Real Tissue Research Articles

Conformal Folded Inverted-F Antenna With Quasi-Isotropic Radiation Pattern for Robust Communication in Capsule Endoscopy Applications

In this article, an antenna with quasi-isotropic radiation pattern is proposed in a 915 MHz industrial, scientific, and medical (ISM) band for robust communication with external devices. The overall structure of the proposed antenna is a folded inverted-F antenna (FIFA). In the simulation, the proposed antenna is integrated with dummy electronics to form a fully encapsulated capsule endoscope architecture. These dummy electronics are encapsulated in a copper cylindrical bucket used as the antenna ground to avoid electromagnetic (EM) interference between these dummy electronics and the antenna. The spherical phantom model is used in the initial design of the proposed antenna. Subsequently, an anatomical realistic phantom was used to evaluate the performance of the proposed antenna in a more realistic human environment. The simulated gain variation (GV) of the proposed antenna in the regular spherical phantom is about 8 dB, and the reflection and other factors caused by the irregular shape of the realistic human tissue environment lead to the GV of the antenna in the anatomically realistic phantom that is about 4 dB. The measurement of reflection coefficient and radiation pattern is carried out in a semisolid muscle-mimicking phantom using a fabricated prototype of the proposed antenna. The measured GV value is about 10.5 dB. Finally, the specific absorption ratio (SAR) was calculated to meet the human safety regulations and the communication link budget was calculated to evaluate the wireless biological telemetry performance of the proposed antenna.

Microwave 3D Imaging System Featuring the Phase Coherence Factor for Improved Beamforming.

This paper presents an improved radar-based imaging system for breast cancer detection that features p-slot ultrawideband antennae in a 32-array set-up. The improved reconstruction algorithm incorporates the phase coherence factor (PCF) into the conventional delay and sum (DAS) beamforming algorithm, thus effectively suppressing noise arising from the side- and gratinglobe interferences. The system is tested by using several breast models fabricated from chemical mixtures formulated on the basis of realistic human tissues. Each model is placed in a hemispherical breast radome that was fabricated from polylactide material and surrounded by 32 p-slot antennae mounted in four concentric layers. These antennae are connected to an 8.5 GHz vector network analyser through two 16-channel multiplexers that automatically switch different combinations of transmitter and receiver pairs in a sequential manner. The system can accurately detect 5 mm tumours in a complex and homogeneously dense 3D breast model with an average signal-to-clutter ratio and full-width half-maximum of 7.0 dB and 2.3 mm, respectively. These values are more competitive than the values of other beamforming algorithms, even with contrasts as low as 1:2. The proposed PCF-weighted DAS is the best-performing algorithm amongst the tested beamforming techniques. This research paves the way for a clinical trial involving human subjects. Our laboratory is planning such a trial as part of future work.

A numerical scheme for the one-dimensional neural field model

Neural field models, typically cast as continuum integro-differential equations, are widely studied to describe the coarse-grained dynamics of real cortical tissue in mathematical neuroscience. Studying these models with a sigmoidal firing rate function allows a better insight into the stability of localised solutions through the construction of specific integrals over various synaptic connectivities. Because of the convolution structure of these integrals, it is possible to evaluate neural field model using a pseudo-spectral method, where Fourier Transform (FT) followed by an inverse Fourier Transform (IFT) is performed, leading to a new identical partial differential equation. In this paper, we revisit a neural field model with a nonlinear sigmoidal firing rate and provide an efficient numerical algorithm to analyse the model regarding finite volume scheme. On the other hand, numerical results are obtained by the algorithm.

The Effect of Pattern and Infill Percentage in 3D Printer for Phantom Radiation Applications

3D printing technology was capable of fabricating phantoms to enhance quality assurance in radiation therapy. The ideal phantom has properties equivalent to the real tissue. However, 3D Printing has the limits to mimicking the attenuation properties of various tissues because during 3D printing there can be only one type of material. The purpose of this study was to evaluate the effect of infill percentage and infill patterns of 3D printing technology to simulate various types of tissue. This study used 25 samples measuring 5 × 5 × 1 cm3 from PETG material. The 20 samples were printed using variations infill percentages from 5 - 100% and the infill pattern in lines. The five samples were then printed with the infill percentage constant at 50% and used the infill pattern triangles, grid, gyroid, octet, and concentric. We used Computed Tomography (CT) to determine the Hounsfield Unit (HU) value for each sample and evaluated the suitability of each sample for phantom applications in radiation therapy and radiology. However, none of the samples was able to simulate compact bone. As a result, we found that PETG material could simulate the properties of soft tissue, fat, lung, kidney, liver, pancreas, and spongy bone. Thus, the study had shown promising potential for the fabrication of the anthropomorphic phantom of radiation therapy.

