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Overly dense microvascular networks are treated by selective reduction of vascular elements. Inappropriate manipulation of microvessels could result in loss of host tissue function or a worsening of the clinical problem. Here, an experimental and computational model were developed to predict skin microvascular network remodeling in response to selective vessel interruption via multi-photon laser ablation without damage of the host tissue. The microvasculature was imaged non-invasively by bright-field and multi-photon laser microscopy, and Optical Coherence Tomography pre-ablation and up to 30 days post-ablation. A theoretical model of network remodeling was developed to compute intravascular pressure and identify vessels most sensitive to changes in flow direction. Immediately post-ablation, the average artery and vein diameters increased by 14% and 23%, respectively. The maximum arterial remodeling was 2.5x vs. 3.3x for a vein at day 20 post-ablation. By day 30, the average artery diameter remained 11% increased whereas the vein diameter returned to near preablation value. Venous blood flow was restored in the same location while arterial blood was redistributed through remodeling of collateral circulation with minimum angiogenesis. The theoretical model predicts that the vessels most likely to act as collaterals after flow disruption are those most sensitive to distant changes in pressure.

Lung cancer is the most common cancer globally with over 2 million new cases diagnosed every year. Fortunately, if caught early, the likelihood of survival is greatly improved. If diagnosed in Stage I, survival rates are >75% over 5 years, vs. just 1% if diagnosed in Stage IV. Early diagnosis requires finding and sampling (biopsy) small, peripheral nodules that are located in the parenchima of the lung and predominately outside small airways. Currently, for early diagnosis a bronchoscope is inserted into the lung airway but due to large size it cannot reach the small airways. Therefore, the doctor has to advance a sharp biopsy needle blindly from the tip of the bronchoscope and into the lung tissue in the approximate direction of the nodule. This blind procedure has low accuracy and carries a high risk of misdiagnosis. Currently, to improve the accuracy, real time x-ray (fluoroscopy) is use which causes exposure of the patient and physician to harmful radiation. Computer and image assisted surgery and medical robotics present viable solutions but are not optimal at present. The scope of our research was to develop a robotic solution for increased precision and accuracy of early diagnosis and treatment of lung cancer, to increase procedure success rate, decrease patient radiation and stress exposure, and reduce the procedure cost. For this purpose, we developed an advanced prototype of a robotic system which is small in size, easy to use and effective. To demonstrate its effectiveness in navigating to peripheral small size lung cancer lesions, we performed laboratory tests or a realistic lung airway model. The preliminary tests of a novel medical robot using a complex lung airway model proved that our catheter driving robotic system is working as designed and allows navigation, through a complex 3D channels structure like the bronchial tree, in both manipulator and robotic modes without fluoroscopy scanning. The robotic system is more precise and stable, and can avoid patient injury and instrument damage due to accidental impact with the airway wall. Because it could be controlled from a different room via the software platform, using this robotic system can drastically reduce radiation exposure of the patient and totally avoid the exposure of the doctor. Another benefit of the proposed robotic system is that it uses currently available catheters in which a reusable electromagnetic guide wire is temporarily inserted to guide the tip of the catheter towards hard to reach targets. After the target is confirmed, the sensor can be retracted and the catheter can be used for its routine function such as biopsy collection. Future development will include placement of a force sensor at the tip of the catheter to “feel” the wall and adapt the speed of insertion in order to avoid wall damage and an improved algorithm to increase the speed in the automatic mode.

Uncontrolled bleeding contributes to 30% to 40% of trauma-related deaths and is the leading cause of potentially preventable deaths. Currently, there is no effective method available to first responders for temporary control of noncompressible intraabdominal bleeding while patients are transported to the hospital. Our previous studies demonstrated that abdominal insufflation provides effective temporary bleeding control. The study aims to prove the feasibility (insufflation to a target pressure) and safety (cardiovascular and respiratory effects) of a novel portable abdominal insufflation device (PAID) designed to control the intraperitoneal bleeding caused by abdominal trauma. The PAID prototype is based on a patented design and manufactured via additive manufacturing. PAID contains a 16-g CO2cartridge and an electronic pressure transducer. PAID was tested on a bench top and a swine animal model. For the animal model study, the intraperitoneal pressure as well as cardiorespiratory parameters (hearth rate, SpO2[peripheral capillary oxygen saturation], and blood pressure) were continuously monitored during the insufflation procedure. The prototype functioned according to specifications on both bench top and animal models. CO2insufflation of the peritoneal cavity was delivered up the target 20 mm Hg and maintained for 30 minutes from 1 or 2 cartridges in the swine model. No intraoperative incidents were registered, and all the recorded physiological parameters were within normal limits. The PAID prototype is a feasible, easy to use device that provides quick, controlled, and safe insufflation of the peritoneal cavity. Future studies will focus on testing the next-generation, semiautomatic PAID prototype in a severe intraabdominal injury model.

