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

Transcatheter aortic valve implantation (TAVI)-related post-operative complications remain significant clinical challenges, and current in-silico simulations fall short in predicting them accurately, limiting their clinical applicability. This scoping review evaluates the state of the art in TAVI computational modeling, identifying methodological gaps and proposing directions for refinement to enhance translational impact. Following PRISMA-ScR guidelines, 40 studies were included, with data extracted and summarized by evaluated outcomes. A quality assessment was performed using a 14-item rubric. Most studies focused on predicting paravalvular leak (65%) and conduction disturbances (20%). This review reveals substantial heterogeneity in modeling approaches, with limited standardization and varying degrees of validation. To improve clinical relevance, future efforts should prioritize model standardization, rigorous validation following ASME V&V guidelines, increased automation, and improved interpretability for clinical users. By ensuring robustness, efficiency, and clinical accessibility, in-silico models could transform TAVI outcome prediction and support personalized treatment planning, ultimately enhancing care standards in structural heart interventions.

In situcartilage engineering aims to repair damaged cartilage within the body by using biomaterials such as hydrogels, often loaded with regenerative cells to support tissue formation at the injury site. Hydrogels are promising candidates forin situcartilage repair due to their biocompatibility and tunable properties. Two major strategies have been explored to enhance their performance: mechanical reinforcement, through the incorporation of secondary structures to improve mechanical behavior and structural integrity; and growth factor delivery, to stimulate cell proliferation, differentiation, and extracellular matrix synthesis. This review first analyzes mechanical reinforcement and growth factor delivery separate, discussing their advantages, limitations, and gaps in the context ofin situapplications. It then highlights the emerging opportunity to combine these strategies within composite, cell-laden hydrogels, and critically examines the current studies, alongside the additional challenges in clinical translation that arises. Finally, future directions are proposed to guide the design and testing of composite hydrogels for more effective and translatablein situcartilage repair therapies.

In 2023, the 8th IFSO analysis reported 480 970 metabolic bariatric procedures worldwide, as an action against obesity, a pandemic affecting more than a billion people. Despite the well-documented risks associated with obesity and the potential health benefits after bariatric surgery (BS), many eligible patients avoid it, raising concerns about whether this is due to a lack of awareness or limitations in existing techniques. Indeed, this discrepancy prompts inquiries into how this trend can be reversed. Is this a lack of proper information to the eligible patients, or is it a conscious choice linked to the limitations of existing technology? This aspect highlights the urgent need for more patient-focused, advanced methodologies that enhance both surgical outcomes and accessibility. Bioengineering offers an innovative approach by personalising BS, encouraging patients to pursue a tailored care pathway. In the era of digital twins, artificial intelligence and virtual surgical planning, bioengineers could support both surgeons and patients, predicting individual success rates, with greater control over surgical outcomes. Some examples are reported in the scientific literature, offering additional information, such as the optimal reduction of stomach volume by varying the tube size in laparoscopic sleeve gastrectomy or adjusting the suture pattern in endoscopic sleeve gastroplasty. Computational models can also predict the mechanical stress and strain on the gastric wall, which is crucial for targeting the brain regions associated with satiety and thus facilitating the weight loss process. Moreover, emerging personalised virtual models are demonstrating significant potential to revolutionise BS, leading to more realistic and precise surgical planning. Therefore, how could these virtual approaches impact the evolution of BS? Which could be the next improvements provided by computational bioengineering in this field? This perspective underscores the importance of adopting and advancing computational bioengineering to address current limitations and enhance the global impact of BS.