Development and Validation of a Virtual Reality Simulator for Robot-Assisted Minimally Invasive Liver Surgery Training.

The value of kinematic data for skill assessment is being investigated. This is the first virtual reality simulator developed for liver surgery. This simulator was coded in C++ using PhysX and FleX with a novel cutting algorithm and used a patient data-derived model and two instruments functioning as ultrasonic shears. The simulator was evaluated by nine expert surgeons and nine surgical novices. Each participant performed a simulated metastasectomy after training. Kinematic data were collected for the instrument position. Each participant completed a survey. The expert participants had a mean age of 47 years and 9/9 were certified in surgery. Novices had a mean age of 30 years and 0/9 were certified surgeons. The mean path length (novice 0.76 ± 0.20 m vs. expert 0.46 ± 0.16 m, p = 0.008), movements (138 ± 45 vs. 84 ± 32, p = 0.043) and time (174 ± 44 s vs. 102 ± 42 s, p = 0.004) were significantly different for the two participant groups. There were no significant differences in activating the instrument (107 ± 25 vs. 109 ± 53). Participants considered the simulator realistic (6.5/7) (face validity), appropriate for education (5/7) (content validity) with an effective interface (6/7), consistent motion (5/7) and realistic soft tissue behavior (5/7). This study showed that the simulator differentiates between experts and novices. Simulation may be an effective way to obtain kinematic data.

Additive Manufacturing of Viscoelastic Polyacrylamide Substrates for Mechanosensing Studies.

Polymerized polyacrylamide (PAA) substrates are linearly elastic hydrogels that are widely used in mechanosensing studies due to their biocompatibility, wide range of functionalization capability, and tunable mechanical properties. However, such cellular response on purely elastic substrates, which do not mimic the viscoelastic living tissues, may not be physiologically relevant. Because the cellular response on 2D viscoelastic PAA substrates remains largely unknown, we used stereolithography (SLA)-based additive manufacturing technique to create viscoelastic PAA substrates with tunable mechanical properties that allow us to identify physiologically relevant cellular behaviors. Three PAA substrates of different complex moduli were fabricated by SLA. By embedding fluorescent markers during the additive manufacturing of the substrates, we show a homogeneous and uniform composition throughout, which conventional manufacturing techniques cannot produce. Rheological investigation of the additively manufactured PAA substrates shows a viscoelastic behavior with a 5–10% loss moduli compared to their elastic moduli, mimicking the living tissues. To understand the cell mechanosensing on the dissipative PAA substrates, single live cells were seeded on PAA substrates to establish the basic relationships between cell traction, cytoskeletal prestress, and cell spreading. With the increasing substrate moduli, we observed a concomitant increase in cellular traction and prestress, but not cell spreading, suggesting that cell spreading can be decoupled from traction and intracellular prestress in physiologically relevant environments. Together, additively manufactured PAA substrates fill the void of lacking real tissue like viscoelastic materials that can be used in a variety of mechanosensing studies with superior reproducibility.

Structure and function of cancer-related developmentally regulated GTP-binding protein 1 (DRG1) is conserved between sponges and humans

Cancer is a disease caused by errors within the multicellular system and it represents a major health issue in multicellular organisms. Although cancer research has advanced substantially, new approaches focusing on fundamental aspects of cancer origin and mechanisms of spreading are necessary. Comparative genomic studies have shown that most genes linked to human cancer emerged during the early evolution of Metazoa. Thus, basal animals without true tissues and organs, such as sponges (Porifera), might be an innovative model system for understanding the molecular mechanisms of proteins involved in cancer biology. One of these proteins is developmentally regulated GTP-binding protein 1 (DRG1), a GTPase stabilized by interaction with DRG family regulatory protein 1 (DFRP1). This study reveals a high evolutionary conservation of DRG1 gene/protein in metazoans. Our biochemical analysis and structural predictions show that both recombinant sponge and human DRG1 are predominantly monomers that form complexes with DFRP1 and bind non-specifically to RNA and DNA. We demonstrate the conservation of sponge and human DRG1 biological features, including intracellular localization and DRG1:DFRP1 binding, function of DRG1 in α-tubulin dynamics, and its role in cancer biology demonstrated by increased proliferation, migration and colonization in human cancer cells. These results suggest that the ancestor of all Metazoa already possessed DRG1 that is structurally and functionally similar to the human DRG1, even before the development of real tissues or tumors, indicating an important function of DRG1 in fundamental cellular pathways.

