Annual Review of Biomedical Engineering最新文献

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent hepatic pathology worldwide, with significant potential for progression to cirrhosis and ultimately end-stage liver disease. Accordingly, a wide range of preclinical models have been developed to better understand the disease mechanisms and progression as well as to accelerate drug discovery. These include in vitro, ex vivo, and in vivo models, which offer unique advantages yet differ in terms of disease driver, species used, and biological complexity-ranging from benchtop cellular systems to whole organs and organisms. In this review, we provide a comprehensive overview of the technologies currently used for the study of MASLD, with a focus on how standardization of disease progression across models may aid therapeutic development.

Type 1 diabetes (T1D) is a chronic condition in which patients suffer from high blood glucose levels due to the body's inability to produce sufficient insulin. Continuous insulin administration and T1D management are difficult, often leading to hypoglycemic events, insulin resistance, and lower quality of life. Major advancements have been made in recent years, including clinical islet transplantation, but their application is limited by rapid immune rejection and islet destruction. Thus, a necessary paradigm shift has been observed in recent times toward biomaterial-based islet transplantation therapy. The use of biomaterial-based encapsulation addresses major limitations, including immune rejection and hypoxia, and provides a proper cell microenvironment offering greater islet viability. Presently, researchers are more focused on developing a clinically translatable therapy for T1D with the existing knowledge of advanced biomaterial technology. In this review article, we provide a historical perspective, highlighting the developments in the field of islet encapsulation and transplantation, and focus on cutting-edge advancements with modern bioengineering from a clinical perspective.

Tumors display genomic and phenotypic heterogeneity, which holds prognostic significance and may influence therapy response. Radiographic imaging modalities, such as computed tomography, magnetic resonance imaging, nuclear medicine techniques, and ultrasonography, are routinely used to generate parametric maps to identify, measure, and map tumor heterogeneity from different perspectives encompassing anatomy, physiology, and metabolism. This review underscores the potential of artificial intelligence (AI)-based habitat imaging analysis, referred to as Radiomics++, in decoding intratumor heterogeneity compared to conventional radiomics. We highlight the general workflow, underlying principles, detailed methodology, and clinical applications of habitat imaging analysis to guide researchers. Validation advancements are then reviewed to verify the reliability of generated habitats by correlating radiologic phenotypes with biologic underpinnings. Furthermore, we address key challenges and opportunities in clinical translation, including data heterogeneity, model performance, and interpretability. Finally, integrating AI-defined habitats with multi-omics is anticipated to deepen our understanding of tumor evolution and advance precision medicine.

Acrolein is a highly reactive α,β-unsaturated aldehyde produced endogenously through lipid peroxidation and enzymatic metabolism and exogenously via environmental exposures. Acrolein covalently adducts to DNA and proteins, leading to oxidative stress, mitochondrial dysfunction, and inflammation, including innate immune response activation via natural antibodies. Acrolein is difficult to measure in biological systems, but acrolein-bound covalent products can be measured reliably. Therapeutically, nucleophilic small molecules that scavenge acrolein such as hydralazine, phenelzine, dimercaprol, carnosine, and N-acetylcysteine (NAC) have shown neuroprotective effects in animal models of multiple sclerosis, Parkinson's disease, spinal cord injury, and traumatic brain injury. These effects include preserved membrane and mitochondrial integrity, reduced inflammation, reduced pain, and improved motor, sensory, and cognitive outcomes. Alternative strategies that enhance clearance or inhibit production of acrolein show promise but face limitations. Acrolein is a key pathophysiological mediator and a viable therapeutic target in central nervous system trauma and neurodegenerative diseases.

Artificial intelligence (AI), including deep and traditional machine learning, holds great promise for advancing biomedical research and healthcare. However, most AI studies remain academic in nature and rarely transition into clinical practice, largely due to limited access to diverse real-world datasets. Centralized learning, the traditional approach to multi-institutional collaboration, is hindered by privacy, legal, and logistical barriers. Federated learning (FL) offers a decentralized alternative, enabling institutions to collaboratively train models without sharing sensitive patient data. This article reviews key algorithmic, privacy, and practical developments in FL for biomedical engineering, including strategies to handle non-identical data distributions and safeguard privacy through differential privacy, secure aggregation, and confidential computing. We also discuss current limitations and considerations for the need of scalable, interoperable infrastructures. FL represents a paradigm shift toward building generalizable, equitable, and clinically impactful AI models. Realizing this vision requires continued advances, such as FL-as-a-service platforms and regulatory-aligned workflows that support persistent and trustworthy model deployment to truly realize AI's promise in patient care.

