Progress in biomedical engineering (Bristol, England)最新文献

Extrusion is the most popular bioprinting platform. Predictions of human tissue and whole-organ printing have been made for the technology. However, after decades of development, extruded constructs lack the essential microscale resolution and heterogeneity observed in most human tissues. Extrusion bioprinting has had little clinical impact with the majority of research directed away from the tissues most needed by patients. The distance between promise and reality is a result of technology hype and inherent design flaws that limit the shape, scale and survival of extruded features. By more widely adopting resolution innovations and softening its ambitions the biofabrication field could define a future for extrusion bioprinting that more closely aligns with its capabilities.

This review comprehensively analyzes the modern literature on including visual aids in diverse surgical assistant robotic systems. The review considered a deep analysis of diverse technical and scientific sources that provide precise information on how the more recent surgical systems, especially those considering robotic devices, perform automatic operations on patients. The search procedure and the corresponding analytics considered only those conditions where vision systems played a significant role in the surgical procedure, despite the type of end-effector and if only position or force were used as part of the feedback analysis. This review is organized considering the robot configuration, the type of end-effector, the vision systems considered for those cases, and the associated control actions, which must include the acquired image or video. The study analyzes the key contributions of the published cases. It provides a critical description of the advantages and shortcomings of the technological implementation of vision systems in surgical robotic devices. Finally, this review provides a general prospective view of ongoing research on vision aids for surgical robotic systems, which will become an ordinary actor in future surgical systems.

Polydopamine (PDA), as a material mimicking the adhesive proteins of mussels in nature, has emerged as a strong candidate for developing novel antibacterial and anti-inflammatory materials due to its outstanding biomimetic adhesion, effective photothermal conversion, excellent biocompatibility and antioxidant capabilities. This review discussed in detail the intricate structure and polymerization principles of PDA, elucidated its mechanisms in combating bacterial infections and inflammation, as well as explored the innovative use of PDA-based composite materials for antibacterial and anti-inflammatory applications. By providing an in-depth analysis of PDA's capabilities and future research directions, this review addresses a crucial need for safer, more effective, and controllable antimicrobial and anti-inflammatory strategies, which aim to contribute to the development of advanced materials that can significantly impact public health.

The issue of antibiotic resistance is increasing with time because of the quick rise of microbial strains. Overuse of antibiotics has led to multidrug-resistant, pan-drug-resistant, and extensively drug-resistant bacterial strains, which have worsened the situation. Different techniques have been considered and applied to combat this issue, such as developing new antibiotics, practicing antibiotic stewardship, improving hygiene levels, and controlling antibiotic overuse. Vaccine development made a substantial contribution to overcoming this issue, although it has been underestimated. In the recent era, reverse vaccinology has contributed to developing different kinds of vaccines against pathogens, revolutionizing the vaccine development process. Reverse vaccinology helps to prioritize better vaccine candidates by using various tools to filter the pathogen's complete genome. In this review, we will shed light on computational vaccine designing, immunoinformatic tools, genomic and proteomic data, and the challenges and success stories of computational vaccine designing.

Integrating biomedical electronic devices holds profound promise for advancements in healthcare and enhancing individuals' quality of life. However, the persistent challenges associated with the traditional batteries' limited lifespan and bulkiness hinder these devices' long-term functionality and consistent power supply. Here, we delve into the biology and material interfaces in self-powered medical devices by summarizing the intrinsic electric demands in humans, analyzing material and biological mechanisms for electricity generation and storage, and discussing the pathways toward self-chargeable powering. As a result, the current challenges in material designs and biological integrations emerged to shape the future directions in advancing self-powered medical devices. This paper calls on the community to integrate biology and material science to develop self-powering medical devices and improve their clinical prospects.

Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2-5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema. This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning (ML) approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies bioimpedance spectroscopy (BIS), Kinect sensor and ML integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas ML and artificial neural network achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. ML and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.

Traumatic brain injuries (TBIs) pose a significant health concern among the elderly population, influenced by age-related physiological changes and the prevalence of neurodegenerative diseases. Understanding the biomechanical dimensions of TBIs in this demographic is vital for developing effective preventive strategies and optimizing clinical management. This comprehensive review explores the intricate biomechanics of TBIs in the elderly, integrating medical and aging studies, experimental biomechanics of head tissues, and numerical simulations. Research reveals that global brain atrophy in normal aging occurs at annual rates of -0.2% to -0.5%. In contrast, neurodegenerative diseases such as Alzheimer's, Parkinson's, and multiple sclerosis are associated with significantly higher rates of brain atrophy. These variations in atrophy rates underscore the importance of considering differing brain atrophy patterns when evaluating TBIs among the elderly. Experimental studies further demonstrate that age-related changes in the mechanical properties of critical head tissues increase vulnerability to head injuries. Numerical simulations provide insights into the biomechanical response of the aging brain to traumatic events, aiding in injury prediction and preventive strategy development tailored to the elderly. Biomechanical analysis is essential for understanding injury mechanisms and forms the basis for developing effective preventive strategies. By incorporating local atrophy and age-specific impact characteristics into biomechanical models, researchers can create targeted interventions to reduce the risk of head injuries in vulnerable populations. Future research should focus on refining these models and integrating clinical data to better predict outcomes and enhance preventive care. Advancements in this field promise to improve health outcomes and reduce injury risks for the aging population.