Sensitive determination of metalloprotein in salt-rich matrices by size exclusion chromatography coupled with inductively coupled plasma-mass spectrometry

Metalloproteins play crucial and distinct roles in a variety of biological processes that rely heavily on the metal ions and various proteins. However, there is still a lack of method for rapid analysis of metalloproteins in complex samples, especially in salt-rich matrices. In this study, a sensitive method for separation and determination of metalloproteins in salt-rich matrices was developed based on the size exclusion chromatography coupled with inductively coupled plasma-mass spectrometry (SEC-ICP-MS), combining with the high matrix introduction (HMI) mode, which is quite essential for biological system. The separation conditions of the SEC-ICP-MS system were optimized by using four iodine labeled proteins with different molecular weights, including bovine serum albumin (BSA, 66.0 kDa), ovalbumin (OVA, 44.0 kDa), carbonic anhydrase (CA, 29.0 kDa) and ribonuclease A (RA, 13.7 kDa). After optimization, four iodine labeled proteins and iodine ions were successfully separated within 30 min by using 10 mmol/L HEPES and 40 mmol/L Na2SO4 (pH=7.0) as mobile phase and a linear relationship between log molecular weight and retention time was established. The relative standard deviations (RSDs, n = 5) of the retention time and peak areas for the four iodine labeled proteins were in the range of 0.2–0.9% and 3.3–7.7%, respectively, suggesting good precision and repeatability. Then the proposed method was successfully applied to the rapid separation and detection of lead-binding proteins in real biological tissue samples.

Design and Mechanical Characterization Using Digital Image Correlation of Soft Tissue-Mimicking Polymers.

Present and future anatomical models for biomedical applications will need bio-mimicking three-dimensional (3D)-printed tissues. These would enable, for example, the evaluation of the quality-performance of novel devices at an intermediate step between ex-vivo and in-vivo trials. Nowadays, PolyJet technology produces anatomical models with varying levels of realism and fidelity to replicate organic tissues. These include anatomical presets set with combinations of multiple materials, transitions, and colors that vary in hardness, flexibility, and density. This study aims to mechanically characterize multi-material specimens designed and fabricated to mimic various bio-inspired hierarchical structures targeted to mimic tendons and ligaments. A Stratasys® J750™ 3D Printer was used, combining the Agilus30™ material at different hardness levels in the bio-mimicking configurations. Then, the mechanical properties of these different options were tested to evaluate their behavior under uni-axial tensile tests. Digital Image Correlation (DIC) was used to accurately quantify the specimens’ large strains in a non-contact fashion. A difference in the mechanical properties according to pattern type, proposed hardness combinations, and matrix-to-fiber ratio were evidenced. The specimens V, J1, A1, and C were selected as the best for every type of pattern. Specimens V were chosen as the leading combination since they exhibited the best balance of mechanical properties with the higher values of Modulus of elasticity (2.21 ± 0.17 MPa), maximum strain (1.86 ± 0.05 mm/mm), and tensile strength at break (2.11 ± 0.13 MPa). The approach demonstrates the versatility of PolyJet technology that enables core materials to be tailored based on specific needs. These findings will allow the development of more accurate and realistic computational and 3D printed soft tissue anatomical solutions mimicking something much closer to real tissues.

