Regeneration of Mechanotransducers in Agonist-Antagonist Myoneural Interface for Neural Prosthetic Control
The current surgical paradigm for amputation leaves nerves transected, and residual musculature haphazardly configured, rendering efferent recordings difficult and unreliable for prosthetic control. Notably, the physiological agonist-antagonist muscle relationships are severed, precluding the generation of musculotendinous proprioceptive afferent signals. This afferent feedback is critical for motor control, balance, and trajectory planning and is primarily generated by two muscle-based mechanotransducers: 1) spindle fibers, which sense muscle fascicle stretch, and 2) golgi tendon organs, which sense force. Here, we created and evaluated an ‘Agonist-antagonist Myoneural Interface’ (AMI). As recently established by our lab, this is a novel surgical paradigm for peripheral nerve interfacing, which reinstates the antagonist-agonist architecture native to intact musculoskeletal anatomy and provides a robust method for bidirectional (efferent and afferent) signaling with motor nerves. After reinnervation of the muscle grafts, we studied the ability of the system to provide isolated electromyographic (EMG) signals in a longitudinal fashion and regenerate the mechanotransducers responsible for afferent signaling. Methodology: Muscle grafts reinnervated with either the transected peroneal or tibial nerves (a flexor-extensor pair) were coapted at the tendons to form an agonist-antagonist pair and secured to fascia (n=10, murine model). 4-6 weeks post-operatively, electrophysiology was performed to assess the quality and isolation of efferent command signals. We electrically stimulated the regenerated motor nerve of the agonist muscle while measuring agonist EMG and the afferent ENG signal generated by the antagonist muscle. In addition, histological studies were performed to evaluate myocyte, neural, and fibrotic tissue remodeling as well as the formation of functional spindle and Golgi tendon organs through standard hemotoxylin and eosin staining (H&E) and trichrome as well as a special spindle stain (s46). Results: Our results indicate healthy regeneration of tissue and preservation of muscle spindles and Golgi tendon organs in the regenerated muscle grafts providing the tissue-level basis for afferent feedback generation. Furthermore, the signal-to-noise ratio (35-55 dB) and quality of electromyographic and electroneurographic signals suggest promise of this surgical approach to serve as a stable interface for efferent control of a neuroprosthesis over time. In the future, this surgical paradigm can be used in place of standard procedures, allowing regenerative muscle grafts to serve as actuators for robotic prostheses and enabling sensory feedback signals- advancing the realm of prosthetic functionality.
Design of Nanoreinforced Polyacrylamide-Based Hybrid Hydrogels for Bone Tissue Engineering
Bone tissue engineering has emerged as a potentially alternative method for localized bone defects or diseases, congenital deformation, and surgical reconstruction. The designing and the fabrication of the ideal scaffold is a great challenge, in restoring of the damaged bone tissues via cell attachment, proliferation, and differentiation under three-dimensional (3D) biological micro-/nano-environment. In this case, hydrogel system composed of high hydrophilic 3D polymeric-network that is able to mimic some of the functional physical and chemical properties of the extracellular matrix (ECM) and possibly may provide a suitable 3D micro-/nano-environment (i.e., resemblance of native bone tissues). Thus, this proposed hydrogel system is highly permeable and facilitates the transport of the nutrients and metabolites. However, the use of hydrogels in bone tissue engineering is limited because of their low mechanical properties (toughness and stiffness) that continue to posing challenges in designing and fabrication of tough and stiff hydrogels along with improved bioactive properties. For this purpose, in our lab, polyacrylamide-based hybrid hydrogels were synthesized by involving sodium alginate, cellulose nanocrystals and silica-based glass using one-step free-radical polymerization. The results showed good in vitro apatite-forming ability (biomineralization) and improved mechanical properties (under compression in the form of strength and stiffness in both wet and dry conditions), and in vitro osteoblastic (MC3T3-E1 cells) cytocompatibility. For in vitro cytocompatibility assessment, both qualitative (attachment and spreading of cells using FESEM) and quantitative (cell viability and proliferation using MTT assay) analyses were performed. The obtained hybrid hydrogels may potentially be used in bone tissue engineering applications after establishment of in vivo characterization.
