A numerical model of an amperometric-enzymatic uric acid biosensor for a non-relentless condition has been developed. This model depends on the arrangement of nonlinear reaction diffusion equations for Michaelis-Menten formalism that depicts the concentrations of substrate and product. The new rough scientific articulations for the concentration of substrate (uricase enzyme) and product and the corresponding current response have been derived for all estimations of parameters utilizing the new perturbation technique. The non-dimensional numerical model of the amperometric biosensor can be effectively used to examine the responses. Moreover, the relative impact of these parameters is chosen by the Damkohler number and the impact of current density on this number likewise contemplated. All the analytical results are compared with simulation results using MATLAB program and the numerical outcomes concur with fitting hypotheses. Notwithstanding, strikingly the model likewise proposed that the choice of substrate and product for uric acid biosensor for the application of kidney disease and GOUT arthritis diseases. Keywords: GOUT arthritis, Amperometric, Uric acid, Damkohler number, Diffusion equations

Vivekanandan S

Department of Instrumentation

School of Electrical Engineering

Vellore Campus

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Informatics in Medicine Unlocked

Journal of Medical Systems

An Enhanced Symptom Clustering with Profile Based Prescription Suggestion in Biomedical application

Co-immobilization of Urease (Urs) plus glutamate dehydrogenase (GLDH) over titania-zirconia (TiO2- ZrO2) nanocomposite and its chemical reactions was examined. TiO2-ZrO2 nanocomposite was incorporated utilizing sol-gel spin covering method with blend of Zirconium (IV) n-propoxide, titanium (IV) butoxide in 1:1 molar proportion. The electrochemical investigations used for urea discovery containing reference (Ag/AgCl), counter (ITO) as well as operational (Urs-GLDH/TiO2-ZrO2/ITO) sensing matrix and the modification of bio electrode is finished by physical adsorption procedure. This enzymatic urea sensor indicates linearity as 5- 100 mg/dL with enhanced affectability as 2.64 μA [Log mM]-1 cm-2 and lower discovery boundary as 0.08 mg/dl (0.42 mM). The XRD of TiO2- ZrO2 nanocomposite demonstrates crests at 2θ:30.7° and showing the nearness of prevalent orthorhombic period of ZrTiO4. The band found at 3520 cm-1 and 1585 cm-1 relate to extending tremor as well as twisting of O-H bond because of assimilating water atoms and cyclic voltammetric (CV) also made foe the electrochemical analysis of uric acid in the analyte. The sensitivity is based on the immobilization of enzymes over the matrix surface. This is credited to the great electrocatalytic conduct of the nanocomposite based uric acid biosensor. © 2019 IEEE.

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Structural, optical and electrochemical response studies of TiO2–ZrO2 nanocomposite for uric acid detection

Breast cancer is one of the significant health problems in the world. If these abnormalities in breast cancer are detected early there is a maximum chance for recovery. For this early prediction we can go for mammography. It is one of the most effective and commonly used method for detecting and screening breast cancer. This paper presents classification of mammograms using feature extracted using Hough transform. Hough transform is a two dimensional transform. It is used to isolate feature of particular shape in an image. Miniaturized scale characterization and masses are the two most vital markers of threat, and their mechanized identification is exceptionally important for early breast cancer diagnosis. Since masses are regularly undefined from the encompassing parenchymal, computerized mass location and arrangement is significantly additionally difficult. This paper talks about the strategies for classification and feature extraction. Here, Hough transform is used to detect features of mammograms image and it is classified using SVM. The classification accuracy is more by the use of SVM classifier. This method is tested on 95 mammograms images collected and classified using SVM. From the result it shows that the proposed method is effectively classify the abnormal classes of mammograms. © 2019 Elsevier Ltd

Measurement

Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform

This study reports the development of a fast and facile route for the synthesis of novel TiO2-CeO2 nanocomposite film using sol-gel spin coating method with the mixture of commercial CeO2, titanium-iso-propoxide (TTIP) and aqueous ammonia. Solutions of TTIP in ethanol were mixed with CeO2 nanoparticles to form a mixed sol-gel. The sol containing TiO2-CeO2 was coated over the glass substrate and the resulting nanocomposite thin film were characterized in terms of phase and surface morphology by XRD, TEM, SEM and FT-IR. Conventional methods of the synthesis of CeO2–TiO2 nanocomposite require a long time, and TiO2 is rarely found as a coated material. In contrast, the sol-gel spin coating method was able to synthesize CeO2–TiO2 sol within a very short time. The particle size of the TiO2–CeO2 nano-composite film was found to be 23 nm. The Uric acid biosensor was fabricated by immobilizing mixed enzyme [urease (Urs) and glutamate dehydrogenase (GLDH)] on this nano-composite film showed a response time of 10 s, sensitivity as 0.9016 µAcm−2 mM−1 and detection limit of 0.165 mM. The value of Michaelis–Menten constant (Km) estimated using Line weaver–Burke plot as 4.8 mM which indicates the high affinity of uricase towards its analyte (uric acid). The enhanced sensitivity and linear range of the nanobiocomposite film obtained by cyclic voltammetry measurements suggested that the mixed enzyme and TiO2-CeO2 composite material has novel characteristics for uric acid determination in the human blood for the application of arthritis diseases. © 2019 Ain Shams University

Ain Shams Engineering Journal

Biocompatible TiO2-CeO2 Nano-composite synthesis, characterization and analysis on electrochemical performance for uric acid determination

Carcinoma otherwise called Cancer is the normally developing and most risky ailment happened in human species. Lung Carcinoma is one of them. It is an ailment that happens because of uncontrolled development of harmful cells in the tissues of the lungs. Earlier determination of the ailment spares immense number of lives, bombing in which may prompt other extreme issues causing sudden deadly demise. Thought process of this framework is to mechanize the recognition procedure in order to perform propelled identification of the malady in its beginning period. In this paper an investigation was made to examine the lung tumour expectation utilizing classification algorithms, for example, back propogation neural network and decision tree. At first 20 tumour and non-disease patients’ examples information were gathered with 30 qualities, pre-prepared and dissected utilizing classification algorithms and later a similar methodology was actualized on 50 occurrences (50 Cancer patients and 10 non growth patients). The informational indexes utilized as a part of this examination are taken from UCI data sets for patients affected by lung cancer and Michigan Lung Cancer patient’s informational index. The principle point of this paper is to give the prior notice to the clients and to quantify the execution investigation of the classification algorithms utilizing WEKA Tool. Test comes about demonstrate that the previously mentioned calculation has promising outcomes for this reason with the general forecast exactness of 94 and 95.4%, separately. Another way to deal with identifies the lungs tumour by Decision tree and BPNN calculation will give viable outcome as contrast with other calculation. The proposed framework will improve the execution of prediction and classification. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

A new approach for prediction of lung carcinoma using back propogation neural network with decision tree classifiers

Sensors and Actuators B: Chemical

Potentiometric enzyme biosensor for aflatoxin B1 detection – Kinetic simulation

Optik

Numerical analysis of graphene coated surface plasmon resonance biosensors for biomedical applications

Colloids and Surfaces B: Biointerfaces

Mathematical modeling approaches to describe the dynamics of protein adsorption at solid interfaces

Electrochimica Acta

Optimal design of amperometric biosensors applying multi-objective optimization and decision visualization

ISRN Biochemistry

Analysis of Mathematical Modelling on Potentiometric Biosensors

A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases

Journal	Data powered by TypesetInformatics in Medicine Unlocked
Publisher	Data powered by TypesetElsevier BV
ISSN	2352-9148
Open Access	Yes