Current Opinion in Biomedical Engineering最新文献

User-machine interfaces map biological signals measured from the user to control commands for external devices. The mapping from biosignals to device inputs is performed by a decoding algorithm. Adaptation of both the user and decoder—co-adaptation—provides opportunities to improve the inclusivity and usability of interfaces for diverse users and applications. User learning leads to robust interface control that can generalize across environments and contexts. Decoder adaptation can personalize interfaces, account for day-to-day signal variability, and improve overall performance. Co-adaptation therefore creates opportunities to shape the user and decoder system to achieve robust and generalizable personalized interfaces. However, co-adaptation creates a two-learner system with dynamic interactions between the user and decoder. Engineering co-adaptive interfaces requires new tools and frameworks to analyze and design user-decoder interactions. In this article, we review adaptive decoding, user learning, and co-adaptation in user-machine interfaces, primarily brain-computer, myoelectric, and kinematic interfaces, for motor control. We then discuss performance criteria for co-adaptive interfaces and propose a game-theoretic approach to designing user-decoder co-adaptation.

Intracellular antibodies have been deployed as powerful research tools for the last 20 years for inhibition of proteins to convey specific information about protein function. Accordingly, intracellular antibodies have been used for target validation in oncology and were the first reagents to inhibit “undruggable” targets, such as RAS mutants and LMO2. Their versatility allows addition of effector functions to invoke cell phenotypes following target engagement inside cells. Moreover, the paratope–epitope interaction of intracellular antibodies has been recently exploited to develop small molecule surrogates. We will discuss the flexibility that intracellular antibodies provide for discovery research and for new generations of therapeutics in all clinical indications where an aberrant protein expression is involved (oncology, neurological disease, infection, inflammation).

Traumatic brain injury (TBI), caused by physical insults to the head, involves complex pathophysiological processes that damage the brain at multiple length scales. Unlike macroscale brain tissue damages, nanoscale cellular impairments, including neuron membrane integrity loss and mechanoporation, are elusive in experiments and necessitate the implementation of in silico atomic-level approaches, such as molecular dynamics (MD) simulations. MD studies have rapidly developed over the past decades, significantly enhancing our understanding in membrane dynamics and biomechanically plausible damage mechanisms induced by TBI. Hence, in this article, we will give an overview of recent MD membrane system models in the context of TBI and discuss the ongoing advancements as well as challenges in this research area.

Regenerative peripheral nerve interfaces (RPNI) and targeted muscle reinnervation (TMR) are surgical approaches for redirecting peripheral nerve growth to denervated muscle targets after amputation. These approaches have demonstrated promise at reducing post-amputation pain and are now frequently performed at the time of amputation. Both TMR and RPNI can also serve as bioamplifiers for efferent neural signals that once went to the lost limb. Through reinnervation of muscle and skin, patients may also afford meaningful afferent feedback. Accordingly, these surgical approaches can be beneficial for bidirectional prostheses. This review discusses recent literature on management of post-amputation pain, prosthetic control, and sensory feedback with each approach. We also discuss how these approaches can be incorporated into wearable systems to improve function in daily life.

Cancer continues to affect underserved populations disproportionately. Novel optical imaging technologies, which can provide rapid, non-invasive, and accurate cancer detection at the point of care, have great potential to improve global cancer care. This article reviews the recent technical innovations and clinical translation of low-cost optical imaging technologies, highlighting the advances in both hardware and software, especially the integration of artificial intelligence, to improve in vivo cancer detection in low-resource settings. Additionally, this article provides an overview of existing challenges and future perspectives of adapting optical imaging technologies into clinical practice, which can potentially contribute to novel insights and programs that effectively improve cancer detection in low-resource settings.

Numerous therapeutic approaches have been developed to enable interrogation and modulation of protein isoforms, but often require laborious experimental development or screening of binders to targets of interest. In this article, we focus on efficient, state-of-the-art computational methods to design both small molecule and protein-based binders to target proteins, and highlight recent generative artificial intelligence approaches to binder design, which represents the most promising direction to enable targeting and modulation of any protein state. Integrated with advances in protein-modifying architectures, the strategies described here may serve as the foundation for therapeutic development in the near future.

The cell nucleus plays a key role in cellular mechanoresponses. 3D genome organisation, gene expression, and cell behaviour, in general, are affected by mechanical force application to the nucleus, which is transmitted from the cellular environment via a network of interconnected cytoskeletal components. To effectively regulate cell responses, these cytoskeletal components must not only exert forces but also withstand external forces when necessary. This review delves into the latest research concerning how the cytoskeleton safeguards the nucleus from mechanical perturbations. Specifically, we focus on the three primary cytoskeletal polymers: actin, intermediate filaments, and microtubules, as well as their interactions with the cell nucleus. We discuss how the cytoskeleton acts as a protective shield for the nucleus, ensuring structural integrity and conveying context-specific mechanoresponses.

Complex tissue regeneration through biomaterial-based strategies has witnessed a substantial structural transformation to facilitate the attachment and migration of host cells. Smart biomaterials offer exceptional features by rearranging themselves into diverse conformations upon exposure to physical (e.g., magnetic, temperature, electric field, and light), chemical (e.g., pH and ionic strength), or biological (e.g., enzymes) stimuli. By engineering conventional biomaterials into three-dimensional smart porous constructs (i.e., grafts) with novel sensory materials through a range of chemical and physical processing routes, it is possible to mimic the diverse mechanical, biological, and physiochemical nature of bone tissue. The resulting smart grafts can efficiently deliver the appropriate signals and guide the stem cells to promote tissue regeneration. In addition, they regulate the release of various bioactive agents in response to external and internal stimuli while combatting infections at the wound sites. This review discusses numerous strategies to engineer synthetic polymers to yield stimuli-responsive smart grafts suitable for bone tissue engineering. Various additives have also been included, ranging from nanoparticles to biologically active agents responsible for the graft's smart function. Furthermore, the review highlights recent trends and developments, contemporary challenges, and future perspectives of smart stimuli-responsive grafts concerning bone tissue engineering.

The field of wearable robotics has seen major advances in recent years, largely owing to an intense focus on optimizing device behavior to accomplish a narrow set of objectives. This approach, however, ignores the end user's perceptions, which are often strongly held and may be key to accepting the technology. Consequently, user preference, which is capable of accounting for factors that are difficult to measure but important to the user, has recently emerged as a formally quantifiable outcome metric. In this perspective, we characterize the methods recently developed and employed to optimize for user preference, describe recent accomplishments in lower-limb wearable robotics research incorporating user preferences, highlight current challenges, and position preference as an important meta-criterion to guide the development of wearable robotic systems.

Wearable sensors offer a unique opportunity to study movement in ecological contexts—that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.