Fabrication of Biosensor Based on Layered Double Hydroxide/Polypyrrole/Carbon Paste Electrode for Determination of Anti-Hypertensive and Prostatic Hyperplasia Drug Terazosin
New insights into the design of highly sensitive, carbon-based electrochemical sensors are presented in this work. This was achieved by exploring the interesting properties of conductive (Mg/Al) layered double hydroxide- Dodecyl Sulphate/Polypyrrole nanocomposites which were synthesized by in-situ polymerization of pyrrole during the assembly of (Mg/Al) layered double hydroxide, and by employing the anionic surfactant Dodecyl sulphate as a modifier. The morphology and surface area of the nanocomposites changed with the percentage of Pyrrole. Under optimal conditions, the modified carbon paste electrode successfully achieved detection limits of 0.057 and 0.134 nmol.L-1 of Terazosin hydrochloride in pharmaceutical formulation and spiked human serum fluid, respectively. Moreover, the sensors are highly stable, reusable, and free from interference by other commonly present excipients in drug formulations.
Integrative Omics-Portrayal Disentangles Molecular Heterogeneity and Progression Mechanisms of Cancer
Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.
Wireless Based System for the Continuous Electrocardiography Monitoring during Surgery
This paper presents a system designed for the wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of heart’s health during a surgery even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. We have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre and post processing steps are briefly discussed. Experimental results are also provided.
Personalized Fingers Design with Silicone Rubber Textured Fingertips for Increased Grip in Transmetacarpal Powered Hand Prostheses
An important limitation of most upper-limb prostheses is the size standardization, which results in noticeable differences between the amputee’s prosthesis and the healthy hand. The need for customization of fingers and palm designs can be met with parametric modeling and 3D-printing, a manufacture technology suitable for low scale, personalized objects. This work presents a design for custom fingers’ phalanges conformed by four-bar linkages and rigid links which lengths are modified according to a user specific hand anthropometry. Additionally, to increase the actual load that the prosthesis can lift, it is essential to take into account its fingers’ friction coefficient, which depends on the materials in contact. Commercial powered prosthesis, such as i-limb and Bebionic, have powerful micro motors with torques comparable to the exerted in human hand articulations. However, they lack the compliant texture of human skin and fingertips ridges, which are significant factors for grasp effectiveness. These ridges coefficient of friction is comparable to that of elastomers, and in this work, they are designed for the customizable fingers using a bio-inspired texture based on silicone rubber. The actuators and other components are arranged in the hand prosthesis in a way that a stump of a transmetacarpal amputee can fit and the design can be extended for more proximal levels of below-elbow amputations.
Evaluation and Usage of Computer-Aided Design Tools for Automatic Creation of Custom-Fitted Hand Prostheses Using Open-Source Designs
Custom upper-limb prostheses are expensive and difficult to develop because they require substantial manual design and manufacture processing. In the case of open-source 3D-printed hand prostheses, there are many models that are easy to assemble and low-cost but lack a proper interface between the rigid parts and the stump. Furthermore, these prostheses have to be scaled correctly to the patient specific anthropometry and must provide a tight fit while also protecting sensitive regions of the stump from irritation. A series of custom offsets between the prosthesis and the stump is commonly used to provide this protective buffer. Due to these challenges, upper-limb prostheses design and fitting becomes complicated and time-consuming, thus making these devices inaccessible to many. Unfortunately, unless prostheses are developed for patients quickly, many reject them in favor of the convenience of improvised grasps. Similarly, many children abandon prostheses because they cannot be regularly fitted for new prostheses as they grow and would otherwise be forced to use uncomfortable and improperly sized prostheses. Developing a method for automatically and rapidly customizing prostheses would reduce the cost and labor associated with the customization processes. This paper analyzes the ability of common CAD tools, such as Autodesk Inventor, Fusion 360, and Rhino, to automatically size and adapt open-source prostheses to patients’ hands based on the 3D-scan of the residual limb. The results demonstrate the unparalleled robustness of Rhino’s Grasshopper plugin to automate complex functions. This work also presents a novel program developed in Grasshopper to automatically create custom-fitted prostheses including protective offsets for sensitive areas with minimum manual intervention.