With increasing age, motor performance declines. This decline is associated with less favorable health outcomes such as impaired activities of daily living, reduced quality of life, or increased mortality. Through regular assessment of motor performance, changes over time can be monitored, and targeted therapeutic programs and interventions may be informed. This can ensure better individualization of any intervention approach (e.g. by considering the current motor performance status of a person) and thus potentially increase its effectiveness with regard to maintaining current performance status or delaying further decline. However, in older adults, motor performance assessment is time consuming and requires experienced examiners and specific equipment, amongst others. This is particularly not feasible in care facility/nursing home settings. Wearable robotic devices, such as exoskeletons, have the potential of being used to assess motor performance and provide assistance during physical activities and exercise training for older adults or individuals with mobility impairments, thereby potentially enhancing motor performance. In this manuscript, we aim to (1) provide a brief overview of age-related changes of motor performance, (2) summarize established clinical and laboratory test procedures for the assessment of motor performance, (3) discuss the possibilities of translating established test procedures into exoskeleton-based procedures, and (4) highlight the feasibility, technological requirements and prerequisites for the assessment of human motor performance using lower limb exoskeletons.

Laser surgery is recognized as a highly effective method for managing retinal diseases. However, the thermal effects of the laser on different eye tissues are not entirely understood yet. In this context, computational modeling can be a useful tool to predict therapy outcomes. Accurate optical and thermal parameters of ocular tissues are crucial to correctly modeling the laser-tissue interactions. The present work aims to provide an easily accessible list of optical and thermal parameters for developing computational models involving ocular tissues. An extensive literature review was conducted to gather data on these parameters. The sources of data and the methodology used to calculate these parameters are analyzed in detail to ensure the reliability of the proposed values. In particular, this review focuses on density, specific heat, thermal conductivity, refractive index, and absorption coefficient, with optical properties referring to the 577 nm wavelength. The review underscores a common tendency to rely on pre-existing values when developing new computational models, often lacking clarity regarding selection criteria and data sources. This emphasizes the necessity for new experimental studies to improve the accuracy of ocular tissue properties.

Three-dimensional (3D) models, such as tumor spheroids and organoids, are increasingly developed by integrating tissue engineering, regenerative medicine, and personalized therapy strategies. These advanced 3Din-vitromodels are not merely endpoint-driven but also offer the flexibility to be customized or modulated according to specific disease parameters. Unlike traditional 2D monolayer cultures, which inadequately capture the complexities of solid tumors, 3D co-culture systems provide a more accurate representation of the tumor microenvironment. This includes critical interactions with mesenchymal stem/stromal cells (MSCs) and induced pluripotent stem cells (iPSCs), which significantly modulate cancer cell behavior and therapeutic responses. Most of the findings from the co-culture of Michigan Cancer Foundation-7 breast cancer cells and MSC showed the formation of monolayers. Although changes in the plasticity of MSCs and iPSCs caused by other cells and extracellular matrix (ECM) have been extensively researched, the effect of MSCs on cancer stem cell (CSC) aggressiveness is still controversial and contradictory among different research communities. Some researchers have argued that CSCs proliferate more, while others have proposed that cancer spread occurs through dormancy. This highlights the need for further investigation into how these interactions shape cancer aggressiveness. The objective of this review is to explore changes in cancer cell behavior within a 3D microenvironment enriched with MSCs, iPSCs, and ECM components. By describing various MSC and iPSC-derived 3D breast cancer models that replicate tumor biology, we aim to elucidate potential therapeutic targets for breast cancer. A particular focus of this review is the Transwell system, which facilitates understanding how MSCs and iPSCs affect critical processes such as migration, invasion, and angiogenesis. The gradient formed between the two chambers is based on diffusion, as seen in the human body. Once optimized, this Transwell model can serve as a high-throughput screening platform for evaluating various anticancer agents. In the future, primary cell-based and patient-derived 3D organoid models hold promise for advancing personalized medicine and accelerating drug development processes.