Current Opinion in Biomedical Engineering最新文献

Additive manufacturing, often known as three-dimensional (3D) printing, is driving significant progress in a diverse range of fields, such as engineering, manufacturing, food, and medicine. Realistic tissue models and organ transplantation can provide necessary innovative opportunities to tackle countless medical and health care obstacles. These can be achieved by incorporation of 3D printing into tissue engineering, using live cells encapsulated in natural or synthetic biomaterials. This evolution of 3D bioprinting has been the focus of our article. Here, we methodically discussed the current stance, history, techniques, materials, and taxonomy of 3D bioprinting along with the challenges encountered.

The development of a dexterous hand prosthesis that is controlled and perceived naturally by the amputee is a major challenge in biomedical engineering. Recent years have seen the rapid evolution of surgical techniques and technologies aimed at this purpose, the majority of which probe muscle electrical activity for control, and deliver electrical pulses to nerves for sensory feedback. Here, we report on the myokinetic interface concept that exploits magnetic field principles to achieve natural control and sensory feedback of an artificial hand. Like implantable myoelectric sensors, but using passive implants, localizing magnets implanted in independent muscles could allow monitoring their contractions and thus controlling the corresponding movements in the artificial hand in a biomimetic, direct, independent, and parallel manner. Selectively vibrating the magnets also offers a unique opportunity to study kinesthetic percepts in humans. The myokinetic interface opens new possibilities for interfacing humans with robotic technologies in an intuitive way.

The development of melt electrowriting (MEW) technology can print many sustainable medical materials into high-resolution scaffolds to be applied in tissue engineering and regenerative medicine. The printability of the MEW can be highly improved after tuning and avoiding the particular phenomena of jet lag, fiber shifting, jet pulsing, and fiber bridging. Different MEW devices are developed to produce scaffolds with complicated or hierarchical structures to mimic human tissues. It is believed that the MEW technology can be extended to many other medical applications in the following years.

Bacterial and viral pathogens are devastating to human health and well-being. In many regions, dozens of pathogen species and variants co-circulate. Thus, it is important to detect many different species and variants of pathogens in a given sample through multiplexed detection methods. CRISPR-based nucleic acid detection has shown to be a promising step towards an easy-to-use, sensitive, specific, and high-throughput method to detect nucleic acids from DNA and RNA viruses and bacteria. Here, we review the current state of multiplexed nucleic acid detection methods with a focus on CRISPR-based methods. We also look toward the future of multiplexed point-of-care diagnostics.

Trauma leading to severe hemorrhage and shock on average kills patients within 3–6 h after injury. With average prehospital transport times reaching 1–6 h in low- to middle-income countries, stopping the bleeding and reversing hemorrhagic shock is vital. First-generation intravenous hemostats rely on traditional drug delivery platforms, such as self-assembling systems, fabricated nanoparticles, and soluble polymers due to their active targeting, biodistribution, and safety. We discuss some challenges in translating these therapies to patients, as very few have successfully made it through preclinical evaluation in large animals, and none have translated to the clinic. Finally, we discuss the physiology of hemorrhagic shock, highlight a new low-volume resuscitant (LVR) PEG-20k, and end with considerations for the rational design of LVRs.

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.