Reagentless Detection of Urea Based on ZnO-CuO Composite Thin Film
A reagentless biosensor for detection of urea based on ZnO-CuO composite thin film is presented in following work. Biosensors have immense potential for varied applications ranging from environmental to clinical testing, health care, and cell analysis. Immense growth in the field of biosensors is due to the huge requirement in today’s world to develop techniques which are both cost effective and accurate for prevention of disease manifestation. The human body comprises of numerous biomolecules which in their optimum levels are essential for functioning. However mismanaged levels of these biomolecules result in major health issues. Urea is one of the key biomolecules of interest. Its estimation is of paramount significance not only for healthcare sector but also from environmental perspectives. If level of urea in human blood/serum is abnormal, i.e., above or below physiological range (15-40mg/dl)), it may lead to diseases like renal failure, hepatic failure, nephritic syndrome, cachexia, urinary tract obstruction, dehydration, shock, burns and gastrointestinal, etc. Various metal nanoparticles, conducting polymer, metal oxide thin films, etc. have been exploited to act as matrix to immobilize urease to fabricate urea biosensor. Amongst them, Zinc Oxide (ZnO), a semiconductor metal oxide with a wide band gap is of immense interest as an efficient matrix in biosensors by virtue of its natural abundance, biocompatibility, good electron communication feature and high isoelectric point (9.5). In spite of being such an attractive candidate, ZnO does not possess a redox couple of its own which necessitates the use of electroactive mediators for electron transfer between the enzyme and the electrode, thereby causing hindrance in realization of integrated and implantable biosensor. In the present work, an effort has been made to fabricate a matrix based on ZnO-CuO composite prepared by pulsed laser deposition (PLD) technique in order to incorporate redox properties in ZnO matrix and to utilize the same for reagentless biosensing applications. The prepared bioelectrode Urs/(ZnO-CuO)/ITO/glass exhibits high sensitivity (70µAmM⁻¹cm⁻²) for detection of urea (5-200 mg/dl) with high stability (shelf life ˃ 10 weeks) and good selectivity (interference ˂ 4%). The enhanced sensing response obtained for composite matrix is attributed to the efficient electron exchange between ZnO-CuO matrix and immobilized enzymes, and subsequently fast transfer of generated electrons to the electrode via matrix. The response is encouraging for fabricating reagentless urea biosensor based on ZnO-CuO matrix.
Electrochemical Biosensor Based on Chitosan-Gold Nanoparticles, Carbon Nanotubes for Detection of Ovarian Cancer Biomarker
Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.
Mathematical Modeling for Diabetes Prediction: A Neuro-Fuzzy Approach
Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.
Fe Modified Tin Oxide Thin Film Based Matrix for Reagentless Uric Acid Biosensing
Biosensors have found potential applications ranging from environmental testing and biowarfare agent detection to clinical testing, health care, and cell analysis. This is driven in part by the desire to decrease the cost of health care and to obtain precise information more quickly about the health status of patient by the development of various biosensors, which has become increasingly prevalent in clinical testing and point of care testing for a wide range of biological elements. Uric acid is an important byproduct in human body and a number of pathological disorders are related to its high concentration in human body. In past few years, rapid growth in the development of new materials and improvements in sensing techniques have led to the evolution of advanced biosensors. In this context, metal oxide thin film based matrices due to their bio compatible nature, strong adsorption ability, high isoelectric point (IEP) and abundance in nature have become the materials of choice for recent technological advances in biotechnology. In the past few years, wide band-gap metal oxide semiconductors including ZnO, SnO₂ and CeO₂ have gained much attention as a matrix for immobilization of various biomolecules. Tin oxide (SnO₂), wide band gap semiconductor (Eg =3.87 eV), despite having multifunctional properties for broad range of applications including