Biophysics reviews最新文献

Machine learning (ML) techniques have been making major impacts on all areas of science and engineering, including biophysics. In this review, we discuss several applications of ML to biophysical problems based on our recent research. The topics include the use of ML techniques to identify hotspot residues in allosteric proteins using deep mutational scanning data and to analyze how mutations of these hotspots perturb co-operativity in the framework of a statistical thermodynamic model, to improve the accuracy of free energy simulations by integrating data from different levels of potential energy functions, and to determine the phase transition temperature of lipid membranes. Through these examples, we illustrate the unique value of ML in extracting patterns or parameters from complex data sets, as well as the remaining limitations. By implementing the ML approaches in the context of physically motivated models or computational frameworks, we are able to gain a deeper mechanistic understanding or better convergence in numerical simulations. We conclude by briefly discussing how the introduced models can be further expanded to tackle more complex problems.

The accurate detection of x-rays enables broad applications in various fields, including medical radiography, safety and security screening, and nondestructive inspection. Medical imaging procedures require the x-ray detection devices operating with low doses and high efficiency to reduce radiation health risks, as well as expect the flexible or wearable ones that offer more comfortable and accurate diagnosis experiences. Recently, halide perovskites have shown promising potential in high-performance, cost-effective x-ray detection owing to their attractive features, such as strong x-ray absorption, high-mobility-lifetime product, tunable bandgap, fast response, as well as low-cost raw materials, facile processing, and excellent flexibility. In this review, we comprehensively summarize the recent advances in halide perovskite x-ray detectors and imaging, focusing on their application potential in medical imaging technology. We highlight the recent demonstrations and optimizations of halide perovskite x-ray detectors and imaging and their application in medical radiography. Finally, we conclude by pointing out the challenges of perovskite x-ray detection devices for the clinical practical applications and by sharing our perspectives on the potential solutions for driving the field forward.

Atrial fibrillation (AF) is recognized as a developing global epidemic responsible for a significant burden of morbidity and mortality. To counter this public health crisis, the advancement of artificial intelligence (AI)-aided tools and methodologies for the effective detection and monitoring of AF is becoming increasingly apparent. A unified strategy from the international research community is essential to develop effective intelligent tools and technologies to support the health professionals for effective surveillance and defense against AF. This review delves into the practical implications of AI-aided tools and techniques for AF detection across different clinical settings including screening, diagnosis, and ambulatory monitoring by reviewing the revolutionary research works. The key finding is that the advance in AI and its use for automatic detection of AF has achieved remarkable success, but collaboration between AI and human intelligence is required for trustworthy diagnostic of this life-threatening cardiac condition. Moreover, designing efficient and robust intelligent algorithms for onboard AF detection using portable and implementable computing devices with limited computation power and energy supply is a crucial research problem. As modern wearable devices are equipped with sophisticated embedded sensors, such as optical sensors and accelerometers, hence photoplethysmography and ballistocardiography signals could be explored as an affordable alternative to electrocardiography (ECG) signals for AF detection, particularly for the development of low-cost and miniature screening and monitoring devices.

Molecular chaperones play a key role in protein homeostasis by preventing misfolding and aggregation, assisting in proper protein folding, and sometimes even disaggregating formed aggregates. Chaperones achieve this through a range of transient weak protein-protein interactions, which are difficult to study using traditional structural and biophysical techniques. Nuclear magnetic resonance (NMR) spectroscopy, however, is well-suited for studying such dynamic states and interactions. A wide range of NMR experiments have been particularly valuable in understanding the mechanisms of chaperone function, as they can characterize disordered protein structures, detect weak and nonspecific interactions involving sparsely populated states, and probe the conformational dynamics of proteins and their complexes. Recent advances in NMR have significantly enhanced our knowledge of chaperone mechanisms, especially chaperone-client interactions, despite the inherent challenges posed by the flexibility and complexity of these systems. In this review, we highlight contributions of NMR to the chaperone field, focusing on the work carried out in our laboratory, which have provided insights into how chaperones maintain function within the cellular environment and interact with various protein substrates.

