Modeling Stem/Progenitor Cell-Induced Neovascularization and\ud Oxygenation around Solid Implants

Tissue engineering constructs and other solid implants with biomedical applications, such as drug delivery devices or bioartificial organs, need oxygen (O2) to function properly. To understand better the vascular integration of such devices, we recently developed a novel model sensor containing O2-sensitive crystals, consisting of a polymeric capsule limited by a nano-porous filter. The sensor was implanted in mice with hydrogel alone (control) or hydrogel embedded with mouse CD117/c-kit+ bone marrow progenitor cells (BMPC) in order to stimulate peri-implant neovascularization. The sensor provided local partial O2 pressure (pO2) using non-invasive electron paramagnetic resonance (EPR) signal measurements. A consistently higher level of per-implant oxygenation was observed in the cell-treatment case as compared to the control over a 10-week period. In order to provide a mechanistic explanation of these experimental observations, we present in this paper a mathematical model, formulated as a system of coupled partial differential equations, that simulates peri-implant vascularization. In the control case, vascularization is considered to be the result of a Foreign Body Reaction (FBR) while in the cell-treatment case, adipogenesis in response to paracrine stimuli produced by the stem cells is assumed to induce neovascularization. The model is validated by fitting numerical predictions of local pO2 to measurements from the implanted sensor. The model is then used to investigate further the potential for using stem cell treatment to enhance the vascular integration of biomedical implants. We thus demonstrate how mathematical modeling combined with experimentation can be used to infer how vasculature develops around biomedical implants in control and stem celltreated cases

Mathematical methods for modeling the microcirculation

The microcirculation plays a major role in maintaining homeostasis in the body. Alterations or dysfunctions of the microcirculation can lead to several types of serious diseases. It is not surprising, then, that the microcirculation has been an object of intense theoretical and experimental study over the past few decades. Mathematical approaches offer a valuable method for quantifying the relationships between various mechanical, hemodynamic, and regulatory factors of the microcirculation and the pathophysiology of numerous diseases. This work provides an overview of several mathematical models that describe and investigate the many different aspects of the microcirculation, including geometry of the vascular bed, blood flow in the vascular networks, solute transport and delivery to the surrounding tissue, and vessel wall mechanics under passive and active stimuli. Representing relevant phenomena across multiple spatial scales remains a major challenge in modeling the microcirculation. Nevertheless, the depth and breadth of mathematical modeling with applications in the microcirculation is demonstrated in this work. A special emphasis is placed on models of the retinal circulation, including models that predict the influence of ocular hemodynamic alterations with the progression of ocular diseases such as glaucoma

The Boston University Photonics Center annual report 2016-2017

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2016-2017 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has undoubtedly been the Photonics Center’s best year since I became Director 10 years ago. In the following pages, you will see highlights of the Center’s activities in the past year, including more than 100 notable scholarly publications in the leading journals in our field, and the attraction of more than 22 million dollars in new research grants/contracts. Last year I had the honor to lead an international search for the first recipient of the Moustakas Endowed Professorship in Optics and Photonics, in collaboration with ECE Department Chair Clem Karl. This professorship honors the Center’s most impactful scholar and one of the Center’s founding visionaries, Professor Theodore Moustakas. We are delighted to haveawarded this professorship to Professor Ji-Xin Cheng, who joined our faculty this year.The past year also marked the launch of Boston University’s Neurophotonics Center, which will be allied closely with the Photonics Center. Leading that Center will be a distinguished new faculty member, Professor David Boas. David and I are together leading a new Neurophotonics NSF Research Traineeship Program that will provide $3M to promote graduate traineeships in this emerging new field. We had a busy summer hosting NSF Sites for Research Experiences for Undergraduates, Research Experiences for Teachers, and the BU Student Satellite Program. As a community, we emphasized the theme of “Optics of Cancer Imaging” at our annual symposium, hosted by Darren Roblyer. We entered a five-year second phase of NSF funding in our Industry/University Collaborative Research Center on Biophotonic Sensors and Systems, which has become the centerpiece of our translational biophotonics program. That I/UCRC continues to focus on advancing the health care and medical device industries