transparent electronics, gas sensors, acoustic devices, UV photodetectors, etc., it has not been explored much for biosensing purpose. To realize a high performance miniaturized biomolecular electronic device, rf sputtering technique is considered to be the most promising for the reproducible growth of good quality thin films, controlled surface morphology and desired film crystallization with improved electron transfer property. Recently, iron oxide and its composites have been widely used as matrix for biosensing application which exploits the electron communication feature of Fe, for the detection of various analytes using urea, hemoglobin, glucose, phenol, L-lactate, H₂O₂, etc. However, to the authors’ knowledge, no work is being reported on modifying the electronic properties of SnO₂ by implanting with suitable metal (Fe) to induce the redox couple in it and utilizing it for reagentless detection of uric acid. In present study, Fe implanted SnO₂ based matrix has been utilized for reagentless uric acid biosensor. Implantation of Fe into SnO₂ matrix is confirmed by energy-dispersive X-Ray spectroscopy (EDX) analysis. Electrochemical techniques have been used to study the response characteristics of Fe modified SnO₂ matrix before and after uricase immobilization. The developed uric acid biosensor exhibits a high sensitivity to about 0.21 mA/mM and a linear variation in current response over concentration range from 0.05 to 1.0 mM of uric acid besides high shelf life (~20 weeks). The Michaelis-Menten kinetic parameter (Km) is found to be relatively very low (0.23 mM), which indicates high affinity of the fabricated bioelectrode towards uric acid (analyte). Also, the presence of other interferents present in human serum has negligible effect on the performance of biosensor. Hence, obtained results highlight the importance of implanted Fe:SnO₂ thin film as an attractive matrix for realization of reagentless biosensors towards uric acid.
Computational Study of Blood Flow Analysis for Coronary Artery Disease
Coronary artery disease happens due to the buildup of cholesterol, fibrous and fatty material inside the arteries called plaque, on their inner walls. This buildup is called atherosclerosis. As it grows, less blood can flow through the arteries. As a result, the heart muscle cannot get the blood or oxygen it needs. This leads to chest pain (angina) or a heart attack. The purpose of this research work is to noninvasively assess hemodynamic parameters such as blood velocity, wall shear stress (WSS), wall shear stress gradient (WSSG), time-average wall shear stress (TAWSS) and wall pressure with computational fluid dynamics (CFD) to identify the mechanical factors responsible for the plaque progression and/or rupture in left coronary arteries (LCA) in coronary arteries using patient-specific data from computed tomography (CT) angiography. For CFD simulations the initial step is the construction of geometrical model of the left coronary artery (LCA). 3D realistic and idealized models of artery were constructed using MIMICS Research 19.0 Establishing the geometrical compatibility and grid generation is done by using ANSYS FLUENT-14.5. Hemodynamic parameters were quantified, and flow patterns were visualized both in the absence and presence of coronary plaques. The wall pressure continuously decreased towards distal segments and showed pressure drops in stenotic segments. Areas of high WSS and high flow velocities were found adjacent to plaques deposition.
A Self-Adaptive Stimulus Artifacts Removal Approach for Electrical Stimulation Based Muscle Rehabilitation
This paper reports an efficient and rigorous self-adaptive stimulus artifacts removal approach for a mixed surface EMG (Electromyography) and stimulus signal during muscle stimulation. The recording of EMG and the stimulation of muscles were performing simultaneously. It is difficult to generate muscle fatigue feature from the mixed signal, which can be further used in closed loop system. A self-adaptive method is proposed in this paper, the stimulation frequency was calculated and verified firstly. Then, a mask was created based on this stimulation frequency to remove the undesired stimulus. 20 EMG signal recordings were analyzed, and the ANOVA (analysis of variance) approach illustrated that the decreasing trend of median power frequencies was successfully generated from the 'cleaned' EMG signal.
