BME frontiers最新文献

If the 20th century was the age of mapping and controlling the external world, the 21st century is the biomedical age of mapping and controlling the biological internal world. The biomedical age is bringing new technological breakthroughs for sensing and controlling human biomolecules, cells, tissues, and organs, which underpin new frontiers in the biomedical discovery, data, biomanufacturing, and translational sciences. This article reviews what we believe will be the next wave of biomedical engineering (BME) education in support of the biomedical age, what we have termed BME 2.0. BME 2.0 was announced on October 12 2017 at BMES 49 (https://www.bme.jhu.edu/news-events/news/miller-opens-2017-bmes-annual-meeting-with-vision-for-new-bme-era/). We present several principles upon which we believe the BME 2.0 curriculum should be constructed, and from these principles, we describe what view as the foundations that form the next generations of curricula in support of the BME enterprise. The core principles of BME 2.0 education are (a) educate students bilingually, from day 1, in the languages of modern molecular biology and the analytical modeling of complex biological systems; (b) prepare every student to be a biomedical data scientist; (c) build a unique BME community for discovery and innovation via a vertically integrated and convergent learning environment spanning the university and hospital systems; (d) champion an educational culture of inclusive excellence; and (e) codify in the curriculum ongoing discoveries at the frontiers of the discipline, thus ensuring BME 2.0 as a launchpad for training the future leaders of the biotechnology marketplaces. We envision that the BME 2.0 education is the path for providing every student with the training to lead in this new era of engineering the future of medicine in the 21st century.

Neurological disorders have always been a threat to human physical and mental health nowadays, which are closely related to the nonregeneration of neurons in the nervous system (NS). The damage to the NS is currently difficult to repair using conventional therapies, such as surgery and medication. Therefore, repairing the damaged NS has always been a vast challenge in the area of neurology. Tissue engineering (TE), which integrates the cell biology and materials science to reconstruct or repair organs and tissues, has widespread applications in bone, periodontal tissue defects, skin repairs, and corneal transplantation. Recently, tremendous advances have been made in TE regarding neuroscience. In this review, we summarize TE's recent progress in neuroscience, including pathological mechanisms of various neurological disorders, the concepts and classification of TE, and the most recent development of TE in neuroscience. Lastly, we prospect the future directions and unresolved problems of TE in neuroscience.

We need novel strategies to target the complexity of cancer and, particularly, of metastatic disease. As an example of this complexity, certain tissues are particularly hospitable environments for metastases, whereas others do not contain fertile microenvironments to support cancer cell growth. Continuing evidence that the extracellular matrix (ECM) of tissues is one of a host of factors necessary to support cancer cell growth at both primary and secondary tissue sites is emerging. Research on cancer metastasis has largely been focused on the molecular adaptations of tumor cells in various cytokine and growth factor environments on 2-dimensional tissue culture polystyrene plates. Intravital imaging, conversely, has transformed our ability to watch, in real time, tumor cell invasion, intravasation, extravasation, and growth. Because the interstitial ECM that supports all cells in the tumor microenvironment changes over time scales outside the possible window of typical intravital imaging, bioengineers are continuously developing both simple and sophisticated in vitro controlled environments to study tumor (and other) cell interactions with this matrix. In this perspective, we focus on the cellular unit responsible for upholding the pathologic homeostasis of tumor-bearing organs, cancer-associated fibroblasts (CAFs), and their self-generated ECM. The latter, together with tumoral and other cell secreted factors, constitute the "tumor matrisome". We share the challenges and opportunities for modeling this dynamic CAF/ECM unit, the tools and techniques available, and how the tumor matrisome is remodeled (e.g., via ECM proteases). We posit that increasing information on tumor matrisome dynamics may lead the field to alternative strategies for personalized medicine outside genomics.

Vascular prostheses (grafts) are widely used for hemodialysis blood access, trauma repair, aneurism repair, and cardiovascular reconstruction. However, smaller-diameter (≤4 mm) grafts that would be valuable for many reconstructions have not been achieved to date, although hundreds of papers on small-diameter vascular grafts have been published. This perspective article presents a hypothesis that may open new research avenues for the development of small-diameter vascular grafts. A historical review of the vascular graft literature and specific types of vascular grafts is presented focusing on observations important to the hypothesis to be presented. Considerations in critically reviewing the vascular graft literature are discussed. The hypothesis that perhaps the "biocompatible biomaterials" comprising our vascular grafts-biomaterials that generate dense, nonvascularized collagenous capsules upon implantation-may not be all that biocompatible is presented. Examples of materials that heal with tissue reconstruction and vascularity, in contrast to the fibrotic encapsulation, are offered. Such prohealing materials may lead the way to a new generation of vascular grafts suitable for small-diameter reconstructions.

Objective and Impact Statement: We developed a generalized computational approach to design uniform, high-intensity excitation light for low-cost, quantitative fluorescence imaging of in vitro, ex vivo, and in vivo samples with a single device. Introduction: Fluorescence imaging is a ubiquitous tool for biomedical applications. Researchers extensively modify existing systems for tissue imaging, increasing the time and effort needed for translational research and thick tissue imaging. These modifications are application-specific, requiring new designs to scale across sample types. Methods: We implemented a computational model to simulate light propagation from multiple sources. Using a global optimization algorithm and a custom cost function, we determined the spatial positioning of optical fibers to generate 2 illumination profiles. These results were implemented to image core needle biopsies, preclinical mammary tumors, or tumor-derived organoids. Samples were stained with molecular probes and imaged with uniform and nonuniform illumination. Results: Simulation results were faithfully translated to benchtop systems. We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings. The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model. Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used. Conclusion: We demonstrate the significance of using a computationally designed illumination for in vitro, ex vivo, and in vivo fluorescence imaging. Application-specific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features. This approach is generalizable across light sources, biological applications, and detectors.

A variety of volatile organic compounds (VOCs) are produced and emitted by the human body every day. The identity and concentration of these VOCs reflect an individual's metabolic condition. Information regarding the production and origin of VOCs, however, has yet to be congruent among the scientific community. This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals. Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis. VOC research has prioritized the study of cancer, resulting in many research articles and reviews being written on the topic. This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research, including neurodegenerative and other noninfectious diseases. Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath, urine, blood, feces, and skin. New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results, and we compare their diagnostic dependability with gas chromatography- and mass spectrometry-based techniques. The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

Objective and Impact Statement. Distinguishing malignant lymphocytes from normal ones is vital in pathological examination. We proposed an inverse light scattering (ILS) method for label-free suspended lymphocytes with complex fine structures to identify their volumes for pathological state. Introduction. Light scattering as cell's "fingerprint" provides valuable morphology information closely related to its biophysical states. However, the detail relationships between the morphology with complex fine structures and its scattering characters are not fully understood. Methods. To quantitatively inverse the volumes of membrane and nucleus as the main scatterers, clinical lymphocyte morphologies were modeled combining the Gaussian random sphere geometry algorithm by 750 reconstructed results after confocal scanning, which allowed the accurate simulation to solve ILS problem. For complex fine structures, the specificity for ILS study was firstly discussed (to our knowledge) considering the differences of not only surface roughness, posture, but also the ratio of nucleus to the cytoplasm and refractive index. Results. The volumes of membrane and nucleus were proved theoretically to have good linear relationship with the effective area and entropy of forward scattering images. Their specificity deviations were less than 3.5%. Then, our experimental results for microsphere and clinical leukocytes showed the Pearson product-moment correlation coefficients (PPMCC) of this linear relationship were up to 0.9830~0.9926. Conclusion. Our scattering inversion method could be effectively applied to identify suspended label-free lymphocytes without destructive sample pretreatments and complex experimental systems.

Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.