Organ-izing the Curriculum: Enhancing Knowledge, Attitudes and Interests in Engineering with Biomedical Course Modules

Proposed abstract for the NSF-Grantees Poster Session Organ-izing the Curriculum: enhancing knowledge, attitudes and interests in engineering with biomedical course modules The relatively new discipline of biomedical engineering emerged from informal collaborations between engineers, physicians and life scientists, and is the fastest growing engineering discipline at most universities. Chemical, mechanical, and electrical engineers play an important and expanding role in this burgeoning field because the fundamental core principles of each discipline are critical to biomedical mainstays such as the design of artificial organs. This project introduces hands-on, biomedically-related experiments and course materials into the engineering curriculum, with the aim of increasing core disciplinary knowledge and increasing interest in engineering. This paper describes the biomedical modules that have been developed and integrated into a variety of courses throughout XXXX’s engineering curriculum. Results demonstrate an increase in student’s understanding of engineering concepts in comparison to control groups. At the freshman level, the treatment group that participated in biomedical education showed significantly higher gains in their perception of classroom climate, interest and confidence in biomedical engineering, confidence in engineering, confidence in writing,and perception of engineers’ contribution to society

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace\u2019s thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125, 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson\u2019s correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values and those calculated by the neural network

Multimodal Ultrasound Imaging for Improved Metastatic Lymph Node Detection

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and is complex in nature due to the variety of organs located in the head and neck region. Knowing the metastatic state of the lymph nodes is paramount in accurately staging and treating HNSCC patients. Currently, metastatic lymph node detection involves the use of magnetic resonance imaging and/or x-ray computed tomography, followed by biopsies for histological confirmation. The main diagnostic criteria is the size of the nodes; however, current imaging methods are not 100% accurate due natural lymph node variability. Ultrasound imaging is able to provide additional biological information in addition to lymph node size such as the hilus state, presence of necrosis and vascular information, but it is hindered by poor resolution and limited contrast. Augmenting ultrasound for metastatic lymph node detection has clinical potential due to the availability of ultrasound in the clinic, reduced radiation exposure and minimized patient morbidity. This thesis focuses on augmenting ultrasound with photoacoustic imaging or with nanoparticle contrast agents for improved detection of lymph node metastasis. First, the development of an ultrasound-photoacoustic (USPA) imaging system is described. The USPA system is capable of imaging blood oxygen saturation (sO2), a promising criteria to differentiate between metastatic and healthy lymph nodes. To correct for tissue-dependent attenuation of light in tissue, a deep neural network was developed and trained using Monte-Carlo simulated and experimentally acquired photoacoustic data for better sO2 predictions. Secondly, to improve ultrasound sensitivity to metastatic cells, molecularly targeted phase change perfluorohexane nanodroplets conjugated to epidermal growth factor receptor (EGFR) antibodies (PFHnD-Abs) were developed. It is shown that the PFHnD-Abs are able to specifically bind to HNSCC cells and improve the ultrasound contrast of the cells, opening the door to targeted metastatic lymph node detection. Lastly, to validate the use of the PFHnD-Abs in-vivo, a paired agent imaging approach was adopted by using using a perfluoropentane core nanodroplet (PFPnD) as a non-targeted imaging agent to enable multiplex ultrasound imaging in vivo. Overall, this work expands the potential of ultrasound for metastatic lymph node detection

Director's Discretionary Fund Report for Fiscal Year 1996

Topics covered include: Waterproofing the Space Shuttle tiles, thermal protection system for Reusable Launch Vehicles, computer modeling of the thermal conductivity of cometary ice, effects of ozone depletion and ultraviolet radiation on plants, a novel telemetric biosensor to monitor blood pH on-line, ion mobility in polymer electrolytes for lithium-polymer batteries, a microwave-pumped far infrared photoconductor, and a new method for measuring cloud liquid vapor using near infrared remote sensing. Also included: laser-spectroscopic instrument for turbulence measurement, remote sensing of aircraft contrails using a field portable imaging interferometer, development of a silicon-micromachined gas chromatography system for determination of planetary surface composition, planar Doppler velocimetry, chaos in interstellar chemistry, and a limited pressure cycle engine for high-speed output

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion