Journal: Annual review of biomedical engineering
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. Expected final online publication date for the Annual Review of Biomedical Engineering Volume 19 is June 4, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Recent research has demonstrated that tumor microenvironments play pivotal roles in tumor development and metastasis through various physical, chemical, and biological factors, including extracellular matrix (ECM) composition, matrix remodeling, oxygen tension, pH, cytokines, and matrix stiffness. An emerging trend in cancer research involves the creation of engineered three-dimensional tumor models using bioinspired hydrogels that accurately recapitulate the native tumor microenvironment. With recent advances in materials engineering, many researchers are developing engineered tumor models, which are promising platforms for the study of cancer biology and for screening of therapeutic agents for better clinical outcomes. In this review, we discuss the development and use of polymeric hydrogel materials to engineer native tumor ECMs for cancer research, focusing on emerging technologies in cancer engineering that aim to accelerate clinical outcomes.
An increasing number of studies have strongly correlated the composition of the human microbiota with many human health conditions and, in several cases, have shown that manipulating the microbiota directly affects health. These insights have generated significant interest in engineering indigenous microbiota community members and nonresident probiotic bacteria as biotic diagnostics and therapeutics that can probe and improve human health. In this review, we discuss recent advances in synthetic biology to engineer commensal and probiotic lactic acid bacteria, bifidobacteria, and Bacteroides for these purposes, and we provide our perspective on the future potential of these technologies. 277 Expected final online publication date for the Annual Review of Biomedical Engineering Volume 20 is June 4, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Living systems exhibit remarkable abilities to self-assemble, regenerate, and remodel complex shapes. How cellular networks construct and repair specific anatomical outcomes is an open question at the heart of the next-generation science of bioengineering. Developmental bioelectricity is an exciting emerging discipline that exploits endogenous bioelectric signaling among many cell types to regulate pattern formation. We provide a brief overview of this field, review recent data in which bioelectricity is used to control patterning in a range of model systems, and describe the molecular tools being used to probe the role of bioelectrics in the dynamic control of complex anatomy. We suggest that quantitative strategies recently developed to infer semantic content and information processing from ionic activity in the brain might provide important clues to cracking the bioelectric code. Gaining control of the mechanisms by which large-scale shape is regulated in vivo will drive transformative advances in bioengineering, regenerative medicine, and synthetic morphology, and could be used to therapeutically address birth defects, traumatic injury, and cancer.
Aging is a complex, multifaceted process that induces a myriad of physiological changes over an extended period of time. Aging is accompanied by major biochemical and biomechanical changes at macroscopic and microscopic length scales that affect not only tissues and organs but also cells and subcellular organelles. These changes include transcriptional and epigenetic modifications; changes in energy production within mitochondria; and alterations in the overall mechanics of cells, their nuclei, and their surrounding extracellular matrix. In addition, aging influences the ability of cells to sense changes in extracellular-matrix compliance (mechanosensation) and to transduce these changes into biochemical signals (mechanotransduction). Moreover, following a complex positive-feedback loop, aging is accompanied by changes in the composition and structure of the extracellular matrix, resulting in changes in the mechanics of connective tissues in older individuals. Consequently, these progressive dysfunctions facilitate many human pathologies and deficits that are associated with aging, including cardiovascular, musculoskeletal, and neurodegenerative disorders and diseases. Here, we critically review recent work highlighting some of the primary biophysical changes occurring in cells and tissues that accompany the aging process.
Engineered, in vitro cardiac cell and tissue systems provide test beds for the study of cardiac development, cellular disease processes, and drug responses in a dish. Much effort has focused on improving the structure and function of engineered cardiomyocytes and heart tissues. However, these parameters depend critically on signaling through the cellular microenvironment in terms of ligand composition, matrix stiffness, and substrate mechanical properties-that is, matrix micromechanobiology. To facilitate improvements to in vitro microenvironment design, we review how cardiomyocytes and their microenvironment change during development and disease in terms of integrin expression and extracellular matrix (ECM) composition. We also discuss strategies used to bind proteins to common mechanobiology platforms and describe important differences in binding strength to the substrate. Finally, we review example biomaterial approaches designed to support and probe cell-ECM interactions of cardiomyocytes in vitro, as well as open questions and challenges.
Rapid diagnostic tests (point-of-care devices) are critical components of informed patient care and public health monitoring (surveillance applications). We propose that among the many rapid diagnostics platforms that have been tested or are in development, lateral flow immunoassays and synthetic biology-based diagnostics (including CRISPR-based diagnostics) represent the best overall options given their ease of use, scalability for manufacturing, sensitivity, and specificity. This review describes the identification of lateral flow immunoassay monoclonal antibody pairs that detect and distinguish between closely related pathogens and that are used in combination with functionalized multicolored nanoparticles and computational methods to deconvolute data. We also highlight the promise of synthetic biology-based diagnostic tests, which use synthetic genetic circuits that activate upon recognition of a pathogen-associated nucleic acid sequence, and discuss how the combined or parallel use of lateral flow immunoassays and synthetic biology tools may represent the future of scalable rapid diagnostics.
In this review, we discuss the science of model validation as it applies to physiological modeling. There is widespread disagreement and ambiguity about what constitutes model validity. In areas in which models affect real-world decision-making, including within the clinic, in regulatory science, or in the design and engineering of novel therapeutics, this question is of critical importance. Without an answer, it impairs the usefulness of models and casts a shadow over model credibility in all domains. To address this question, we examine the use of nonmathematical models in physiological research, in medical practice, and in engineering to see how models in other domains are used and accepted. We reflect on historic physiological models and how they have been presented to the scientific community. Finally, we look at various validation frameworks that have been proposed as potential solutions during the past decade.
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, © these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
Subconcussive head injury represents a pathophysiology that spans the expertise of both clinical neurology and biomechanical engineering. From both viewpoints, the terms injury and damage, presented without qualifiers, are synonymously taken to mean a tissue alteration that may be recoverable. For clinicians, concussion is evolving from a purely clinical diagnosis to one that requires objective measurement, to be achieved by biomedical engineers. Subconcussive injury is defined as subclinical pathophysiology in which underlying cellular- or tissue-level damage (here, to the brain) is not severe enough to present readily observable symptoms. Our concern is not whether an individual has a (clinically diagnosed) concussion, but rather, how much accumulative damage an individual can tolerate before they will experience long-term deficit(s) in neurological health. This concern leads us to look for the history of damage-inducing events, while evaluating multiple approaches for avoiding injury through reduction or prevention of the associated mechanically induced damage. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 22 is June 4, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.