Detection of Important Biological Elements in Drug-Drug Interaction Occurrence
Drug-drug interactions (DDIs) are main cause of the adverse drug reactions and nature of the functional and molecular complexity of drugs behavior in human body make them hard to prevent and treat. With the aid of new technologies derived from mathematical and computational science the DDIs problems can be addressed with minimum cost and efforts. Market basket analysis is known as powerful method to identify co-occurrence of thing to discover patterns and frequency of the elements. In this research, we used market basket analysis to identify important bio-elements in DDIs occurrence. For this, we collected all known DDIs from DrugBank. The obtained data were analyzed by market basket analysis method. We investigated all drug-enzyme, drug-carrier, drug-transporter and drug-target associations. To determine the importance of the extracted bio-elements, extracted rules were evaluated in terms of confidence and support. Market basket analysis of the over 45,000 known DDIs reveals more than 300 important rules that can be used to identify DDIs, CYP 450 family were the most frequent shared bio-elements. We applied extracted rules over 2,000,000 unknown drug pairs that lead to discovery of more than 200,000 potential DDIs. Analysis of the underlying reason behind the DDI phenomena can help to predict and prevent DDI occurrence. Ranking of the extracted rules based on strangeness of them can be a supportive tool to predict the outcome of an unknown DDI.
Despeckling of Ultrasound Images Using Trilateral Filter
Speckle phenomenon in ultrasound images occurs as a granular appearance due to the constructive and destructive interference of the echo signals from the target. This decreases the ultrasound image quality and contrast, thereby making the diagnosis and automatic detection of objects in ultrasound images difficult. There are various techniques available for removal of speckle noise and they can be classified as adaptive filter based or wavelet based or anisotropic diffusion based or hybrid of the above methods. This paper investigates the effectiveness of trilateral filter for optimal de-noising of speckle from ultrasound images. Those pixels which are similar in location, intensity and structure are only considered for filtering process so that over smoothing the edges are prevented even with wide window size. Hence, minute structures are preserved for diagnosis of ultrasound images. The performance of the tri-lateral filter is compared with the existing ultrasound de-speckling filters using various metrics such as PSNR, Contrast to Speckle Ratio, Speckle Index. A wavelet based quality metric has been proposed to evaluate the quality of the de-noised ultrasound images. Various simulated and clinical ultrasound images are used for evaluating the performance the trilateral filter for despeckling.
Multi-Layer Perceptron and Radial Basis Function Neural Network Models for Classification of Diabetic Retinopathy Disease Using Video-Oculography Signals
Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings find out that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases based on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. 25 features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models based on multi-layer perceptron and radial basis function are recommended to diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparatively classification performance of proposed models. Our results show that proposed RBF model (100%) results in better classification performance than MLP model (94%).
Optimal Electrocardiogram Sampling Frequency for Multiscale Entropy Based Heart Rate Variability
Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification. High ECG sampling rate increase memory requirements and processing time whereas low sampling rate degrade signal quality and result in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale.
Secured Embedding of Patient’s Confidential Data in Electrocardiogram Using Chaotic Maps
This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.
Anonymous Gel-Fluid Transition of Solid Supported Lipids
Solid-supported lipid bilayers are often used as a simple model for studies of biological membranes. The presence of a solid substrate that interacts attractively with lipid head-groups is expected to affect the phase behavior of the supported bilayer. Molecular dynamics simulations of a coarse-grained model are thus performed to investigate the phase behavior of supported one-component lipid bilayer membranes. Our results show that the attraction of the lipid head groups to the substrate leads to a phase behavior that is different from that of a free standing lipid bilayer. In particular, we found that the phase behaviors of the two leaflets are decoupled in the presence of a substrate. The proximal leaflet undergoes a clear gel-to-fluid phase transition at a temperature lower than that of a free standing
bilayer, and that decreases with increasing strength of the substrate-lipid attraction. The distal leaflet, however, undergoes a change from a homogeneous liquid phase at high temperatures to a heterogeneous state consisting of small liquid and gel domains, with the average size of the gel domains that increases with decreasing temperature. While the chain order parameter of the proximal leaflet clearly shows a gel-fluid phase transition, the chain order parameter of the distal leaflet does not exhibit a clear phase transition. The decoupling in the phase behavior of the two leaflets is due to a non-symmteric lipid distribution in the two leaflets resulting from the presence of the substrate.