During lung cancer screening, suspected nodules are detected using computed tomography. Although most nodules are benign, a tissue diagnostic is still required. Currently, all diagnostic procedures that use image-guided tools expose both the patient and the medical doctor to damaging X-ray radiation. The present study describes the development of a novel robotic and electromagnetic tracking navigation system to guide a catheter in the peripheral airways and made of a navigation software and a patent pending robotic system, EndoRoTM that inserts and guides the catheter semi-automatically to reach a specific target in the airways without the need of CT or fluoroscopy scanning. The preliminary tests on a benchtop airway model showed that the novel robotic system allows navigation through the bronchial tree, in both manual manipulation with a joystick and semi-automatic computer manipulation without fluoroscopy scanning. Overall, the new robotic system will allow a more precise guidance of the biopsy catheters to targets located in the peripheral airways, will reduce injury, and improve the clinical outcomes in early cancer diagnosis and treatment.

Background and Objectives: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagnosis process is necessary. Materials and Methods: For this purpose, we developed an ensemble method that fused two deep learning models, one based on convolutional neural network and the other based on transfer learning. For the first model, called 5-CNN, we developed an efficient end-to-end trained model with five convolutional layers, while for the second model, the pre-trained VGG-19 architecture was repurposed, optimized and trained. We trained and validated our models using a dataset of ultrasound images consisting of four types of thyroidal images: autoimmune, nodular, micro-nodular, and normal. Results: Excellent results were obtained by the ensemble CNN-VGG method, which outperformed the 5-CNN and VGG-19 models: 97.35% for the overall test accuracy with an overall specificity of 98.43%, sensitivity of 95.75%, positive and negative predictive value of 95.41%, and 98.05%. The micro average areas under each receiver operating characteristic curves was 0.96. The results were also validated by two physicians: an endocrinologist and a pediatrician. Conclusions: We proposed a new deep learning study for classifying ultrasound thyroidal images to assist physicians in the diagnosis process.

Aim: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images consisting of 2 types of liver images, normal and liver steatosis. Material and methods: We assessed two pre-trained models of convolutional neural networks, Inception-v3 and VGG-16 using fine-tuning. Both models were pre-trained on ImageNet dataset to extract features from B-mode ultrasound liver images. The results obtained through these methods were compared for selecting the predictive model with the best performance metrics. We trained the two models using a dataset of 262 images of liver steatosis and 234 images of normal liver. We assessed the models using a dataset of 70 liver steatosis im - ages and 63 normal liver images. Results. The proposed model that used Inception v3 obtained a 93.23% test accuracy with a sensitivity of 89.9%% and a precision of 96.6%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.93. The other proposed model that used VGG-16, obtained a 90.77% test accuracy with a sensitivity of 88.9% and a precision of 92.85%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.91. Conclusion . The deep learning algorithms that we proposed to detect steatosis and classify the images in normal and fatty liver images, yields an excellent test performance of over 90%. However, future larger studies are required in order to establish how these algorithms can be implemented in a clinical setting.

