Applications In Biomedical Domains Research Articles

Advances in Gold Nanoparticles: Synthesis, Functionalization Strategies, and Theranostic Applications in Cancer.

Cancer is among the leading causes of mortality and morbidity in the world. Metallic nanoparticles, especially gold nanoparticles (AuNPs) have emerged to be attractive systems to circumvent the associated adverse effects. By the virtue of their unique properties of tunable size, shape, composition, optical properties, biocompatibility, minimal toxicity, multivalency, fluorescence-luminescence property and surface plasmon resonance; AuNPs have the potential to be used as drug delivery systems. It is vital to ensure that the drug reaches the target site of action for selective kill of cancer cells without harm to healthy cells. These AuNPs can be easily functionalized with a wide array of ligands like peptides, oligonucleotides, polymers, carbohydrates for active targeting to ensure site specific delivery and reduced systemic effects. AuNPs have been in-vestigated as carriers for gene delivery, drug delivery with or without photothermal therapy, in diagnosis based on radiation or spectroscopy. They have emerged as attractive theranostic approach in the overall management of cancer with superior benefit to risk features. In this review, we have discussed synthesis of different AuNPs (nanorods, spherical nanoparticles, and hollow AuNPs), their functionalization strategies and their applications in biomedical domain. Various research studies and clinical trials on application of AuNPs in diagnosis and therapeutics are highlighted.

Fiber grating sensors and their recent applications in biomedical domain

Sensors play an important role in measuring various physio-chemical and biological parameters. Biomedical applications of sensors include early level diagnosis of diseases, treatment technique enhancement and also monitoring the effect of medical techniques using suitable biomedical devices. The sensors which are deemed suitable for biomedical applications need to be fast, accurate, flexible, small, and most importantly, biocompatible. The inherent advantages of fibre optic sensors, such as small foot print, electrical passiveness, multiplexing capability and fast response make them the most preferred for biomedical sensing applications. Fibre optics sensors are generally used in the measurement of parameters such as temperature, strain, pressure, displacement, angle, and force. And find applications in diverse regimes like structural health monitoring, shape sensing, seismic sensing; in the bio medical domain, they have been deployed in gait analysis, pulse rate monitoring, body joint angle measurements, acquisition of respiratory parameters, cardiovascular parameters and many more. Despite being a topic of significant interest in biomedical applications, the adoptablility of optical sensors in clinical practice is not very encouraging. Hence, there is a need to address the reasons for the same, and the present review aims to highlight some critical areas. The present review paper primarily discusses the optical fibre grating techniques: their fabrication methods, the applications of these sensors in biophysical and biomechanical measurements, bio proteins and biomarker detection in body fluids. The review also discusses the bottlenecks in the clinical application of these sensors. The comparison of the performance of the optical fibre sensors with other sensing techniques is also discussed in this review paper.

Investigation of Biofabricated Iron Oxide Nanoparticles for Antimicrobial and Anticancer Efficiencies

Capparis zeylanica leaf extract was employed in this work to create iron oxide nanoparticles (α-Fe2O3) using anhydrous ferric chloride. The UV spectrum, XRD, FT-IR, and FE-SEM with EDX methods were used to characterize the fabricated nanoparticles. The iron oxide nanoparticles obtained were spherical in form, with an average crystallite size of 28.17 nm determined by XRD. The agar well diffusion method was used to assess the antimicrobial activity of the α-Fe2O3 nanoparticles created in this study against pathogenic organisms, Gram-negative bacteria (Escherichia coli and Pseudomonas aeroginosa), Gram-positive bacteria (Staphylococcus aureus and Streptococcus pyogenes), and fungi (Candida albicans and Aspergillus niger). Among the pathogens tested, S. pyogenes had the highest zones of inhibition (25 ± 1.26 mm), followed by S. aureus (23 ± 0.8 mm), E. coli (23 ± 2.46 mm), P. aeroginosa (22 ± 1.86 mm), C. albicans (19 ± 2.34 mm) and A. niger (17 ± 3.2 mm). The substance was further tested for anticancer activity against A549 (lung cancer) cells using the MTT assay. The cytotoxic reaction was found to be concentration-dependent. The present study, therefore, came to the conclusion that the bio-effectiveness of the manufactured α-Fe2O3 nanoparticles may result in applications in biomedical domains.

Bioinspired quantum dots: Promising nanosystems for biomedical application

Quantum dots (QDs) are semiconducting nanocrystals exhibiting a unique morphology and morphology-dependent optical, electrical and biological properties. The synthesis, characterization and applications of QDs are highly active fields of investigation. However, recently biological routes to synthesize quantum dots have gained a lot of attention as they produce highly biocompatible and non-toxic QDs that possess excellent morphologies and tunable properties. Moreover, the green synthesis of QDs eliminates the use of hazardous chemicals and solvents, and therefore, becomes a cost-effective and environment-friendly approach. Owing to these properties, the bioinspired QDs show broad applications in biomedical domains like diagnosis and treating different diseases. Further, this review aims to impart a detailed discussion on the biomedical utilities of bioinspired quantum dots by highlighting surface modification techniques and biosafety aspects of quantum dots, making this review one of its kind.

Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

Abstract With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can handle complex & non-linear relations in the data and also predict and classify outcomes with higher accuracy. Deep learning models are one of such models which can handle complex and nonlinear data and are being increasingly used in the biomedical filed in the recent years. Deep learning methodology evolved from artificial neural networks which process the input data through multiple hidden layers with higher level of abstraction. Deep Learning networks are used in various fields such as image processing, speech recognition, fraud deduction, classification and prediction. Objectives of this paper is to provide an overview of Deep Learning Models and its application in the biomedical domain using R Statistical software Deep Learning concepts are illustrated by using the R statistical software package. X-ray Images from NIH datasets used to explain the prediction accuracy of the deep learning models. Deep Learning models helped to classify the outcomes under study with 91% accuracy. The paper provided an overview of Deep Learning Models, its types, its application in biomedical domain. - is paper has shown the effect of deep learning network in classifying images into normal and disease with 91% accuracy with help of the R statistical package.

Comparison of Nano-second and Millisecond Pulse Generators for Biological applications of Electroporation

A phenomenon that transiently increases the permeability of the cells is known as electroporation. It is the basis for number of the applications in biomedical domains. It is essential to consider requirement of high precision and the overall size of electroporator. The recent decades have seen the development of solid-state power electronic modules. The modules enable generation of high voltage millisecond and nano-second pulses with options to reduce the overall size of the equipment. The selective modules are verified with experimental models and available for commercial usage. While the other modules are still undergoing optimization processes. The generator generates pulses for varying performances. Hence, this paper presents knowledge for different nanosecond and millisecond pulse generating circuits for electroporation purposes. The performance parameters like the width of the pulse, its amplitude are compared for different circuit topologies. The performance analysis of different topologies and their impact on the performance of the electroporation at the cell biology level are considered in this paper.

Responsive mechanism of 2-fluoro-5-nitrobenzoate based two-photon fluorescent probes for H2Sn detection: A theoretical perspective

Two-photon fluorescent probes with large two-photon absorption (TPA) cross sections have shown wide applications in biomedical domain. However, both the species and amounts of high efficient probes are far from meeting the requirements, one main reason is that the relationship between the molecular structures and the responsive mechanisms are not clear and theoretical framework in this field is not perfect. In this work, the photophysical properties including one- and two-photon absorption and emission of three newly synthesized fluorescent probes for hydrogen polysulfide (H2Sn) detection are investigated by density functional theory and time-dependent density functional theory with the polarizable continuum model in different solvents. Results indicate that the enhanced fluorescent intensity and enlarged TPA cross section can be found when the probes reacted with H2Sn. Moreover, the OPA intensity is largest and its fluorescent intensity is largely enhanced when detecting H2Sn for Pro2, this verifies its superior performance in the detection of H2Sn than Pro1 and Pro 3. Furthermore, the inner mechanism for the increase of TPA cross section is revealed, the responsive mechanisms for photo induced electron transfer (PET) and fluorescence resonance energy transfer (FRET) processes are revealed through analyzing the energies and distributions of frontier orbitals. Our calculations provide theoretical perspectives for experimental measurements and could sever as a useful reference for developing advanced probes in biomedical fields.

Analysis the Photo-Electric Characteristics of Quantum Corrected Horizontally Doped Si/4H-SiC Multi-Layers Avalanche Photo Sensor for Application in Bio-Medical Region

In this paper, Quantum Corrected (QMC) horizontally doped Si/4H-SiC multi layers avalanche photo sensor (APS) has been developed for application in bio-medical domain. The photo electric characteristics of the developed photo sensor has been analysed in terms of photo current, photo responsivity and quantum efficiency in 300nm – 900nm wavelength region. These all parameters have been improved considerably by incorporating Si/4H-SiC multi layers structure into the active region of the devices. Recently demand of such bio-medical photo-sensors is increasing significantly for identification of anti-bodies and virus in human blood. The validity of newly developed model has been established by comparing the simulated result with the reported experimental data. The photo electrical characteristics analysis of the designed QMC model-based Si/4H-SiC multi layers avalanche photo sensor depict that the photo current is ~20μA whereas the photo responsivity and quantum efficiency are ~0.65A/Wand ~0.67 respectively. The results clearly establish the superiority of proposed photo-sensor for developing medical instruments in visible wavelength region (300nm – 900nm).

Simulation of spectral response of SPR multilayers nanostructures detector intended for biomedical application

The photo electronics devices based on SPR (surface plasmon resonance) phenomena have extensive applications in biomedical domain. However, the biological environments to detect influence on SPR effect maybe on wavelength or the incidence angle of resonance. So, the spectral response of SPR effect in multi layers nanostructures is simulated by the method of transfer matrices (T-matrix). As a result, these nanostructures can be used for detection in biomedical domain.

Network embedding in biomedical data science.

Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.

