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

Computational modeling and simulation (CM&S) is a powerful tool that can be used to support the development, evaluation, and regulatory authorization of medical devices. CM&S can provide valuable insights into device performance, safety, and effectiveness, as well as reduce the need for animal or human testing. Computational models are, however, idealized digital representations that often have many assumptions and need to be credible before they are used in decision making that could incur patient harm. While the medical device community has made great strides to advance the use of CM&S, a number of challenges remain. To begin addressing these challenges, the US Food and Drug Administration (FDA) and the Medical Device Innovation Consortium (MDIC) co-sponsored theFDA/MDIC Symposium on Computational Modeling and Simulationon April 16-17, 2024 in College Park, Maryland, USA, where attendees from around the world convened to hear from leaders in the field through a unique blend of invited presentations and interactive panel discussions. The symposium agenda covered several major themes, including credibility considerations for CM&S used across the medical device total product life cycle, practical examples of performing model credibility assessment, and the use of CM&S for clinical decision making and the emerging areas ofin silicoclinical trials and digital twins. The objective of this article is to summarize the major takeaways of the symposium. We first provide an overview of the invited presentations followed by summaries of the topics covered during the interactive panel discussions. In doing so, we highlight the main takeaways and identify areas in which panelists had shared perspectives or differences of opinion. Next, we present the results of a survey conducted at the symposium that sought attendees' perspectives on different aspects of medical device CM&S. Finally, we conclude by summarizing the major outcomes of the symposium, including areas where more work and investment are needed to advance the field.

This review explores the current state of eversion robotics in the context of colonoscopy, given the need for less invasive, more patient-friendly screening technologies. Conventional colonoscopy often leads to discomfort and patient reluctance, contributing to delayed diagnoses and high colorectal cancer mortality rates. Eversion robots, also known as vine robots or soft growing robots are soft, pressure-driven devices that extend by everting from the tip whilst offering a promising option by enabling frictionless advancement and potentially pain-free procedures. We examine the key challenges and opportunities in adapting eversion robots for clinical endoscopic use, focusing on material selection, actuation, steering, and payload delivery. From the literature, thermoplastic polyurethane emerges as the most viable material for the robot's sleeve due to its airtightness, biocompatibility, suitability for heat or ultrasonic welding, and availability in highly flexible thin layers. Tip-steering mechanisms are identified as the most effective strategies for navigation, allowing high flexibility without increasing the wall thickness of the robot, as required in alternative approaches using distributed actuation mechanisms. The review also evaluates strategies for integrating functional tools at the tip of the robot, concluding that cap-free designs provide superior adaptability to the varying colon diameter, preserve compressibility, and keep tip friction to a minimum, unlike cap-based payload delivery methods. By consolidating current research and identifying pathways for innovation, this review supports the development of eversion soft robots as a next-generation solution for minimally invasive colorectal diagnostics and therapy.

One of the most common sites of cancer metastasis is the bone, with a large proportion of both breast cancer and prostate cancer patients who develop metastases having involvement of the skeleton. The prognosis for patients with bone metastases is poor as there are limited effective treatment options. The lack of reliable models to recapitulate the native bone microenvironment during the drug discovery process, has resulted in a poor understanding of the biological processes that enable and drive metastases, and difficulty evaluating potential treatments. Animal models that have been successful in the genesis of cutting-edge treatments for primary cancer have not been able to be used for treatments for metastases, in part due to their inability to accurately recapitulate the native human microenvironment. Consequently, the development and availability of drugs to treat and/or prevent bone metastases are lacking. The last decade has seen an increase in the development and use of three dimensional (3D) scaffolds in cell culture to investigate cancer, as these models have demonstrated similar cancer cellular growth and gene/protein expression to the native human microenvironment. The majority of 3D cell culture systems for studying cancer processes comprise a soft matrix, which fails to accurately replicate the rigidity and structural complexity of bone tissue, which further alters the behaviour of cells. This systematic literature review focuses on the research to date on the development and characterisation of solid scaffolds that have been used for the purpose of in vitro investigation of bone metastases. It highlights the importance of materials testing to characterise the models, ensuring they have a composition, structure and strength similar to bone, to give appropriate mechanical cues to cells, while also highlighting the biological validation completed to ensure the models are an accurate representation of the metastatic niche.