At present, deep learning becomes an important tool in medical image analysis, with good performance in diagnosing, pattern detection, and segmentation. Ultrasound imaging offers an easy and rapid method to detect and diagnose thyroid disorders. With the help of a computer-aided diagnosis (CAD) system based on deep learning, we have the possibility of real-time and non-invasive diagnosing of thyroidal US images. This paper proposed a study based on deep learning with transfer learning for differentiating the thyroidal ultrasound images using image pixels and diagnosis labels as inputs. We trained, assessed, and compared two pre-trained models (VGG-19 and Inception v3) using a dataset of ultrasound images consisting of 2 types of thyroid ultrasound images: autoimmune and normal. The training dataset consisted of 615 thyroid ultrasound images, from which 415 images were diagnosed as autoimmune, and 200 images as normal. The models were assessed using a dataset of 120 images, from which 80 images were diagnosed as autoimmune, and 40 images diagnosed as normal. The two deep learning models obtained very good results, as follows: the pre-trained VGG-19 model obtained 98.60% for the overall test accuracy with an overall specificity of 98.94% and overall sensitivity of 97.97%, while the Inception v3 model obtained 96.4% for the overall test accuracy with an overall specificity of 95.58% and overall sensitivity of 95.58%

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time.

Background and Aims: Mucosal healing (MH) is associated with a stable course of Crohn’s disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator’s errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. Methods: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. Results: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. Conclusions: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model.

The paper presents the new methods of use of processing technologies for realizing proof-of-concept new medical micro-devices components. By using both classical mechanical machining and unconventional laser beam cutting and welding combined with soldering two proof-of-concept medical devices components were realized. The materials processed were stainless steel and super-elastic nitinol alloy. The selection of the processing technologies was done based on the specific requirements of the devices components as well as the characteristics of the applied materials. The selected technologies were fit for the requirements of the proof-of-concept medical micro-devices components, further RTD work being needed in order to comply with the bio-compatibility and in-exploitation requirements. The specific outcomes of using classical mechanical machining and laser beam processing are presented.

Our understanding of cancer progression or response to therapies would benefit from benchtop, tissue-level assays that preserve the biology and anatomy of human tumors ex vivo. We present methodology for maintaining patient tumor samples ex vivo for the purpose of drug testing in the clinical setting. The harvested tumor biopsy, excised from mice or patients, is integrated into a support tissue that includes stroma and vasculature. This support tissue preserves tumor histo-architecture and relevant expression profiles, and tumor tissues cultured using this system display different sensitivities to chemotherapeutics compared to tumor explants with no supporting tissue. The methodology is more rapid than PDX models, easy to implement and amenable to high throughput assays, making it an attractive tool for in vitro drug screening or for the guidance of patient-specific chemotherapies. This article is protected by copyright. All rights reserved.

The paper presents an algorithm proposed by the authors in order to obtain a theoretical model of the bronchial and used to test the virtual bronchoscopy procedure implemented in an innovative system for bronchoscopy with electromagnetically tracked and steerable biopsy forceps. The procedure is based on the Weibel theoretical model of the human tracheobronchial tree with new features added by the authors. The algorithm can be an important helping tool in the development of sophisticated algorithms for bronchoscopy applications and may be further implemented in clinical studies of navigated bronchoscopy.

Winning academic innovation requires a new team of interdisciplinary specialists and electronic resources for information management. The industry model of innovation social network (ISN) can serve as an example of a proven culture of innovation. It contains a complex research and development network (RDN) led by a product specialist (PS) who coordinates technical, clinical, and business experts to ensure fitness for use of the newly developed products. Currently, the academic innovation social network (aISN) is inefficient and made of only a few of the industry functions such as the clinician/scientist inventors and their laboratory personnel or clinical team and the university technology transfer office (TTO). Moreover, the essential information and knowledge for innovation (i.e., the shortcomings of current clinical and research approaches) are not recorded, more emphasis being currently placed on what works rather than what needs to be improved. A new academic innovation program led by a director of medical innovation (DMI) was developed to accelerate academic innovation at the level of process and execution. As a result, a more complete aISN was created where more academic innovation projects are generated from real clinical needs and advanced toward licensing and venture capital investment. Furthermore, a new innovation electronic platform, the academic innovation management system (AIMS), was developed to connect academia with the external network of service providers and capital investors. The newly developed innovation team spans academic and industry environments and is better prepared to advance winning innovative projects toward commercialization and clinical use.

A novel system for electromagnetic navigation in bronchoscopy (NaviCAD) to improve peripheral lesion targeting and diagnostic is currently under development. The virtual bronchoscopy module of this system, including the collision and resolution algorithm, together with some preliminary tests on a complex phantom are presented in this paper. The NaviCAD system consists of a planning and orientation software, a navigation forceps, and an electromagnetic tracking system connected to a computer running the NaviCAD software. NaviCAD can be used with any bronchoscopy system, it has a short set-up procedure time and learning curve. The system proves to be easy to use, accurate and useful for experienced users and novices, with precision in reaching targets in sub-segmental bronchi where a video-bronchoscope cannot reach.