Hybrid Manufacturing System to Produce 3D Structures for Osteochondral Tissue Regeneration
One utmost challenge in Tissue Engineering is the production of 3D constructs capable of mimicking the functional hierarchy of native tissues. This is well stated for osteochondral tissue due to the complex mechanical functional unit based on the junction of articular cartilage and bone. Thus, the aim of the present study was to develop a new additive manufacturing system coupling micro-extrusion with hydrogels printing. An integrated system was developed with 2 main features: (i) the printing of up to three distinct hydrogels; (ii) in coordination with the printing of a thermoplastic structural support. The hydrogel printing module was projected with a ‘revolver-like’ system, where the hydrogel selection was made by a rotating mechanism. The hydrogel deposition was then controlled by pressured air input. The use of specific components approved for medical use was incorporated in the material dispensing system (Nordson EDF Optimum® fluid dispensing system). The thermoplastic extrusion modulus enabled the control of required extrusion temperature through electric resistances in the polymer reservoir and the extrusion system. After testing and upgrades, a hydrogel modulus with 3 syringes (3cm3 capacity each), with a pressure range of 0-2.5bar, a rotational speed of 0-5rpm, and working with needles from 200-800µm was obtained. This modulus was successfully coupled to the extrusion system that presented a temperature up to 300˚C, a pressure range of 0-12bar, and working with nozzles from 200-500µm. The applied motor could provide a velocity range 0-2000mm/min. Although, there are distinct printing requirements for hydrogels and polymers, the novel system could develop hybrid scaffolds, combining the 2 moduli. The morphological analysis showed high reliability (n=5) between the theoretical and obtained filament and pore size (350µm and 300µm vs. 342±4µm and 302±3µm, p>0.05, respectively) of the polymer; and multi-material 3D constructs were successfully obtained. Human tissues present very distinct and complex structures regarding their mechanical properties, organization, composition and dimensions. For osteochondral regenerative medicine, a multiphasic scaffold is required as subchondral bone and overlying cartilage must regenerate at the same time. Thus, a scaffold with 3 layers (bone, intermediate and cartilage parts) can be a promising approach. The developed system may give a suitable solution to construct those hybrid scaffolds with enhanced properties. The present novel system is a step-forward regarding osteochondral tissue engineering due to its ability to generate layered mechanically stable implants through the double-printing of hydrogels with thermoplastics.
Site-Specific Delivery of Hybrid Upconversion Nanoparticles for Photo-Activated Multimodal Therapies of Glioblastoma
In order to enhance the photodynamic/photothermal therapeutic efficacy on glioblastoma, the functionized upconversion nanoparticles with the capability of converting the deep tissue penetrating near-infrared light into visible wavelength for activating photochemical reaction were developed. The drug-loaded nanoparticles (NPs) were obtained from the self-assembly of oleic acid-coated upconversion nanoparticles along with maleimide-conjugated poly(ethylene glycol)-cholesterol (Mal-PEG-Chol), as the NP stabilizer, and hydrophobic photosensitizers, IR-780 (for photothermal therapy, PTT) and mTHPC (for photodynamic therapy, PDT), in aqueous phase. Both the IR-780 and mTHPC were loaded into the hydrophobic domains within NPs via hydrophobic association. The peptide targeting ligand, angiopep-2, was further conjugated with the maleimide groups at the end of PEG adducts on the NP surfaces, enabling the affinity coupling with the low-density lipoprotein receptor-related protein-1 of tumor endothelial cells and malignant astrocytes. The drug-loaded NPs with the size of ca 80 nm in diameter exhibit a good colloidal stability in physiological conditions. The in vitro data demonstrate the successful targeting delivery of drug-loaded NPs toward the ALTS1C1 cells (murine astrocytoma cells) and the pronounced cytotoxicity elicited by combinational effect of PDT and PTT. The in vivo results show the promising brain orthotopic tumor targeting of drug-loaded NPs and sound efficacy for brain tumor dual-modality treatment. This work shows great potential for improving photodynamic/photothermal therapeutic efficacy of brain cancer.