The plant cytoskeleton is an intricate network composed of actin filaments and microtubules. The cytoskeleton undergoes continuous dynamic changes that provide the basis for rapidly responding to intrinsic and extrinsic stimuli, including mechanical stress. Microtubules can respond to alterations of mechanical stress and reorient along the direction of maximal tensile stress in plant cells. The cytoskeleton can also generate driving force for cytoplasmic streaming, organelle movement, and vesicle transportation. In this review, we discuss the progress of how the plant cytoskeleton responds to mechanical stress. We also summarize the roles of the cytoskeleton in generating force that drive organelles and nuclear transportation in plant cells. Finally, some hypotheses concerning the link between the roles of the cytoskeleton in force response and organelle movement, as well as several key questions that remain to be addressed in the field, are highlighted.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Nanotechnology has demonstrated tremendous promise in the realm of ocular illnesses, with applications for disease detection and therapeutic interventions. The nanoscale features of nanoparticles enable their precise interactions with retinal tissues, allowing for more efficient and effective treatments. Because biological organs are compatible with diverse nanomaterials, such as nanoparticles, nanowires, nanoscaffolds, and hybrid nanostructures, their usage in biomedical applications, particularly in retinal illnesses, has increased. The use of nanotechnology in medicine is advancing rapidly, and recent advances in nanomedicine-based diagnosis and therapy techniques may provide considerable benefits in addressing the primary causes of blindness related to retinal illnesses. The current state, prospects, and challenges of nanotechnology in monitoring nanostructures or cells in the eye and their application to regenerative ophthalmology have been discussed and thoroughly reviewed. In this review, we build on our previously published review article in 2021, where we discussed the impact of nano-biomaterials in retinal regeneration. However, in this review, we extended our focus to incorporate and discuss the application of nano-biomaterials on all retinal diseases, with a highlight on nanomedicine-based diagnostic and therapeutic research studies.

Pancreas, a vital organ with intricate endocrine and exocrine functions, is central to the regulation of the body's glucose levels and digestive processes. Disruptions in its endocrine functions, primarily regulated by islets of Langerhans, can lead to debilitating diseases such as diabetes mellitus. Murine models of pancreatic dysfunction have contributed significantly to the understanding of insulitis, islet-relevant immunological responses, and the optimization of cell therapies. However, genetic differences between mice and humans have severely limited their clinical translational relevance. Recent advancements in tissue engineering and microfabrication have ushered in a new era of in vitro models that offer a promising solution. This paper reviews the state-of-the-art engineered tools designed to study endocrine dysfunction of the pancreas. Islet on a chip devices that allow precise control of various culture conditions and noninvasive readouts of functional outcomes have led to the generation of physiomimetic niches for primary and stem cell derived islets. Live pancreatic slices are a new experimental tool that could more comprehensively recapitulate the complex cellular interplay between the endocrine and exocrine parts of the pancreas. Although a powerful tool, live pancreatic slices require more complex control over their culture parameters such as local oxygenation and continuous removal of digestive enzymes and cellular waste products for maintaining experimental functionality over long term. The combination of islet-immune and slice on chip strategies can guide the path toward the next generation of pancreatic tissue modeling for better understanding and treatment of endocrine pancreatic dysfunctions.

In the intricately defined spatial microenvironment, a single fertilized egg remarkably develops into a conserved and well-organized multicellular organism. This observation leads us to hypothesize that stem cells or other seed cell types have the potential to construct fully structured and functional tissues or organs, provided the spatial cues are appropriately configured. Current organoid technology, however, largely depends on spontaneous growth and self-organization, lacking systematic guided intervention. As a result, the structures replicated in vitro often emerge in a disordered and sparse manner during growth phases. Although existing organoids have made significant contributions in many aspects, such as advancing our understanding of development and pathogenesis, aiding personalized drug selection, as well as expediting drug development, their potential in creating large-scale implantable tissue or organ constructs, and constructing multicomponent microphysiological systems, together with functioning at metabolic levels remains underutilized. Recent discoveries have demonstrated that the spatial definition of growth factors not only induces directional growth and migration of organoids but also leads to the formation of assembloids with multiple regional identities. This opens new avenues for the innovative engineering of higher-order organoids. Concurrently, the spatial organization of other microenvironmental cues, such as physical stresses, mechanical loads, and material composition, has been minimally explored. This review delves into the burgeoning field of organoid engineering with a focus on potential spatial microenvironmental control. It offers insight into the molecular principles, expected outcomes, and potential applications, envisioning a future perspective in this domain.

Multicellular systems, such as epithelial cell collectives, undergo transitions similar to those in inert physical systems like sand piles and foams. To remodel or maintain tissue organization during development or disease, these collectives transition between fluid-like and solid-like states, undergoing jamming or unjamming transitions. While these transitions share principles with physical systems, understanding their regulation and implications in cell biology is challenging. Although cell jamming and unjamming follow physics principles described by the jamming diagram, they are fundamentally biological processes. In this review, we explore how cellular processes and interactions regulate jamming and unjamming transitions. We begin with an overview of how these transitions control tissue remodeling in epithelial model systems and describe recent findings of the physical principles governing tissue solidification and fluidization. We then explore the mechanistic pathways that modulate the jamming phase diagram axes, focusing on the regulation of cell fluctuations and geometric compatibility. Drawing upon seminal works in cell biology, we discuss the roles of cytoskeleton and cell-cell adhesion in controlling cell motility and geometry. This comprehensive view illustrates the molecular control of cell jamming and unjamming, crucial for tissue remodeling in various biological contexts.