Testing the efficacy of cancer drugs requires functional assays that recapitulate the cell populations, anatomy and biological responses of human tumors. Although current animal models and in vitro cell culture platforms are informative, they have significant shortcomings. Mouse models can reproduce tissue-level and systemic responses to tumor growth and treatments observed in humans, but xenografts from patients often do not grow, or require months to develop. On the other hand, current in vitro assays are useful for studying the molecular bases of tumorigenesis or drug activity, but often lack the appropriate in vivo cell heterogeneity and natural microenvironment. Therefore, there is a need for novel tools that allow rapid analysis of patient-derived tumors in a robust and representative microenvironment. We have developed methodology for maintaining harvested tumor tissue in vitro by placing them in a support bed with self-assembled stroma and vasculature. The harvested biopsy or tumor explant integrates with the stromal bed and vasculature, providing the correct extracellular matrix (collagen I, IV, fibronectin), associated stromal cells, and a lumenized vessel network. Our system provides a new tool that will allow ex vivo drug-screening and can be adapted for the guidance of patient-specific therapeutic strategies.

Minimal invasive surgical procedures such as laparoscopy are preferred over open surgery due to faster postoperative recovery, less trauma and inflammatory response, and less scarring. Laparoscopic repairs of hiatal hernias require pre-procedure planning to ensure appropriate exposure and positioning of the surgical ports for triangulation, ergonomics, instrument length and operational angles to avoid the fulcrum effect of the long and rigid instruments. We developed a novel surgical planning and navigation software, iMTECH to determine the optimal location of the skin incision and surgical instrument placement depth and angles during laparoscopic surgery. We tested the software on five cases of human hiatal hernia to assess the feasibility of the stereotactic reconstruction of anatomy and surgical planning. A whole-body CT investigation was performed for each patient, and abdominal 3D virtual models were reconstructed from the CT scans. The optical trocar access point was placed on the xipho-umbilical line. The distance on the skin between the insertion point of the optical trocar and the xiphoid process was 159.6, 155.7, 143.1, 158.3, and 149.1 mm, respectively, at a 40&deg, elevation angle. Following the pre-procedure planning, all patients underwent successful surgical laparoscopic procedures. The user feedback was that planning software significantly improved the ergonomics, was easy to use, and particularly useful in obese patients with large hiatal defects where the insertion points could not be placed in the traditional positions. Future studies will assess the benefits of the planning system over the conventional, empirical trocar positioning method in more patients with other surgical challenges.

Virtual bronchoscopy is often used for planning a real bronchoscopy procedure. Software applications are developed for virtual bronchoscopy, involving usually segmentation of the tracheobronchial tree from the medical image scan, which is a difficult operation, both conceptually and from the computer implementation and running time point of view. That is why in this paper, a new method for bronchoscopy procedure planning that does not require such a segmentation is presented. The proposed procedure involves automatic path generation between the starting and ending points, skin removal, an algorithm for detection and resolution of collision with the airways walls and validation of the automatically created path. Results are presented for two datasets – one being the representation of a theoretical lungs model, with six levels of branches and the other one being the image scan of a real patient. Together with a system for tracking the bronchoscope during the real procedure, the proposed method can improve the diagnostic success rate of lung cancer using bronchoscopy and decrease the discomfort perceived by the patient.

Primary lung cancer is an increasing health issue worldwide, with an ever-growing incidence due to various risk factors dispersed in all settings of modern society. Late discovery and poor survival rates for patients that do not qualify for surgical treatments greatly decrease overall mortality. Imaging methods remain powerful tools for early detection; however, molecular profiling is currently required for better understanding treatment options and for developing novel agents. Most procedures that are associated with tissue collection are invasive and, if performed in suboptimal conditions, may lead to morbidity and mortality. The need for better optical biopsy tools has thus arisen, directing further tissue collection and minimizing the chance of misdiagnosis. A series of methods have been proposed, including optical coherence tomography, narrow band imaging or autofluorescence. Lately, a novel in vivo tool for rapid and non-invasive microscopy gained traction - probe based confocal laser endomicroscopy became available in an increased number of referral centers worldwide. Miniaturization and the use of optical fibers allowed for the development of a dedicated device for pulmonary applications; lung cancer diagnosis and characterization are key issues targeted by this novel technology. We present here recent advancements in the field of optical biopsy of lung tissue, with a focus on emerging technologies and their involvement in cancer diagnostics and future therapeutic options.