Cepstral Features Extraction for Heart Sounds Classification

A novel method for separation between normal and abnormal heart sounds based on Phonocardiogram (PCG) is presented in this article. For features extraction phase, Mel-Frequency Cepstral Coefficients (MFCC) algorithm is used to extract information from heart sound signals. In this step, changing the frames size, during framing process, shows it influence on the obtained result. In classification step, Support Vector Machines (SVM) is used with different kernels. Simulation results obtained from different databases are compared and discussed. The developed system gave good results when applied to different datasets with an accuracy of 96% and 98% for the first and the second dataset respectively. The developed system can be used for other applications in biomedical domains

Six Sigma and its application in Biomedical Domain revisited

Six Sigma and its application in Biomedical Domain revisited

Design and characterization of asymetrical super-lattice Si/4H-SiC pin photo diode array: a potential opto-sensor for future applications in bio-medical domain

Photo-sensors are integral part of different bio-medical diagnostic equipment. Each type of bio-molecules possess unique spectral fingerprint in visible wavelength region of electro-magnetic spectrum. Now-a-days, the enhancement of quantum-efficiency and photo-responsivity of such bio-medical opto-sensors, for accurate identification of virus/anti-bodies in blood by optical means, is a big challenge to medical-device Engineers. The authors have addressed this issue in this research paper by proposing a novel structure of asymmetrical Si/4H-SiC super-lattice pin diode array for the development of high-sensitive visible light (300–800 nm) sensor on native substrate. The simulation experiment is carried out by developing a generalized large-signal quantum modified drift–diffusion simulator incorporating three different modes of carrier generation-recombination under light and dark conditions: avalanching, tunneling and photo-irradiation. The validity of the model has been established by comparing the simulation results with those of experimental observations. A good agreement between theory and experiment, under similar biasing conditions, establishes the validity of the developed model. The characteristics analysis depicts that the quantum efficiency of the designed single photo-sensor is ~ 65% within 400–700 nm wavelength region, whereas, the same enhances to nearly ~ 90% with a 3 × 3 photo-sensor array based on asymmetrical super-lattice single pin devices. In visible wavelength region, the simulated photo-sensors (both single and array) have demonstrated significant photo responsivity. The photo-responsivity values, at 500 nm wavelength of incident radiation, are observed to be 0.65 A/W for a single photo-diode and 0.85 A/W for a 3 × 3 combination of photo-diode array. This clearly establishes the potentiality of the asymmetrical super-lattice pin-array as a visible photo-sensor for future application in developing medical instruments. A comparative analysis of Si and Si/4H-SiC asymmetrical super-lattice photo-sensors establishes the superiority of the later as a high-sensitive visible light-sensor in terms of better photo-responsivity and quantum efficiency. To the best of authors’ knowledge, this is the first report on Si/4H-SiC super-lattice pin photo-sensor array in the visible range of optical irradiation. The experimental feasibility of the device and a proposed circuit for future bio-medical implementation are also incorporated in the present research-paper for further development.

Ionic polymers based on quaternized polysulfones: hydrodynamic properties of polymer mixtures in solution

Abstract Hydrodynamic properties developed in a series of mixtures, obtained from quaternized polysulfone and cellulose acetate phthalate or polyvinyl alcohol in N-methyl-2-pyrrolidone, were evaluated by viscometric investigations. Theoretical and experimental aspects concerning the viscometric data for binary polymer/solvent and ternary polymer/polymer/solvent mixtures have been discussed by the new Wolf model, as a function of the charge density of polyion, structural peculiarity of polymers, and polymer mixture composition. Intrinsic viscosity and also the hydrodynamic parameters obtained by the Wolf method offer new information on the competition between different types of interactions manifested in ternary polymer/polymer/solvent systems. The complex dependence of viscosity on polymer composition is influenced by conformational changes of constituent polymers from the mixture, as well as by cumulative effects of electrostatic interactions, hydrogen bonding or association phenomena. Additionally, the above-mentioned interactions indicate the compatibility of these polymers over a large composition domain. This study investigates the hydrodynamic functions from the perspective of some newly-issued theories and analyzes the choice of optimal polymer mixtures compositions for specific applications in biomedical domains.

Poly(3,4-ethylenedioxythiophene)/graphene oxide composite coating for electrode-tissue interface.

Owing to interacting with the living tissue directly, the electrode-tissue interface largely determines the performance of the whole bioelectronics devices. The miniaturization of biomedical electronic components requires interface materials to possess properties including excellent electrical performance, good biocompatibility and compatibility with microelectronic fabrication process. Considering the unique characteristics and wide applications in biomedical domain of conducting polymer and graphene, composite film consists of poly(3,4-ethylenedioxythiophene) (PEDOT) and graphene oxide (GO) is proposed as electrode-tissue interface in this work. The facilely electrochemically synthesized PEDOT/GO coating on microelectrodes shows low impedance, high charge storage capacity and good biocompatibility to act as electrode-tissue interface. As a result, the composite film is a potential biomaterial as electrode-tissue interface for tissue engineering and further implantable electrophysiological devices.