Precision neurotherapeutics represents a transformative paradigm shift from standardized "one-size-fits-all" treatments of neurological, neurodegenerative, and/or psychiatric disorders toward individualized interventions that leverage patient-specific biological, behavioral, and physiological characteristics. Traditional neurotherapeutic approaches achieve modest response rates of 30-60% for first-line treatments, necessitating personalized strategies that account for individual differences in genetics, brain structure and function, and treatment response profiles. This review examines advances across three core domains: pharmaceutical approaches utilizing fragment-based drug discovery, pharmacokinetic modeling, and quantitative systems pharmacology; neuromodulation technologies evolving from open-loop to adaptive closed-loop systems with real-time biomarker feedback; and biomarker development spanning neuroimaging, pharmacogenomics, and digital health applications. Critical challenges include developing robust methodological frameworks for single-subject parameter estimation, addressing signal-to-noise ratio limitations in neuroimaging, and navigating complex regulatory landscapes. The convergence of artificial intelligence, computational modeling, and US Food and Drug Administration policy shifts toward in silico approaches creates unprecedented opportunities for mechanistically informed biomarkers that can guide truly personalized mental health care.

As health care systems worldwide seek to decentralize diagnostics and expand precision medicine, silicon photonic biosensors have become a compelling solution. Their development over the past decade, especially in the last 5 years, marks a significant convergence of photonics, nanotechnology, and biomedical engineering that aims to reshape the diagnostic landscape. This review presents a comprehensive analysis of advances in silicon photonic biosensors, focusing on key configurations including microring resonators, photonic crystals, interferometers, and other emerging transduction mechanisms. We discuss the integration of advanced surface functionalization strategies for efficient and robust bioreceptor immobilization, which is critical for reliable biomedical applications. We emphasize the translation of these devices into clinical settings, primarily in infectious diseases and cancer diagnostics. Finally, we address current limitations, such as fabrication complexity, microfluidic integration, and data interpretation, and outline future directions to enhance scalability and clinical adoption in personalized medicine and decentralized health care.

The autonomic nervous system (ANS) plays a vital role in health care for both acute care and chronic diseases. The traditional view of the ANS is to divide it into individual organ systems and study the separate components with a reductionist approach, which has been proven insufficient. Here, we argue that a holistic network-level view of the ANS is critical for generating new insights and deepening our understanding of its complex and dynamic functions. In this review, we treat the ANS as such a coordinated and dynamic network. We advocate for studying its interactions with major organ systems and the central nervous system using continuous and longitudinal monitoring in ambulatory and at-home settings rather than clinic-based snapshots. We first briefly review ANS physiology, then outline our network perspective, and finally highlight cutting-edge research directions and emerging engineering innovations in ANS monitoring, modeling, and modulation that benefit from this network-level view.

Electrical stimulation of the brain is being developed as a treatment for an increasing number of neurological disorders. Technologies for delivering electrical stimulation are advancing rapidly and vary in specificity, coverage, and invasiveness. Supracortical microstimulation (SCMS), characterized by microelectrode contacts placed on the epidural or subdural cortical surface, achieves a balance between the advantages and limitations of other electrical stimulation technologies by delivering spatially precise activation without disrupting the integrity of the cortex. However, in vivo experiments involving SCMS have not been comprehensively summarized. Here, we review the field of SCMS, focusing on recent advances, to guide the development of clinically translatable supracortical microelectrodes. We also highlight the gaps in our understanding of the biophysical effects of this technology. Future work investigating the unique electrochemical properties of supracortical microelectrodes and validating SCMS in nonhuman primate preclinical studies can enable rapid clinical translation of innovative treatments for humans with neurological disorders.

Over the last decade, a plethora of organoid models have been generated to recapitulate aspects of human development, disease, tissue homeostasis, and repair. Organoids representing multiple tissues have emerged and are typically categorized based on their origin. Tissue-derived organoids are established directly from tissue-resident stem/progenitor cells of either adult or fetal origin. Starting from pluripotent stem cells (PSCs), PSC-derived organoids instead recapitulate the developmental trajectory of a given organ. Gene editing technologies, particularly the CRISPR-Cas toolbox, have greatly facilitated gene manipulation experiments with considerable ease and scalability, revolutionizing organoid-based human biology research. Here, we review the recent adaptation of CRISPR-based screenings in organoids. We examine the strategies adopted to perform CRISPR screenings in organoids, discuss different screening scopes and readouts, and highlight organoid-specific challenges. We then discuss individual organoid-based genome screening studies that have uncovered novel genes involved in a variety of biological processes. We close by providing an outlook on how widespread adaptation of CRISPR screenings across the organoid field may be achieved, to ultimately leverage our understanding of human biology.