In this work, we report the synthesis of functional biocompatible piezoelectric (1 - x)Ba(Ti0.8Zr0.2)TiO3-x(Ba0.7Ca0.3)TiO3, x = 0.45 (BCZT45), thin films with high piezoelectric properties. Pulsed-laser-based techniques, classical pulsed-laser deposition and matrix-assisted pulsed-laser evaporation, were used to synthesize the BCZT45 thin films. The second technique was employed in order to ensure growth on polymer flexible Kapton substrates. The BCZT45 thin films grown by both techniques show similar structural properties and high piezoelectric coefficient coupling between the mechanical loading and electrical potential. While it has long been shown that the electrical potential favors biological processes like osteogenesis, the assessment of cell adhesion and osteogenic differentiation onto BCZT materials has not yet been demonstrated. We prove here for the first time that BCZT 45 coatings on Kapton polymer substrates provide optimal support for osteogenic differentiation of mesenchymal stem cells in the bone marrow.

Recruitment of new blood vessels from the surrounding tissue is central to tumor progression and involves a fundamental transition of the normal, organized vasculature into a dense disarray of vessels that infiltrates the tumor. At present, studying the co-development of the tumor and recruited normal tissue is experimentally challenging because many of the important events occur rapidly and over short length scales in a dense three-dimensional space. To overcome these experimental limitations, we partially confined tumors within biocompatible and optically clear tissue isolation chambers (TICs) and implanted them in mice to create a system that is more amenable to microscopic analysis. Our goal was to integrate the tumor into a recruited host tissue – complete with vasculature – and demonstrate that the system recapitulates relevant features of the tumor microenvironment. We show that the TICs allow clear visualization of the cellular events associated with tumor growth and progression at the host–tumor interface including cell infiltration, matrix remodeling and angiogenesis. The tissue within the chamber is viable for more than a month, and the process is robust in both the skin and brain. Treatment with losartan, an angiotensin II receptor antagonist, decreased the collagen density and fiber length in the TIC, consistent with the known activity of this drug. We further show that collagen fibers display characteristic tumor signatures and play a central role in angiogenesis, guiding the migration of tethered endothelial sprouts. The methodology combines accessible methods of microfabrication with animal models and will enable more informative studies of the cellular mechanisms of tumor progression.

Background and Objective: Navigation of a flexible endoscopic ultrasound (EUS) probe inside the gastrointestinal (GI) tract is problematic due to the small window size and complex anatomy. The goal of the present study was to test the feasibility of a novel fusion imaging (FI) system which uses electromagnetic (EM) sensors to co-register the live EUS images with the pre-procedure computed tomography (CT) data with a novel navigation algorithm and catheter. Methods: An experienced gastroenterologist and a novice EUS operator tested the FI system on a GI tract bench top model. Also, the experienced gastroenterologist performed a case series of 20 patients during routine EUS examinations. Results: On the bench top model, the experienced and novice doctors reached the targets in 67 ΁ 18 s and 150 ΁ 24 s with a registration error of 6 ΁ 3 mm and 11 ΁ 4 mm, respectively. In the case series, the total procedure time was 24.6 ΁ 6.6 min, while the time to reach the clinical target was 8.7 ΁ 4.2 min. Conclusions: The FI system is feasible for clinical use, and can reduce the learning curve for EUS procedures and improve navigation and targeting in difficult anatomic locations.

Objectives Vascular networks respond to chronic alterations in blood supply by structural remodeling. Previously, we showed that blood flow changes in the mouse gracilis artery lead to transient diameter increases, which can generate large increases in circumferential wall stress. Here, we examine the associated changes in the medial area of the arterial wall and the effects on circumferential wall stress.

