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

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.

Since the concept of aggregation-induced emission (AIE) was first coined by Tang and co-workers, AIE-active luminogens (AIEgens) have drawn widespread attention among chemists and biologists due to their unique advantages such as high fluorescence efficiency, large Stokes shift, good photostability, low background noise, and high biological visualization capabilities in the aggregated state, surpassing conventional fluorophores. A growing number of AIEgens have been engineered to possess multifunctional properties, including near-infrared emission, two-photon absorption, reactive oxygen species (ROS) generation and photothermal conversion, making them suitable for deep-tissue imaging and phototherapy. AIEgens show great potential in biomedical applicationsin vitroandin vivo. However, despite the favorable photophysical stability and ROS/heat generation capability in the aggregated state, limitations including uncontrolled size, low targeting efficiency, and unexpected dispersion in physiological environments have hindered their biomedical applications. The combination of AIEgens with lipids offers a simple, promising, and widely adopted solution to these challenges. This review article provides an overview of the synthesis methods of AIEgen-lipid nanostructures and their applications in the biomedical engineering field, aiming to serve as a guideline for developing these AIEgens-lipid nanostructures with promising biological applications.

Mechanical stimuli have multiple effects on cell behavior, affecting a number of cellular processes including orientation, proliferation or apoptosis, migration and invasion, the production of extracellular matrix proteins, the activation and translocation of transcription factors, the expression of different genes such as those involved in inflammation and the reprogramming of cell fate. The recent development of cell stretching devices has paved the way for the study of cell reactions to stretching stimuliin-vitro, reproducing physiological situations that are experienced by cells in many tissues and related to functions such as breathing, heart beating and digestion. In this work, we review the highly-relevant contributions cell stretching devices can provide in the field of mechanobiology. We then provide the details for the in-house construction and operation of these devices, starting from the systems that we already developed and tested. We also review some examples where cell stretchers can supply meaningful insights into mechanobiology topics and we introduce new results from our exploitation of these devices.

The role of bioelectricity in regulating various physiological processes has attracted increasing scientific interest in implementing exogenous electrical stimulations as a therapeutic approach. In particular, electrical stimuli are used clinically in pre-/post-surgery patient care for the musculoskeletal tissues. The reported potential of electric fields (EF) to regulate bone cell homeostasis and kineticsin vitrohas further provoked more studies in this field of research. Various customised apparatuses have been developed, and a range of parameters for the applied EFs have been investigatedin vitrowith bone cells or mesenchymal stem cells. Additionally, biomaterials with conductive or piezo-electric properties have been designed to complement the enhancing effects of the EF on bone regeneration. Despite much research, there remained a significant gap in knowledge due to the diverse range of EF parameters available. Mathematical models are built to facilitate further understanding and zero in on an effective range of EF parametersin silico. However, the diverse range of EF parameters, experimental conditions, and reported analytical output of different works of literature were reported to possess significant variance, making it challenging to accurately model the fieldin silico. This review categorises the existing experimental approaches and the parameters used to distinguish the potential variables that apply to mathematical modelling. Furthermore, we will discuss existing modelling approaches and models available in the literature. With this, we will concisely highlight the need to categorise EF parameters, osteogenic differentiation initiators and research output.

The design and optimization of bone scaffolds are critical for the success of bone tissue engineering (BTE) applications. This review paper provides a comprehensive analysis of computational optimization methods for bone scaffold architecture, focusing on the balance between mechanical stability, biological compatibility, and manufacturability. Finite element method (FEM), computational fluid dynamics (CFD), and various optimization algorithms are discussed for their roles in simulating and refining scaffold designs. The integration of multiobjective optimization and topology optimization has been highlighted for developing scaffolds that meet the multifaceted requirements of BTE. Challenges such as the need for consideration of manufacturing constraints and the incorporation of degradation and bone regeneration models into the optimization process have been identified. The review underscores the potential of advanced computational tools and additive manufacturing techniques in evolving the field of BTE, aiming to improve patient outcomes in bone tissue regeneration. The reliability of current optimization methods is examined, with suggestions for incorporating non-deterministic approaches andin vivovalidations to enhance the practical application of optimized scaffolds. The review concludes with a call for further research into artificial intelligence-based methods to advance scaffold design and optimization.

Schizophrenia is a complex and chronic psychiatric disorder that significantly impacts patients' quality of life. Ranking 12th among 310 diseases and injuries that result in disability, the number of patients suffering from schizophrenia continues to rise, emphasizing the urgent need for developing effective treatments. Despite the availability of effective antipsychotic drugs, over 80% of patients taking oral antipsychotics experience relapses, primarily caused by non-adherence as the high dosing frequency is required. In this review, we discuss about schizophrenia, its incidence, pathological causes, influencing factors, and the challenges of the current medications. Specifically, we explore nanocrystal technology and its application to paliperidone, making it one of the most successful long-acting antipsychotic drugs introduced to the market. We highlight the clinical advantages of paliperidone nanocrystals, including improved adherence, efficacy, long-term outcomes, patient satisfaction, safety, and cost-effectiveness. Additionally, we address the physicochemical factors influencing the drug's half-life, which crucially contribute to long-acting medications. Further studies on nanocrystal-based long-acting medications are crucial for enhancing their effectiveness and reliability. The successful development of paliperidone nanocrystals holds great promise as a significant approach for drug development, with potential applications for other chronic disease management.

Retinal implantation of an electrode array is an emerging treatment for vision loss caused by outer-retinal degeneration. This article collects and analyses harms associated with the treatment reported in the peer-reviewed literature, thus enabling informed decision-making by patients, clinicians, researchers, engineers, and policymakers. We searched MEDLINE, Embase, and clinical trials registries for peer-reviewed journal articles reporting harms outcomes. We extracted data from articles including study design, definitions of 'serious adverse event', and timing of adverse events. We applied the McMaster tool to these articles to assess the risk of bias in harms assessment and reporting. Our searches returned 585 abstracts. We reviewed the full text of 59 articles describing 11 different devices. McMaster scores ranged from 3 to 12 (maximum 15; higher scores indicate less risk). We compiled a comprehensive list of all serious and non-serious adverse events associated with retinal implantation. Several harms were common across devices. Our meta-analysis showed that serious adverse events are log-uniformly distributed throughout follow-up. Improved reporting and further clinical studies are needed to develop a reliable safety profile of retinal implantation. Our findings will help guide the design, conduct, and reporting of future clinical trials of retinal implantation and other emerging treatments for vision loss. (PROSPERO registration: CRD42022308123.).

Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard's envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.

This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.

Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.