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

Advancements in assistive robots have significantly transformed healthcare procedures in recent years. Clinical continuum robots have enhanced minimally invasive surgeries, offering benefits to patients such as reduced blood loss and a short recovery time. However, controlling these devices is difficult due to their limited accuracy in three-dimensional deflections and challenging localization, particularly in confined spaces like human internal organs. Consequently, there has been growing research interest in employing miniaturized soft growing robots, a promising alternative that provides enhanced flexibility and maneuverability. In this work, we extensively investigated issues concerning their designs and interactions with humans in clinical contexts. We took insights from the open challenges of the generic soft growing robots to examine implications for miniaturization, actuation, and biocompatibility. We proposed technological concepts and provided detailed discussions on leveraging existing technologies, such as smart sensors, haptic feedback, and artificial intelligence, to ensure the safe and efficient deployment of the robots. Finally, we offer an array of opinions from a biomedical engineering perspective that contributes to advancing research in this domain for future research to transition from conceptualization to practical clinical application of miniature soft growing robots.

Background: Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in radiotherapy, where accuracy is critical. The purpose of the study is to present patterns and develop topics in the application of AI to improve the precision of anatomical diagnosis, delineation of organs, and therapeutic effectiveness in radiation and radiological imaging.

Methods: We performed a bibliometric analysis of scholarly articles in the fields starting in 2014. Through an examination of research output from key contributing nations and institutions, an analysis of notable research subjects, and an investigation of trends in scientific terminology pertaining to artificial intelligence in radiology and radiotherapy. Furthermore, we examined software solutions based on artificial intelligence in these domains, with a specific emphasis on extracting anatomical features and recognizing organs for the purpose of treatment planning.

Results: Our investigation found a significant surge in papers pertaining to artificial intelligence in the fields since 2014. Institutions such as Emory University and Memorial Sloan-Kettering Cancer Center made substantial contributions to the development of the United States and China as leading research-producing nations. Key study areas encompassed adaptive radiation informed by anatomical alterations, MR-Linac for enhanced vision of soft tissues, and multi-organ segmentation for accurate planning of radiotherapy. An evident increase in the frequency of phrases such as "radiomics," "radiotherapy segmentation," and "dosiomics" was noted. The evaluation of AI-based software revealed a wide range of uses in several subdisciplinary fields of radiation and radiology, particularly in improving the identification of anatomical features for treatment planning and identifying organs at risk.

Conclusions: The incorporation of AI in anatomical diagnosis in radiological imaging and radiotherapy is progressing rapidly, with substantial capacity to transform the precision of diagnoses and the effectiveness of treatment planning.

Mechanical forces of various kinds and magnitudes are crucial to cell and tissue development. At the cell level, mechanotransduction refers to the processes that turn mechanical triggers into a biochemical response. Just like most biological processes, many of these mechanical forces are not static but change dynamically over time. Therefore, to further our fundamental understanding of dynamic mechanotransduction, it is paramount that we have a good toolbox available to specifically trigger and analyze every step of the way from force to phenotype. While many individual studies have described such tools, to our knowledge, a comprehensive overview providing guidance on which tool to use to address specific questions is still lacking. Thus, with this review, we aim to provide an overview and comparison of available dynamic cell stimulation techniques. To this end, we describe the existing experimental techniques, highlighting and comparing their strengths and weaknesses. Furthermore, we provide a one-glance overview of the niches of mechanical stimulation occupied by the different approaches. We finish our review with an outlook on some techniques that could potentially be added to the toolbox in the future. This review can be relevant and interesting for a broad audience, from engineers developing the tools, to biologists and medical researchers utilizing the tools to answer their questions, or to raise new ones.

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.