The Validation of RadCalc for Clinical Use: An Independent Monitor Unit Verification Software
In the matter of patient treatment planning quality assurance in 3D conformational therapy (3D-CRT) and volumetric arc therapy (VMAT or RapidArc), the independent monitor unit verification calculation (MUVC) is an indispensable part of the process. Concerning 3D-CRT treatment planning, the MUVC can be performed manually applying the standard ESTRO formalism. However, due to the complex shape and the amount of beams in advanced treatment planning technic such as RapidArc, the manual independent MUVC is inadequate. Therefore, commercially available software such as RadCalc can be used to perform the MUVC in complex treatment planning been. Indeed, RadCalc (version 6.3 LifeLine Inc.) uses a simplified Clarkson algorithm to compute the dose contribution for individual RapidArc fields to the isocenter. The purpose of this project is the validation of RadCalc in 3D-CRT and RapidArc for treatment planning dosimetry quality assurance at Antoine Lacassagne center (Nice, France). Firstly, the interfaces between RadCalc and our treatment planning systems (TPS) Isogray (version 4.2) and Eclipse (version13.6) were checked for data transfer accuracy. Secondly, we created test plans in both Isogray and Eclipse featuring open fields, wedges fields, and irregular MLC fields. These test plans were transferred from TPSs according to the radiotherapy protocol of DICOM RT to RadCalc and the linac via Mosaiq (version 2.5). Measurements were performed in water phantom using a PTW cylindrical semiflex ionisation chamber (0.3 cm³, 31010) and compared with the TPSs and RadCalc calculation. Finally, 30 3D-CRT plans and 40 RapidArc plans created with patients CT scan were recalculated using the CT scan of a solid PMMA water equivalent phantom for 3D-CRT and the Octavius II phantom (PTW) CT scan for RapidArc. Next, we measure the doses delivered into these phantoms for each plan with a 0.3 cm³ PTW 31010 cylindrical semiflex ionisation chamber (3D-CRT) and 0.015 cm³ PTW PinPoint ionisation chamber (Rapidarc). For our test plans, good agreements were found between calculation (RadCalc and TPSs) and measurement (mean: 1.3%; standard deviation: ± 0.8%). Regarding the patient plans, the measured doses were compared to the calculation in RadCalc and in our TPSs. Moreover, RadCalc calculations were compared to Isogray and Eclispse ones. Agreements better than (2.8%; ± 1.2%) were found between RadCalc and TPSs. As for the comparison between calculation and measurement the agreement for all of our plans was better than (2.3%; ± 1.1%). The independent MU verification calculation software RadCal has been validated for clinical use and for both 3D-CRT and RapidArc techniques. The perspective of this project includes the validation of RadCal for the Tomotherapy machine installed at centre Antoine Lacassagne.
Liver Regeneration of Small in situ Injury
Liver is the center of detoxification and exposed to toxic metabolites all the time. It is highly regenerative after injury, with the ability to restore even after 70% partial hepatectomy. Most of the previous studies were using hepatectomy as injury models for liver regeneration study. There is limited understanding of small-scale liver injury, which can be caused by either low dose drug consumption or hepatocyte routine metabolism. Although these small in situ injuries do not cause immediate symptoms, repeated injuries will lead to aberrant wound healing in liver. Therefore, the cellular dynamics during liver regeneration is critical for our understanding of liver regeneration mechanism. We aim to study the liver regeneration of small-scale in situ liver injury in transgenic mice labeling actin (Lifeact-GFP). Previous studies have been using sample sections and biopsies of liver, which lack real-time information. In order to trace every individual hepatocyte during the regeneration process, we have developed and optimized an intravital imaging system that allows in vivo imaging of mouse liver for consecutive 5 days, allowing real-time cellular tracking and quantification of hepatocytes. We used femtosecond-laser ablation to make controlled and repeatable liver injury model, which mimics the real-life small in situ liver injury. This injury model is the first case of its kind for in vivo study on liver. We found that small-scale in situ liver injury is repaired by the coordination of hypertrophy and migration of hepatocytes. Hypertrophy is only transient at initial phase, while migration is the main driving force to complete the regeneration process. From cellular aspect, Akt/mTOR pathway is activated immediately after injury, which leads to transient hepatocyte hypertrophy. From mechano-sensing aspect, the actin cable, formed at apical surface of wound proximal hepatocytes, provides mechanical tension for hepatocyte migration. This study provides important information on both chemical and mechanical signals that promote liver regeneration of small in situ injury. We conclude that hypertrophy and migration play a dominant role at different stages of liver regeneration.