During the typical healing response to an implanted biomaterial, vascular-rich granulation tissue forms around the implant and later resolves into a relatively avascular, fibrous capsule. We have previously shown that a microvascular construct (MVC) consisting of isolated microvessel fragments suspended in a collagen I gel forms a persistent microcirculation in lieu of avascular scar when implanted. The current study evaluated the potential for microvascular constructs to maintain a vascularized tissue environment around an implanted biomaterial. An analysis of the peri-implant tissue around bare expanded polytetrafluoroethylene (ePTFE), ePTFE embedded within a microvascular construct, or ePTFE embedded within collagen alone revealed that the presence of the MVC, but not collagen alone, promoted vascular densities comparable to that of the granulation tissue formed around bare ePTFE. The vessels within the microvascular construct surrounding the ePTFE were perfusion competent, as determined by India ink perfusion casting, and extended into the interstices of the polymer. In contrast to bare ePTFE, the presence of the MVC or collagen alone significantly reduced the number of activated macrophages in association with ePTFE. Similar results were observed for ePTFE modified to increase cellularity and prevent the formation of an avascular scar. The microvascular construct may prove effective in forming vascularized tissue environments and limiting the number of activated macrophages around implanted polymers thereby leading to effective implant incorporation.

Background and Objectives: Data on contrast-enhanced endoscopic ultrasound (CE-EUS) for colorectal cancer (CRC) evaluation are scarce. Therefore, we aimed to assess the vascular perfusion pattern in CRC by quantitative CE-EUS and compare it to immunohistochemical and genetic markers of angiogenesis. Patients and Methods: We performed a retrospective analysis of CE-EUS examinations of 42 CRC patients, before any therapy. CE-EUS movies were processed using a dedicated software. Ten parameters were automatically generated from the time-intensity curve (TIC) analysis: peak enhancement (PE), rise time (RT), mean transit time, time to peak (TTP), wash-in area under the curve (WiAUC), wash-in rate (WiR), wash-in perfusion index (WiPI), wash-out AUC (WoAUC), and wash-in and wash-out AUC (WiWoAUC). The expression levels of the vascular endothelial growth factor receptor 1 (VEGFR1) and VEGFR2 genes were assessed from biopsy samples harvested during colonoscopy. Microvascular density and vascular area were calculated after CD31 and CD105 immunostaining. Results: Forty-two CE-EUS video sequences were analyzed. We found positive correlations between the parameters PE, WiAUC, WiR, WiPI, WoAUC, WiWoAUC, and N staging (Spearman r = 0.437, r = 0.336, r = 0.462, r = 0.437, r = 0.358, and r = 0.378, respectively, P < 0.05), and also between RT and TTP and CD31 vascular area (r = 0.415, and r = 0.421, respectively, P < 0.05). VEGFR1 and VEGFR2 expression did not correlate with any of the TIC parameters. Conclusions: CE-EUS with TIC analysis enables minimally invasive assessment of CRC angiogenesis and may provide information regarding the lymph nodes invasion. However, further studies are needed for defining its role in the evaluation of CRC patients.

In this chapter, the authors introduce the lean innovation concept in academia with a direct and personal approach, and point out how the academic research and clinical skills are used to develop new medical products. First, the differences between academic and industrial innovation are discussed, and the concept of lean innovation is introduced. In lean innovation, a new medical product’s key attributes are tested using an early prototype or scientific experiments, therefore accelerating the innovation process and reducing development costs. For illustration purposes, a hypothetical new medical product, an atraumatic vascular forceps, is discussed in the remaining sections of the chapter. The authors demonstrate how the most important stages of the early development process apply to this hypothetical product. The first stages of development are forming the social network of medical innovation participants (with the main players being the clinician/inventor and the innovation director), and defining the clinical need and the design inputs and outputs of the product to meet that need. Academic skills such as formulating and testing research hypotheses are used to test product feasibility in research and clinical studies. In parallel with the technical feasibility activities, some business and marketing activities can be done by the clinician and the innovation director such as a prior art search, defining the clinical market of the new product, and raising funds. The authors conclude that innovation and entrepreneurship are fit to the academic environment and should be considered as part of the academic mission. Knowledge of the medical development process and building the appropriate social network to support it could make medical product innovation a familiar reality among academic clinicians and researchers.