P75: The Next Generation of Cardiac BioSimulators: Model-Based Venticular Behaviour with Pressure Feedback Control

Study: Physical cardiac simulators are widely used in surgical training as well as in preclinical device testing. Their fidelity rapidly advances, but often lacks patient-specific cardiac responses. On the other hand, computational simulations of the complete cardiovascular system are being investigated for years but rarely come together in a physical lab model. This study will show our progress in implementing real-time responsive models in a physical cardiac biosimulator. Methods: In this work we implemented a ventricular contraction model combined with a baroreflex response in LifeTec Group’s Cardiac BioSimulator. This BioSimulator generates flow through hearts obtained from abattoirs using a piston pump. We programmed the pump to mimic the heart’s contraction, and to respond to real-time pre- and afterload changes. The contraction model describes the ventricular behavior based on experimental data of myocardial fibers. Next, we will perform experiments with a pump connected to the left ventricle mimicking LVAD functionality and demonstrate ventricular unloading responses. Results: Contraction behavior and baroreflex response were implemented successfully in the pump model, thus representing contractile functionality. Preliminary tests on a simple benchtop model already showed real-time response in PV-loops by adjusting afterload resistance. Recently we added baroreflex functionality that describes heart rate response. Next results will focus on LVAD support simulations on the Cardiac Biosimulator. Conclusion: By combining a state-of-the-art cardiac simulator with a model that describes physiological contraction behavior of the heart, we are one step closer to a realistic simulator that comprises both anatomy and real tissue feedback as well as the correct physiological response that could even be used to assess and train the effect of an intervention.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

A Present Update of Camostate and the Role in SARS-CoV-2 Infection

SARS-CoV-2 has triggered a pandemic of "coronavirus disease-2019" (Covid-19). Viral cell entry of coronaviruses relies on the binding of viral spike protein (S protein) to cellular receptors and S type of protein processing by the host cell proteases. It is known that SARS-CoV-2 uses the human receptor ACE2 for cell entry and the serine protease TMPRSS2 for "priming" of the S protein. Camostate has antiviral properties against coronaviruses. The effects are due to inhibition of the endogenous protease TMPRSS2, which the SARS-CoV-2 virus requires for the host cell entry (as well as exit). Camostate is a serine protease inhibitor. Camostate, which is already in clinical use, reduces cell infection. So far, this has been shown exclusively in cell lines or cell cultures, which do not reflect the complexity of the three-dimensional alveolar lung cell association with type I, type II cells, endothelial cells and alveolar macrophages.It is well known, that Camostate blocks TMPRSS2 and related proteases [1-36]. For SARS-CoV-2 to enter lung cells, the virus must be activated by the cellular protease TMPRSS2. Camostateis used in Japan to treat inflammation of the pancreas, blocks the protease TMPRSS2 and thereby inhibit SARS-CoV-2[1-18,20-29,31-35]. However, it was unclear whether the camostate degradation product GBPA also inhibits the virus and whether the virus can use related proteases for infection in addition to TMPRSS2, which may not be inhibited by camostate[18,20,29]. These proteases are available to the virus for replication in the upper respiratory tract and are inhibited by camostate[1-36]. It is therefore not possible for SARS-CoV-2 to evade the antiviral effect of Camostate by switching to related proteases instead of TMPRSS2. In further studies, the researchers were able to show that in addition to Camostate, the primary Camostate metabolite GBPA also inhibits the protease TMPRSS2, thereby blocking SARS-CoV-2 infection. Camostate is converted to GBPA in the body within a very short time.GBPA can exert an antiviral effect in patients. A higher dosage of Camostate to effectively treat COVID-19 than to treat pancreatic inflammation may be required [33,36]. Inhibition of SARS-CoV-2 by Camostate had initially been shown only in the lung cell line Calu-3[6]. The involvement of researchers from Hannover, Germany, made it possible to analyze the antiviral effect of Camostatein real lung tissue as well [6]. Camostate and GBPA inhibit SARS-CoV-2 infection of lung tissue [15]. The camostatemesilate-containing drug Foipan® has been available on the Japanese market since 1986 [1-36]. It is used to treat chronic inflammation of the pancreas and postoperative reflux esophagitis [33,36]. According to the product information, the substance has an inhibitory effect on several enzymes and intervenes in various systems in the body. Among other things, an inhibitory effect on trypsin and normalization of amylase levels are mentioned [33,36]. However, the reason why this substance suddenly becomes interesting in the corona crisis is different [24,26,30].