Development of a Few-View Computed Tomographic Reconstruction Algorithm Using Multi-Directional Total Variation
Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better.
Assessment of Arterial Stiffness through Measurement of Magnetic Flux Disturbance and Electrocardiogram Signal
Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment.
Implementation of Feeding Task via Learning from Demonstration
In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method, we show that obstacles can be avoided by robots end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.
activities of daily living tasks (ADLs)
, feeding task
, Gaussian mixture model (GMM)
, human-robot interaction (HRI)
, learning and adaptive systems
, learning by demonstration
, learning by imitation
, learning from demonstration (LFD)
, meal assistant
, probabilistic motion encoding
, probability and statistical methods
, programming by demonstration (PBD)
Best Practices for Presentation Attack Detection Evaluation in Voice Biometrics
With the growing number of voice biometrics applications (e.g. banking), it has become necessary to be able to assess and compare different systems’ detection of Presentation Attacks, as attackers could have access to sensitive data. To this end, a common ground is needed to perform comparable security evaluations. There are methodologies to perform generic security evaluations such as the standards ISO/IEC JTC1/SC3 30107 or Common Criteria’s CEM. This contribution has presented the joint of Common Criteria and the standard. With the use of this methodology, future contributions should have a common ground. This paper clarifies the importance of assessing the Presentation Attack Detection capabilities of a voice biometrics system and give tools or recommendations to perform it.
A Sui Generis Technique to Detect Pathogens in Post-Partum Breast Milk Using Image Processing Techniques
Mother’s milk provides the most superior source of nutrition to a child. There is no other substitute to the mother’s milk. Postpartum secretions like breast milk can be analyzed on the go for testing the presence of any harmful pathogen before a mother can feed the child or donate the milk for the milk bank. Since breast feeding is one of the main causes for transmission of diseases to the newborn, it is mandatory to test the secretions. In this paper, we describe the detection of pathogens like E-coli, Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), Hepatitis C (HCV), Cytomegalovirus (CMV), Zika and Ebola virus through an innovative method, in which we are developing a unique chip for testing the mother’s milk sample. The chip will contain an antibody specific to the target pathogen that will show a color change if there are enough pathogens present in the fluid that will be considered dangerous. A smart-phone camera will then be acquiring the image of the strip and using various image processing techniques we will detect the color development due to antigen antibody interaction within 5 minutes, thereby not adding to any delay, before the newborn is fed or prior to the collection of the milk for the milk bank. If the target pathogen comes positive through this method, then the health care provider can provide adequate treatment to bring down the number of pathogens. This will reduce the postpartum related mortality and morbidity which arises due to feeding infectious breast milk to own child.
, breast milk
, image processing techniques
Effectiveness of Dry Needling on Pain and Pressure Point Threshold in Cervicogenic Headache
Headache disorders are one of the 10 most disabling conditions for men and women. Headache that originated from upper cervical spine and refereed to the one side of the head and/or face is known as cervicogenic headache (CH) which constitute15% to 20% among all the headaches. In our best knowledge manual therapy is often advocated for managing CH, but very little focus given on muscle system although it is a musculoskeletal disorder. In this study, 75 patients with CH were selected and divided into two groups Group A: Manual therapy and Group B: dry needling along with manual therapy group. Assessment was done using NPRS (0-10) for pain, wide spread pressure pain threshold using an algometer at the beginning and end of the study. There is a consistent reduction in pain and tenderness in both the group but significant improvement was shown in combined group. Outcome of the study has explored that the effectiveness of dry needling along with Mulligan is more beneficial in patients with cervicogenic headaches.
An Interpretable Data-Driven Approach for the Stratification of the Cardiorespiratory Fitness
, J. Henriques
, P. Carvalho
, T. Rocha
, S. Paredes
, R. Cabiddu
, R. Trimer
, R. Mendes
, A. Borghi-Silva
, L. Kaminsky
, E. Ashley
, R. Arena
, J. Myers
The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).