Spasticity, a type of hypertonia characterized by a velocity-dependent increase in muscle tone, is associated with damage to the brain and/or spinal cord in different neurological conditions. However, secondary non-neurological factors, such as soft tissue changes, can complicate the assessment and differentiation of the underlying causes. Accurate assessment is crucial for effective treatment planning, with clinicians relying on passive movement to grade the "feel" of the spastic limb. This review aims to identify and evaluate the feasibility of spasticity models or simulations for clinical teaching. Models based on human spastic limbs were examined, with no restrictions on specific conditions or populations. A comprehensive search of four databases and gray literature was conducted to identify relevant studies. Criteria for inclusion focused on model development, data, and evaluation processes. Study selection and data extraction were carried out by independent reviewers, and data synthesis was performed by systematically mapping model properties, methods, and utility. The quality of the studies was assessed using an adapted framework for health technology assessments. The findings highlight opportunities for the development of simulation models to support training. However, significant limitations to the existing evidence base limit the feasibility of developing spasticity models based on existing literature. .

Diabetes mellitus is a widespread chronic disease with steadily growing prevalence and associated comorbidities. Current treatment of diabetes can be quite cumbersome for the patients, leading to global efforts to develop a fully-automated artificial pancreas. Such a device will need to employ some form of blood glucose prediction, as well as algorithms to detect meal intake and various physical activities. Many methods were already developed for these tasks, enabling the meta-analysis of the current state of the art. First, an overview of glycemic control strategies and sensors is provided. Then, the relevant studies are introduced and described prior to the meta-analysis. The resulting meta-analysis quantifies the accuracy of prediction models for the various prediction horizons (15, 30, 45, 60, and 120 minutes) and the performance of meal and physical activity detection models using sensitivity and metrics related to false-positivity. Following the observed patterns across the prediction horizons, a novel approach to evaluating the physiological plausibility of prediction methods is proposed. The baseline state-of-the-art model performance for said tasks is estimated. Finally, a discussion about the current issues in the research of diabetic technologies and their potential solutions is conducted.

Bioimpedance measurements have gained significant attention due to their ability to assess body composition, muscle health, and internal physiological states without the need for intrusive procedures. This review paper explores the advancements and applications of bioimpedance technology, a non-invasive and cost-effective method for real-time monitoring of physiological parameters and physical activities. It discusses key measurement modalities such as bioelectrical impedance analysis (BIA), electrical impedance myography (EIM), and electrical impedance tomography (EIT), highlighting their unique advantages and applications. It also examines the role of biopotential electrodes, both polarizable and non-polarizable, in ensuring accurate physiological measurements. Despite challenges such as low spatial resolution, motion artifacts and sensitivity to electrode placement, the review highlights promising solutions. These include the integration of hybrid sensor systems, machine learning algorithms for signal interpretation, and the development of wearable and flexible electronics. The paper concludes by emphasizing the growing potential of bioimpedance technology in fields such as sports science, rehabilitation, personalized healthcare, fitness monitoring, and human-machine interaction, suggesting a future where continuous physiological monitoring becomes seamlessly embedded in daily life.

The hair growth is a highly controlled biological process, governed by the cycles of the hairs in anagen (growth), catagen (regression) and telogen (rest period). Breaks in it could lead to hair thinning and loss, that is why a response there's certainly a need for effective treatment. Current methods such as topical minoxidil, oral finasteride and modern techniques including platelet-rich plasma therapy or hair transplantation work by improving the functioning of hair follicles to prolong their growth phase. In this instance, the aim of this article is to mainly review about emerging mechanobiological strategies such as electrical stimulation, microneedling, microcurrent therapy conjugated with nanotechnology, low-frequency techniques that provide a context for futuristic non-invasive approaches.

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder after Alzheimer's disease, affecting over ten million people worldwide. It is characterized by motor symptoms such as tremors, rigidity, and gait disturbances. Current treatments focus on alleviating symptoms and slowing down brain degeneration, but no cure exists, leading to a progressive decline in patients' quality of life. Three-dimensional (3D) bioprinting has emerged as a powerful technique for developing constructs that engineer neural tissues with complexities mimicking physiological conditions. These constructs can serve as vehicles for controlled drug delivery and potential substitutes for neurodegeneration. This article aims to compile new research data and review the current state of PD models engineered by 3D bioprinting, focusing on the desired biochemical features of bioinks for cell protection during printing, cell behavior, and differentiation into 3D constructs. Additionally, it discusses the physical, mechanical, and chemical characterization of bioprinted scaffolds and the importance of post-printing assessment to ensure printability, shape fidelity, appropriate construct degradation, and extracellular matrix production rates for developing complex 3D bioprinted constructs. Finally, it proposes opportunities for models that can be used to study novel therapeutics and immunomodulatory responses in tissues engineered for PD and other neurodegenerative diseases.

Emerging evidence suggests that adipose tissue is not just a fat depot but a metabolically active organ that plays a central role in connecting obesity with its comorbidities. Understanding the complex interactions between adipocytes and neighboring cell types in obesity requires models that accurately replicate adipocyte behavior within their natural environment. Three-dimensional (3D) adipocyte cultures mimic the native tissue microenvironment by incorporating the spatial architecture as well as cell-cell and cell-extracellular matrix interactions presentin vivo, offering improved platforms for (patho)physiological adipose tissue modeling. 3D models of adipose tissue dysfunction enable the study of complex cellular crosstalk, such as adipocyte cancer cell interactions in breast, colorectal, bone, and pancreatic cancers; epicardial and pericardial adipocyte-myocardial cell dynamics in obesity-related cardiac dysfunction; and adipocyte-hepatocyte interactions in the development of non-alcoholic fatty liver disease, among other critical pathophysiological processes. In this review, we first discuss 3D models of adipose tissue and current strategies for mimicking the obesogenic microenvironment, including dietary stimulation of hyperlipidemia and hyperglycemia, as well as the incorporation of oxygen gradients, proinflammatory cytokines, and immune cells. Secondly, we examine 3D co-culture platforms that incorporate disease-associated/dysfunctional adipocytes with various cell types, such as cancer cells, cardiac cells, hepatocytes, immune cells, endothelial cells (EC), and fibroblasts, to model intercellular and interorgan crosstalk in obesity. Lastly, we provide insights into enhancing the physiological relevance of dysfunctional adipose tissue models and their co-culture systems while discussing future directions in tissue engineering aimed at improving clinical translation and reducing obesity related complications and mortality.

Cellular biophysical properties are increasingly linked to disease development, including muscular dystrophy, cancer, glaucoma, and other conditions. Transcription profiles of various types have been utilized to elucidate the relationship between genes and their regulatory functions. While spatial transcriptomics creates high-resolution maps of gene regulation in tissues, it does not capture the mechanically coordinated responses of cells based on their transcriptional profiles and cell locations. Mechanobiology, on the other hand, studies how cells perceive and respond to forces but lacks genomic information. In this paper, we explore the emergence of an integrative platform called spatial mechano-transcriptomics. This method combines spatial transcriptomic and mechanical data from the same cells within a timeframe suitable for diagnostic procedures. Spatial mechano-transcriptomics examines the relationship between physical properties, including cell membrane stiffness, and differences in the cell's transcription profile, which could be used to predict disease states. Integrating spatial and mechanical observations has the potential to revolutionize precision diagnostics and lead to the development of new therapeutics, resulting in significant advances in biomedical research.