Mechanics of Three-Dimensional Soft Network Materials With a Class of Bio-Inspired Designs

Abstract Inspired by the helix-shaped microstructures found in many collagenous tissues, a class of three-dimensional (3D) soft network materials that incorporate similar helical microstructures into periodic 3D lattices was reported recently. Owing to their high stretchability, high air permeability, defect-insensitive behavior, and capabilities of reproducing anisotropic J-shaped stress–strain curves of real biological tissues (e.g., heart muscles), these 3D soft network materials hold great promise for applications in tissue engineering and bio-integrated devices. Rapid design optimization of such soft network materials in practical applications requires a relevant mechanics model to serve as the theoretical basis. This paper introduces a nonlinear micromechanics model of soft 3D network materials with cubic and octahedral lattice topologies, grounded on the development of finite-deformation beam theory for the 3D helical microstructure (i.e., the building-block structure of 3D network materials). As verified by finite element analysis (FEA) and experimental measurements, the developed model can well predict the anisotropic J-shaped stress–strain curves and deformed configurations under large levels of uniaxial stretching. The theoretical model allows a clear understanding of different roles of microstructure parameters on the J-shaped stress–strain curve (that is characterized by the critical strain of mode transition, as well as the stress and the tangent modulus at the critical strain). Furthermore, we demonstrate the utility of the theoretical model in the design optimization of 3D soft network materials to reproduce the target isotropic/anisotropic stress–strain curves of real biological tissues.

Graphics-processing-unit-accelerated Monte Carlo simulation of polarized light in complex three-dimensional media.

.SignificanceMonte Carlo (MC) methods have been applied for studying interactions between polarized light and biological tissues, but most existing MC codes supporting polarization modeling can only simulate homogeneous or multi-layered domains, resulting in approximations when handling realistic tissue structures.AimOver the past decade, the speed of MC simulations has seen dramatic improvement with massively parallel computing techniques. Developing hardware-accelerated MC simulation algorithms that can accurately model polarized light inside three-dimensional (3D) heterogeneous tissues can greatly expand the utility of polarization in biophotonics applications.ApproachHere, we report a highly efficient polarized MC algorithm capable of modeling arbitrarily complex media defined over a voxelated domain. Each voxel of the domain can be associated with spherical scatters of various radii and densities. The Stokes vector of each simulated photon packet is updated through photon propagation, creating spatially resolved polarization measurements over the detectors or domain surface.ResultsWe have implemented this algorithm in our widely disseminated MC simulator, Monte Carlo eXtreme (MCX). It is validated by comparing with a reference central-processing-unit-based simulator in both homogeneous and layered domains, showing excellent agreement and a 931-fold speedup.ConclusionThe polarization-enabled MCX offers biophotonics community an efficient tool to explore polarized light in bio-tissues, and is freely available at http://mcx.space/.

Decomposition of a Tissue Into Its Constituent Elements for Specifying the Values of Doses

In this research, a real tissue has been decomposed to its constituent materials and elements applying its volumetric density and initial mass. This study has been carried out to obtain the exact amounts of constituent elements existing in liver tissue as well as specify the absorbed dose because of the tissue getting exposed to radiation in radiotherapy and radiography. In order, a medical model has been defined in a way that its compositions are just like the materials existing in the tissue of a human. Then, the accurate mass, density, and volume of every element in it are specified. In the next stage, the related tissue is exposed to a neutron beam, and then the values of doses absorbed in the main constituent material namely water, and also the total absorbed dose is obtained by the MCNP nuclear code. This study can be used to study the radiotherapy and radiography scopes for obtaining the absorbed dose as a result of interaction between radiation beam and biological cells. In this research, the values of absorbed doses have been obtained for a wide range of neutron energies in a way that the absorbed doses can accurately be obtained.

Development of a Training Model for Teaching Intrauterine Fetal Blood Transfusion

This article describes an inexpensive simulator developed for teaching intrauterine blood transfusion. The model is constructed from a boneless chicken thigh folded over a Penrose drain placed in a water-filled snap-lock lid container and covered by melted ballistic gel to simulate the fetal intrahepatic vessel. Participants valued this educational tool and reported feeling the model was practical and realistic. This low-cost, high-fidelity model provides realistic tissue resistance and represents a sonographically accurate intrahepatic fetal blood transfusion training tool.

Recent Advances and Perspective of Nanotechnology-Based Implants for Orthopedic Applications.