The goals of this study were to determine the time course and spatial dependence of structural diameter changes in the mouse gracilis artery after a redistribution of blood flow and to compare the observations with predictions of computational models for structural adaptation. Diameters were measured 1, 2, 7, 14, 21, 28, and 56 days after resection of one of the two blood supplies to the artery. Overall average diameter, normalized with respect to diameters in untreated vessels, increased slightly during the first 7 days, then increased more rapidly, reaching a peak around day 21, and then decreased. This transient increase in diameter was spatially nonuniform, being largest toward the point of resection. A previously developed theoretical model, in which diameter varies in response to stimuli derived from local metabolic and hemodynamic conditions, was extended to include effects of time-delayed remodeling stimuli in regions of reduced perfusion. Predictions of this model were consistent with observed diameter changes, including the transient increase in diameters near the point of resection, when a remodeling stimulus with a time delay of ∼7 days was included. The results suggest that delayed stimuli significantly influence the dynamic characteristics of vascular remodeling resulting from reduced blood supply.

The arterial vascular network of the porcine triceps brachii muscle (TBM) (an extensor muscle to the forearm) was studied and compared to another extensor muscle (the soleus muscle) of small rodents. The left axillary arteries (LAA) of nine Yucatan miniature swine were perfused with latex material to reveal the organization of the arterial blood supply to the TBM. Blood is supplied to the TBM by the main branches of the LAA. Some of the branches end in one of the four heads of the TBM. Other branches continue to bifurcate further and to supply blood to adjacent muscles. The feed arteries (FAs) arise as side-branches at regular distances from the branches of the LAA. The mean number of FAs per TBM was 109.7 and the mean diameter was 388 microns. The distribution of FAs to the muscle area is heterogenous. Most FAs penetrate the epimysium of the TBM on its medial aspect. It appears that more FAs come out of the branches of LAA that end in the TBM as compared to arteries which continue to bifurcate away from TBM. FA diameters were in the range of 300 microns. This study is the first necessary step towards understanding the distribution of oxygen and nutrients to the porcine skeletal muscle. It reveals the presence of a more complicated vascular network than that observed previously in small laboratory animals.

INTRODUCTION: Numerous anti-angiogenic agents are currently developed to limit tumor growth and metastasis. While these drugs offer hope for cancer patients, their transient effect on tumor vasculature is difficult to assess in clinical settings. Confocal laser endomicroscopy (CLE) is a novel endoscopic imaging technology that enables histological examination of the gastrointestinal mucosa. The aim of the present study was to evaluate the feasibility of using CLE to image the vascular network in fresh biopsies of human colorectal tissue. For this purpose we have imaged normal and malignant biopsy tissue samples and compared the vascular network parameters obtained with CLE with established histopathology techniques. MATERIALS AND METHODS: Fresh non-fixed biopsy samples of both normal and malignant colorectal mucosa were stained with fluorescently labeled anti-CD31 antibodies and imaged by CLE using a dedicated endomicroscopy system. Corresponding biopsy samples underwent immunohistochemical staining for CD31, assessing the microvessel density (MVD) and vascular areas for comparison with CLE data, which were measured offline using specific software. RESULTS: The vessels were imaged by CLE in both normal and tumor samples. The average diameter of normal vessels was 8.5±0.9 µm whereas in tumor samples it was 13.5±0.7 µm (p = 0.0049). Vascular density was 188.7±24.9 vessels/mm(2) in the normal tissue vs. 242.4±16.1 vessels/mm(2) in the colorectal cancer samples (p = 0.1201). In the immunohistochemistry samples, the MVD was 211.2±42.9/mm(2) and 351.3±39.6/mm(2) for normal and malignant mucosa, respectively. The vascular area was 2.9±0.5% of total tissue area for the normal mucosa and 8.5±2.1% for primary colorectal cancer tissue. CONCLUSION: Selective imaging of blood vessels with CLE is feasible in normal and tumor colorectal tissue by using fluorescently labeled antibodies targeted against an endothelial marker. The method could be translated into the clinical setting for monitoring of anti-angiogenic therapy.

