhancing self-renewal, delaying differentiation, and improving secretory and immunomodulatory profiles. Stem cells cultured under microgravity conditions show modulated secretory and immunomodulatory profiles, including changes in regenerative and anti-inflammatory factors, which may influence tumor microenvironments and hold promise to support anti-tumor responses. Additionally, microgravity enables scaffold-free tissue modeling and reduces reliance on animal models in preclinical studies. Taken together, microgravity emerges as a powerful platform for developing stem-cell-based cancer therapies, enabling exosome engineering, secretome optimization, and immune effector generation. The integration of cancer and stem cell research in microgravity thus provides a translational framework for next-generation strategies in precision oncology and regenerative medicine.
Cervical cancer remains a significant global health concern, particularly in low- and middle-income countries (LMICs), where access to efficient and affordable screening methods is limited. Conventional diagnostic methods often have low sensitivity and specificity, are costly and invasive, limiting their widespread use. This creates an urgent need for an affordable, non-invasive, reliable diagnostic approach. Biomarker detection has become increasingly popular due to its less invasive and cost-effective nature. Among various platforms, aptamer-based biosensors or aptasensors have emerged as a promising diagnostic tool that offers high sensitivity, specificity, robustness, and portability at low cost, which makes them highly suitable for LMICs and point-of-care (POC) applications. This review critically explores both current and novel biomarkers associated with cervical cancer and their roles in carcinogenesis. While aptasensors have demonstrated utility in detecting markers such as carcinoma embryonic antigen, vascular endothelial growth factor, cancer antigen 125, HPV-16 E7, HPV-16 L1, miR-21 and protein tyrosine kinase 7, there is now an emerging development focus on aptasensor-based detection of biomarkers specific to cervical cancer, such as SCC antigen, YKL-40, high mobility group box chromosomal protein 1, CXCL13, heterogeneous nuclear ribonucleoprotein A1, and p16INKa/ki-67. This review highlights cancer biomarkers relevant to cervical cancer, utilizing existing aptamer biosensing data to underscore their diagnostic potential and support earlier disease intervention strategies. Furthermore, multiplexed detection strategies enhance diagnostic specificity for cervical cancer and minimize reliance on technical expertise and advanced infrastructure. Overall, this review emphasizes the emerging role of aptamer-based biosensors in improving early detection and effective screening of cervical cancer, particularly in LMICs and POC settings, highlighting the need for further research to address current gaps in cervical cancer-specific aptamer biosensor development.
Neurological disorders, such as Parkinson's disease and stroke, often lead to neurogenic dysphagia, a swallowing disorder that compromises nutrition and increases the risk of malnutrition, aspiration of food, and even death. Although instrumental assessments remain the gold standard for evaluating swallowing function, their invasiveness, cost, and reliance on specialized clinical facilities limit their routine use. A systematic search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science, including only peer-reviewed studies using non-invasive wearable devices for assessing and monitoring dysphagia in neurological disorders. The initial search identified 1669 records, and an updated search retrieved 128 additional records. After screening, 24 studies met the inclusion criteria. Accelerometers and surface electromyography sensors were the most frequently used, either alone or in multimodal configurations. Multimodal approaches generally provided more accurate and comprehensive assessments than single-sensor systems. However, validation with gold-standard methods was performed in only half of the studies, and considerable heterogeneity was observed in sensor placement, study protocols, and signal-processing strategies. Wearable sensing technologies represent a non-invasive and scalable solution for assessing neurogenic dysphagia. To enable clinical translation, future research should focus on standardized methodologies, robust validation, and the integration of artificial intelligence for more precise swallowing assessment in individuals with neurological disorders.
Soft pneumatic actuators promise gentle, body-safe assistance, yet many fail at the moment of integration: inflation alters geometry, increases profile, and stresses seams and ports. This topical review reframes actuator selection through a structure-first lens that links how an actuator is built to how it deforms and how it embeds in garments. Actuators are classified by structural architecture, deformation behavior, and fabrication method, and evaluated against four integration criteria central to wearable systems: geometric compatibility, fabrication scalability, system integration readiness, and actuation simplicity. A staged selection pathway is proposed and presented as a literature-informed mapping table that links reported wearable application contexts to dominant integration priorities and a practical structural-class starting point. Planar sheet designs can preserve a thin, predictable pressurized envelope at modest pressures when seam paths, constraint layers, and attachment features are co-designed. Pouch and bladder actuators are thin at rest, simple to fabricate, and readily integrated with textiles, but commonly bulge under load without external constraint; envelope control and port or manifold routing frequently limit garment integration. Fiber-constrained actuators deliver high specific force but often require rigid end terminations and elevated pressures. Segmented elastomers provide rich kinematics through chamber layout while tending toward bulging and routing burden in multi-segment formats. Mechanical metamaterials realize geometry-programmed motion when hinge fidelity and pattern alignment are maintained; durability of compliant joints and consistent crease formation set current limits. The resulting synthesis identifies practical priorities for wearable use: preserve thin profiles under load, embed stitchable or bondable attachment features, document reproducible process windows, minimize dead volume, routing, and valve count with compact pneumatic architectures, and target low-pressure operation compatible with lightweight hardware. Framing integration in structural terms standardizes comparison across actuator classes, clarifies the narrow feasible design space for compact, body-conforming devices, and supports deliberate actuator choice for biomedical wearables.
The investigation of neural circuit dynamics faces a fundamental challenge: existing tools cannot simultaneously achieve cellular resolution, millimeter-depth penetration, and compatibility with freely behaving subjects. Electrophysiology offers temporal precision at depth, while advanced microscopy provides superb resolution, but is physically constrained to superficial layers or head-fixed preparations. In this review, we propose that implantable photonic devices are emerging as the critical solution to address this challenge. We first critically examine the evolution of electrophysiology and microscopic imaging, establishing their inherent trade-offs. We then detail how integrated photonic probes, leveraging semiconductor innovations like single-photon avalanche diode (SPAD) arrays andμLEDs, enable optical sensing and manipulation deep within the brain of behaving animals. By establishing frameworks to compare important performance and synthesizing the latest research, we provide analysis of this transformative shift. Finally, we outline the multidisciplinary challenges in scaling, thermal management, data processing, and biocompatibility, which must be overcome to realize the full potential of implantable photonics as a new paradigm for closed-loop neuroscience and clinical translation.
Maintaining quiet balance is a simple yet indispensable motor task that is crucial for human health and well-being, as it is essential in everyday activities. The loss of balance ability-due to aging and traumatic or degenerative disorders-is associated with an increased risk of falls and injuries. Therefore, it is critical to identify the underlying neural control mechanisms of healthy and impaired posture and to develop better and more targeted ways to predict, prevent, and treat postural disorders. After providing context on relevant biomechanical and neurophysiological aspects of quiet stance, this review offers a detailed analysis of the different computational models used in existing literature to explain unimpaired and impaired quiet balance. We identify six key aspects of modeling upright stance which are currently discussed in the literature, and we delve into the different neuromotor control hypotheses that have been proposed. We critically discuss the reviewed findings and provide our perspective on how upright and unperturbed posture may be an independent neuromotor control primitive for human motion, or in this case, non-motion. Furthermore, we highlight the need for continued investigation into impaired balance models to advance rehabilitation, enhance assistive robotics, and improve the prediction and mitigation of balance impairments. In conclusion, our review uniquely synthesizes the modeling challenges of quiet stance and provides a critical overview of the most debated controversies in this field. By analyzing such controversies, the review establishes a reference point for future research efforts aimed to clarify and further comprehend upright unperturbed stance.
Deep learning for cross-subject electroencephalography (EEG) decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.
Microwave breast imaging (MBI) is gaining attention as a promising, non-invasive modality for breast imaging, leveraging safe, low-power radiofrequency signals to eliminate ionizing radiation and compression related discomfort associated with mammography. Recent advancements in prototype systems have demonstrated potential in imaging breast tissues and detecting lesions, including cancer. Despite these encouraging results, clinical adoption of MBI faces significant challenges. This review examines recent advancements in MBI prototype developments, analyses the current evidence regarding its efficacy and diagnostic accuracy, and discusses challenges that impede its clinical adoption, including technological, regulatory, and economic barriers. Our synthesis aims to provide insights into the potential of MBI in transforming breast imaging practices and to outline pathways for further research and development toward clinical integration.
Coronary artery disease is often treated with vascular stents to restore the blood flow. Recently, there has been growing interest in biodegradable metal stents, especially those made from magnesium (Mg). This review starts by explaining the complex pathophysiology of atherosclerosis and the current treatment options. It then discusses stents, including their potential benefits, capabilities, and limitations, as they represent the gold standard for percutaneous coronary intervention in treating atherosclerosis. Given the vital role of stents, we provide a thorough review of the manufacturing techniques involved, such as wire forming, subtractive manufacturing, and additive manufacturing, with a specific focus on stents made from Mg alloys. Mg-based stents are considered highly promising biodegradable options because their natural absorption helps alleviate long-term complications after surgery, such as chronic inflammation and hypersensitivity reactions, which can result from the persistent presence of foreign materials in the artery. Additionally, the long-term presence of traditional stents increases the risk of late stent thrombosis and can impair vascular healing and vasodilatory function. Furthermore, this review offers an overview of various innovative stent systems designed for different clinical applications that are currently undergoing research and clinical trials. The past reviews offer perspectives on alloy design, fabrication, or surface modification in isolation. Here, in this comprehensive framework we connect material composition, structural design, and surface engineering with biological outcomes andin vivoperformance. This work concludes with suggestions for future research directions, including investigations into optimizing manufacturing procedures and processing parameters to produce Mg-based stents with improved quality.
Neural activity encompasses both rhythmic oscillations and aperiodic background dynamics, reflecting complex brain function beyond traditional rhythm-centric views. Theaperiodiccomponent, once considered noise, is now recognised as a meaningful signal indicative of excitation-inhibition balance and intrinsic neural timescales. Here, we review advanced signal processing frameworks, including spectral parameterisation and burst detection algorithms, that disentangle theseperiodicandaperiodiccomponents. We critically evaluate evidence suggesting thataperiodicparameters track neurodevelopment and serve as candidate biomarkers for Alzheimer's Disease and Parkinsonism. Furthermore, we highlight how neuroengineering interventions, such as deep brain stimulation and acupuncture, actively modulate these features. Crucially, we address the current methodological heterogeneity in the field, proposing a standardisedroadmapfor estimation to resolve conflicting interpretations. These findings underscore the complementary roles of oscillatory and aperiodic dynamics, offering novel avenues for closed-loop brain-computer interfaces and personalized neurotherapeutics.
Ultrasonic power transfer (UPT) is gaining traction for wireless energy delivery to implants and wearables because it combines centimeter-scale penetration with compact receivers. This review takes a transducer-centric view of UPT and organizes the field across bulk piezoelectrics (including lead-free options), piezoelectric micromachined ultrasonic transducers, capacitive micromachined ultrasonic transducers, flexible polymer platforms and magnetostrictive transducers. We connect working mechanisms and structural configurations to practical performance-operating frequency ranges, bandwidth, link efficiency and output power, and miniaturization trade-offs-and summarize representative demonstrations in biomedical systems. System-level considerations for integration (acoustic/electrical matching and rectification) and bidirectional links (including backscatter and active telemetry) are highlighted to show how a single acoustic carrier can deliver power and data through tissue. We conclude with challenges (attenuation and misalignment, materials reliability and packaging, and scaling to millimeter/sub-millimeter form factors) and opportunities that draw on materials innovations (metamaterials, lead-free ceramics, flexible polymers) and machine-learning-assisted co-design for robust, efficient through-tissue operation. Together, this transducer-focused synthesis provides a practical map from device physics and fabrication choices to system performance and emerging applications.
