Annual Review of Biomedical Engineering最新文献

Kidney disease is a global health crisis affecting more than 850 million people worldwide. In the United States, annual Medicare expenditures for kidney disease and organ failure exceed $81 billion. Efforts to develop targeted therapeutics are limited by a poor understanding of the molecular mechanisms underlying human kidney disease onset and progression. Additionally, 90% of drug candidates fail in human clinical trials, often due to toxicity and efficacy not accurately predicted in animal models. The advent of ex vivo kidney models, such as those engineered from induced pluripotent stem (iPS) cells and organ-on-a-chip (organ-chip) systems, has garnered considerable interest owing to their ability to more accurately model tissue development and patient-specific responses and drug toxicity. This review describes recent advances in developing kidney organoids and organ-chips by harnessing iPS cell biology to model human-specific kidney functions and disease states. We also discuss challenges that must be overcome to realize the potential of organoids and organ-chips as dynamic and functional conduits of the human kidney. Achieving these technological advances could revolutionize personalized medicine applications and therapeutic discovery for kidney disease.

There is nothing like a global pandemic to motivate the need for improved respiratory treatments and mucosal vaccines. Stimulated by the COVID-19 pandemic, pulmonary aerosol drug delivery has seen a flourish of activity, building on the prior decades of innovation in particle engineering, inhaler device technologies, and clinical understanding. As such, the field has expanded into new directions and is working toward the efficient delivery of increasingly complex cargos to address a wider range of respiratory diseases. This review seeks to highlight recent innovations in approaches to personalize inhalation drug delivery, deliver complex cargos, and diversify the targets treated and prevented through pulmonary drug delivery. We aim to inform readers of the emerging efforts within the field and predict where future breakthroughs are expected to impact the treatment of respiratory diseases.

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

Multicellular model organisms, such as Drosophila melanogaster (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using D. melanogaster as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

Cell traction force plays a critical role in directing cellular functions, such as proliferation, migration, and differentiation. Current understanding of cell traction force is largely derived from 2D measurements where cells are plated on 2D substrates. However, 2D measurements do not recapitulate a vital aspect of living systems; that is, cells actively remodel their surrounding extracellular matrix (ECM), and the remodeled ECM, in return, can have a profound impact on cell phenotype and traction force generation. This reciprocal adaptivity of living systems is encoded in the material properties of biological gels. In this review, we summarize recent progress in measuring cell traction force for cells embedded within 3D biological gels, with an emphasis on cell-ECM cross talk. We also provide perspectives on tools and techniques that could be adapted to measure cell traction force in complex biochemical and biophysical environments.

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.