Progress in prosthetics development have greatly improved everyday life of people with lower limb amputations. However, efficiently operating a prosthetic device requires a complex learning process, as users need to familiarize with the biomechanics of prosthetic-assisted gait. Both mental and physical workloads play a role in this process, with significant implications for prosthetic use and acceptance. In this perspective review, we explore the intricate relationships between these elements in lower limb prosthetic users and foster the discussion about the possible correlation between various metrics and their use in the fields of biomechanics, cognitive and physical fatigue. First, we describe all compensatory movements that may be performed during prosthetic-assisted gait. We then examine the two loads: mental workload (MWL) and physical load, which are both reflected in the metabolic cost required for prosthetic use. From this analysis, we envision that prosthetic gait could be regulated by different factors, namely, biomechanics, MWL and metabolism. The main goal of this perspective is to foster discussion across these three domains, defining all types of 'loads' imposed on the patient using objective and cross related descriptors. We argue that such descriptors should be analyzed through specific approaches and protocols in order to be quantified in absolute manner. Being able to univocally describe the single inference of these macro areas to bionic limbs use, could lead to new paradigms in the patient's rehabilitation process and in the design of future robotic prostheses.

Immersion in cold water alters physiological (including cardiovascular) state via complex interplay between external stressors (namely, hydrostatic pressure of ambient water and heat loss due to cold) and compensatory mechanisms in the body (namely, humoral and autonomic nervous system control). Prolonged immersion in cold water leads to life-threatening physiological states including death. In addition, rewarming can benefit or harm a casualty depending on the casualty's physiological state and compensatory reserve. However, technology for assessing the survivability of a casualty impacted by cold water immersion does not exist. Toward the overarching goal of fostering the development of next-generation triage and treatment guidance technology for resuscitation after cold water immersion, the goal of this paper is to help establish a comprehensive understanding of cardiovascular responses to cold water immersion and rewarming as well as relevant physiological measurement technologies which may enable status assessment in future implementations. We review literature on the influence of water immersion, exposure to cold, and rewarming on cardiovascular physiology. We summarize the existing findings into a comprehensive mechanistic understanding of typical cardiovascular responses to cold water immersion and rewarming through time. Then, we review literature on the physiological measurement and physiological signal analytics technologies applicable to cold water immersion settings. We conclude the paper with a perspective on outstanding challenges and opportunities pertaining to physiological sensing and analytics to enable autonomous assessment and treatment guidance for resuscitation after cold water immersion.

Microwave imaging (MWI) is a promising modality due to its non-invasive nature and lower cost compared to other medical imaging techniques. These characteristics make it a potential alternative to traditional imaging techniques. It has various medical applications, particularly explored in breast and brain imaging. Machine learning (ML) has also been increasingly used for medical applications. This paper provides a scoping review of the role of ML in MWI, focusing on two key areas: image reconstruction and classification. The reconstruction section discusses various ML algorithms used to enhance image quality, highlighting methods such as convolutional neural network and support vector machine. The classification section delves into the application of ML for distinguishing between different tissue types, including applications in breast cancer detection and neurological disorder classification. By analyzing the latest studies and methodologies, this review addresses the current state of ML-enhanced MWI and sheds light on its potential for clinical applications.

Background.Transcatheter pulmonary valve replacement (TPVR) has emerged as a less invasive alternative to surgical pulmonary valve replacement for patients with right ventricular outflow tract dysfunction, such is especially important for those individuals whom had previous cardiac surgical procedures. Recently, three-dimensional (3D) printing and finite element (FE) computational simulation technologies have been employed to enhance preoperative planning processes; however, their effectiveness and clinical significance remain to be fully validated. This systematic review aims to describe the applications and potential impacts of 3D printing and FE simulation technologies for TPVR in clinical practice.Methods.A systematic search of PubMed, Science Direct, Web of Science, and Google Scholar was conducted to identify studies using patient-specific 3D-printed models and FE simulations for preoperative planning and device performance testing.Results.From 289 identified articles, 28 met the inclusion criteria for this review. The quality assessment of the articles showed that the article selection process was adequate. The eligible studies demonstrated that both 3D printing and FE-based simulations have been primarily used to select the appropriate pulmonary valve size as well as predict the optimal placement; i.e. to avoid potential complications such as paravalvular leakage or pulmonary regurgitation. These technologies are generally used in complex congenital and adult-congenital cases. Additionally, these studies provide valuable insights into the mechanical performances of the transcatheter valves using patient-specific anatomies.Conclusion.3D-printed models and FE simulations have both demonstrated utilities in TPVR planning; by accurately reproducing a given patient's anatomy and allowing evaluations of potential device-tissue interactions. These tools thus allow for personalized treatments and also contribute to device innovations and development. Yet, further research in this field is required due to the noted limitations of current studies, including small sample sizes, insufficient standardization, and/or challenges in replicating the biomechanics of cardiac tissue.