Bioimplant engineering strives to provide biological replacements for regenerating, retaining, or modifying injured tissues and/or organ function. Modern advanced material technology breakthroughs have aided in diversifying ingredients used in orthopaedic implant applications. As such, nanoparticles may mimic the surface features of real tissues, particularly in terms of wettability, topography, chemistry, and energy. Additionally, the new features of nanoparticles support their usage in enhancing the development of various tissues. The current study establishes the groundwork for nanotechnology-driven biomaterials by elucidating key design issues that affect the success or failure of an orthopaedic implant, its antibacterial/antimicrobial activity, response to cell attachment propagation, and differentiation. The possible use of nanoparticles (in the form of nanosized surface or a usable nanocoating applied to the implant’s surface) can solve a number of problems (i.e., bacterial adhesion and corrosion resilience) associated with conventional metallic or non-metallic implants, particularly when implant techniques are optimised. Orthopaedic biomaterials’ prospects (i.e., pores architectures, 3D implants, and smart biomaterials) are intriguing in achieving desired implant characteristics and structure exhibiting stimuli-responsive attitude. The primary barriers to commercialization of nanotechnology-based composites are ultimately discussed, therefore assisting in overcoming the constraints in relation to certain pre-existing orthopaedic biomaterials, critical factors such as quality, implant life, treatment cost, and pain alleviation.

Modeling the C. elegans germline stem cell genetic network using automated reasoning

Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision-making between stem cells and differentiation. We model the “stem cell fate” versus entry into the “meiotic development” pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. A methodological contribution of this work is demonstrating how to effectively model within a formal reasoning framework a complex genetic network with a wealth of known experimental data and constraints. We provide a summary of the steps we have found useful for the development and analysis of this model and can potentially be applicable to other genetic networks. This work also lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.

Construction of plant-based adipose tissue using high internal phase emulsions and emulsion gels

There is growing consumer demand for plant-based meat and seafood analogs due to ethical, environmental, and health concerns associated with the production of real meat and seafood. Meat and seafood analogs should mimic the desirable appearance, texture, and flavor of the real versions. In this study, we investigated the possibility of using advanced emulsion technologies to create plant-based adipose tissue. High internal phase emulsions (HIPEs) were formulated that consisted of concentrated dispersions of soybean protein-coated soybean oil droplets. The HIPEs contained 75% soybean oil and 0.25 to 3% soybean protein. At higher protein contents, the HIPEs mimicked the appearance of beef adipose tissue but were too soft at ambient temperature and did not melt upon heating. These problems could be partly overcome by using emulsion gels that consisted of soybean protein-coated soybean oil droplets dispersed in an agar hydrogel. The final composition of these emulsion gels was 60% soybean oil, 2% soybean protein, and 0.25 to 2% agar. The incorporation of the agar increased the hardness of the emulsion gels at ambient temperature and led to melting behavior. Nevertheless, the emulsion gels were still somewhat softer that real beef adipose tissue at ambient temperature and they melted at a higher temperature. These results show that concentrated emulsion gels containing cold-setting polysaccharides may be useful for mimicking the desirable physicochemical attributes of animal adipose tissue but further research is required to more accurately simulate their properties.

Characterization of signal kinetics in real time surgical tissue classification system

Effective surgical margin assessment is paramount for good oncological outcomes and new methods are in active development. One emerging approach is the analysis of the chemical composition of surgical smoke from tissues. Surgical smoke is typically removed with a smoke evacuator to protect the operating room staff from its harmful effects to the respiratory system. Thus, analysis of the evacuated smoke without disturbing the operation is a feasible approach. Smoke transportation is subject to lags that affect system usability. We analyzed the smoke transportation delay and evaluated its effects to tissue classification with differential mobility spectrometry in a simulated setting using porcine tissues. With a typical smoke evacuator setting, the front of the surgical plume reaches the analysis system in 380 ms and the sensor within one second. For a typical surgical incision (duration 1.5 s), the measured signal reaches its maximum in 2.3 s and declines to under 10% of the maximum in 8.6 s from the start of the incision. Two-class tissue classification was tested with 2, 3, 5, and 11 s repetition rates resulting in no significant differences in classification accuracy, implicating that signal retention from previous samples is mitigated by the classification algorithm.