INTRODUCTION:Confocal laser endomicroscopy (CLE) is becoming a popular method for optical biopsy of digestive mucosa for both diagnostic and therapeutic procedures. Computer aided diagnosis of CLE images, using image processing and fractal analysis can be used to quantify the histological structures in the CLE generated images. The aim of this study is to develop an automatic diagnosis algorithm of colorectal cancer (CRC), based on fractal analysis and neural network modeling of the CLE-generated colon mucosa images. MATERIALS AND METHODS:We retrospectively analyzed a series of 1035 artifact-free endomicroscopy images, obtained during CLE examinations from normal mucosa (356 images) and tumor regions (679 images). The images were processed using a computer aided diagnosis (CAD) medical imaging system in order to obtain an automatic diagnosis. The CAD application includes image reading and processing functions, a module for fractal analysis, grey-level co-occurrence matrix (GLCM) computation module, and a feature identification module based on the Marching Squares and linear interpolation methods. A two-layer neural network was trained to automatically interpret the imaging data and diagnose the pathological samples based on the fractal dimension and the characteristic features of the biological tissues. RESULTS:Normal colon mucosa is characterized by regular polyhedral crypt structures whereas malignant colon mucosa is characterized by irregular and interrupted crypts, which can be diagnosed by CAD. For this purpose, seven geometric parameters were defined for each image: fractal dimension, lacunarity, contrast correlation, energy, homogeneity, and feature number. Of the seven parameters only contrast, homogeneity and feature number were significantly different between normal and cancer samples. Next, a two-layer feed forward neural network was used to train and automatically diagnose the malignant samples, based on the seven parameters tested. The neural network operations were cross-entropy with the results: training: 0.53, validation: 1.17, testing: 1.17, and percent error, resulting: training: 16.14, validation: 17.42, testing: 15.48. The diagnosis accuracy error was 15.5%. CONCLUSIONS:Computed aided diagnosis via fractal analysis of glandular structures can complement the traditional histological and minimally invasive imaging methods. A larger dataset from colorectal and other pathologies should be used to further validate the diagnostic power of the method.

Vascular networks respond to chronic alterations in blood supply by structural remodeling. Previously, we showed that blood flow changes in the mouse GA lead to transient diameter increases, which can generate large increases in circumferential wall stress. Here, we examine the associated changes in the medial area of the arterial wall and the effects on circumferential wall stress.To induce blood flow changes, one of the two feeding vessels to the GA was surgically removed. At 7-56 days after blood flow interruption, the vasculature was perfused with India ink for morphological measurements, and processed for immuno-cytochemistry to mark the medial cross-section area. Theoretical simulations of hemodynamics were used to analyze the data.During adaptive increases in vessel diameter, increases in medial area were observed, most strongly in the middle region of the artery. Simulations showed that this increase in medial area limits the increase in estimated circumferential stress during vascular adaptation to less than 50%, in contrast to an increase of up to 250% if the medial area had remained unchanged.During vascular adaptation, increases in circumferential stress are limited by growth of the media coordinated with diameter changes.

The abnormal tumor microenvironment fuels tumor progression, metastasis, immune suppression, and treatment resistance. Over last several decades, developments in and applications of intravital microscopy have provided unprecedented insights into the dynamics of the tumor microenvironment. In particular, intravital multiphoton microscopy has revealed the abnormal structure and function of tumor-associated blood and lymphatic vessels, the role of aberrant tumor matrix in drug delivery, invasion and metastasis of tumor cells, the dynamics of immune cell trafficking to and within tumors, and gene expression in tumors. However, traditional multiphoton microscopy suffers from inherently slow imaging rates—only a few frames per second, thus unable to capture more rapid events such as blood flow, lymphatic flow, and cell movement within vessels. Here, we report the development and implementation of a video-rate multiphoton microscope (VR-MPLSM) based on resonant galvanometer mirror scanning that is capable of recording at 30 frames per second and acquiring intravital multispectral images. We show that the design of the system can be readily implemented and is adaptable to various experimental models. As examples, we demonstrate the utility of the system to directly measure flow within tumors, capture metastatic cancer cells moving within the brain vasculature and cells in lymphatic vessels, and image acute responses to changes in a vascular network. VR-MPLSM thus has the potential to further advance intravital imaging and provide new insight into the biology of the tumor microenvironment.