Stroke is a leading cause of adult disability worldwide, with approximately 101 million survivors globally. Over 60% of these individuals live with from long-term, often lifelong, movement impairments that significantly hinder their ability to perform essential daily activities and maintain independence. Post-stroke movement disabilities are highly associated with structural and functional changes in motor descending pathways, particularly the corticospinal tract and other indirect motor pathways via the brainstem. For decades, neuroengineers have been working to quantitively evaluate the post-stroke changes of motor descending pathways, aiming to establish a precision prognosis and tailoring treatments to post-stroke motor impairment. However, a clear and practicable technique has not yet been established as a breakthrough to change the standard of care for current clinical practice. In this review, we outline recent progress in neuroimaging, neuromodulation, and electrophysiological approaches for assessing structural and functional changes of motor descending pathways in stroke. We also discuss their limitations and challenges, arguing the need of artificial intelligence and large multi-modal data registry for a groundbreaking advance to this important topic.

Image-guided tumor ablation (IGTA) has revolutionized modern oncological treatments by providing minimally invasive options that ensure precise tumor eradication with minimal patient discomfort. Traditional techniques such as ultrasound (US), computed tomography, and magnetic resonance imaging have been instrumental in the planning, execution, and evaluation of ablation therapies. However, these methods often face limitations, including poor contrast, susceptibility to artifacts, and variability in operator expertise, which can undermine the accuracy of tumor targeting and therapeutic outcomes. Incorporating deep learning (DL) into IGTA represents a significant advancement that addresses these challenges. This review explores the role and potential of DL in different phases of tumor ablation therapy: preoperative, intraoperative, and postoperative. In the preoperative stage, DL excels in advanced image segmentation, enhancement, and synthesis, facilitating precise surgical planning and optimized treatment strategies. During the intraoperative phase, DL supports image registration and fusion, and real-time surgical planning, enhancing navigation accuracy and ensuring precise ablation while safeguarding surrounding healthy tissues. In the postoperative phase, DL is pivotal in automating the monitoring of treatment responses and in the early detection of recurrences through detailed analyses of follow-up imaging. This review highlights the essential role of DL in modernizing IGTA, showcasing its significant implications for procedural safety, efficacy, and patient outcomes in oncology. As DL technologies continue to evolve, they are poised to redefine the standards of care in tumor ablation therapies, making treatments more accurate, personalized, and patient-friendly.

Microfluidic-based sperm selection is an essential tool recently introduced into assisted reproductive technologies. Conventional approaches such as swim-up and density-gradient centrifugation (DGC) are widely used, however, they lack selectivity and limit the necessary sperm amount in the sample. Moreover, the DGC method has been reported to damage the sperm's DNA, whilst the emerging microfluidic devices offer a soft and flexible way to selectively sort various volumes of raw sperm samples. The flexibility of the discussed technology is associated with the channel architectures based on different sorting mechanisms. In particular, motility-based sorting devices are generally applied for rapid sperm selection without cell damaging by reactive oxygen species. Non-motile sperm samples can be separated from non-sperm cells by inertial microfluidics. The most promising approach to sperm selection has been presented by rheotaxis-based chips, shown to closely mimic the female reproductive tract. In this review, we discuss the key aspects of the chip design according to the underlying mechanisms. The microfluidic chips' fabrication issues and challenges have also been highlighted.