Improving performance of neurons by adding colour noise

Noise is not always an interfering signal which perturbs the system. On the contrary, noise signals can enhance the performance of some non‐linear systems such as stochastic resonance (SR). These systems can detect the weak input signal when it is added to the noise signal. According to this property, SR models play a significant role in the functioning of the brain for detecting weak input signals and synchronisation of neural connections. In this study, the authors model neurons as SR systems where different types of noise, i.e. white noise and pink noise, are employed to amplify the weak nervous signals. They demonstrate colour noise, in particular, pink noise enhances the performance of the SR system to amplify the input signal. Furthermore, pink noise has a wider range of optimum values in comparison to white noise. Therefore, they can conclude that neurons are more sensitive to detect the signals that carry pink noise than signals with white noise or without noise. Hence, the retrieving ability of neurons can be improved by adding pink noise.Inspec keywords: stochastic processes, white noise, neural nets, brain, noise, neurophysiologyOther keywords: interfering signal, particular noise, colour noise, weak nervous signals, pink noise, white noise, SR system, authors model neurons, SR models, noise signal, weak input signal, nonlinear systems     

Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms

In this study, three non‐linear indices consist of compression, one‐dimensional (1D) and two‐dimensional (2D) fractal dimensions are used for the determination of the malignancy or benignity of cancer tumours in breast thermograms. On the other hand, by developing the high‐precision infrared cameras as well as new methods of image processing, biomedical thermography images have found a prominent position among the others. Furthermore, cancerous tissue can be affected by the laser. In this study, in order to treat the cancerous lesion identified by breast thermograms, the laser parameters are designed. The basis of controller designing is the obtained non‐linear indices. If the indices are moved from the chaotic behaviour to normal condition, the treating tissue is going from cancerous to a healthy condition and the treatment process is completed. Radiation frequency and the energy density of laser are designed as two key elements in the cancer treatment. In this study, the type I and type II fuzzy controllers are employed for the control strategies. Using the proposed closed‐loop control, the non‐linear indices of the cancerous lesion will be reduced during the treatment process. The simulation results on two datasets of breast thermograms indicate the superiority of type II fuzzy controller.Inspec keywords: closed loop systems, fractals, infrared imaging, tumours, fuzzy control, medical image processing, cancer, biological tissues, gynaecology, patient treatmentOther keywords: cancer tumours, breast thermograms, high‐precision infrared cameras, image processing, cancerous tissue, cancerous lesion, nonlinear indices, type II fuzzy controller, closed‐loop control, fuzzy controller design, breast cancer treatment, 2D fractal dimensions     

Pseudo‐spectral method for controlling the drug dosage in cancer

A mixed chemotherapy–immunotherapy treatment protocol is developed for cancer treatment. Chemotherapy pushes the trajectory of the system towards the desired equilibrium point, and then immunotherapy alters the dynamics of the system by affecting the parameters of the system. A co‐existing cancerous equilibrium point is considered as the desired equilibrium point instead of the tumour‐free equilibrium. Chemotherapy protocol is derived using the pseudo‐spectral (PS) controller due to its high convergence rate and simple implementation structure. Thus, one of the contributions of this study is simplifying the design procedure and reducing the controller computational load in comparison with Lyapunov‐based controllers. In this method, an infinite‐horizon optimal control problem is proposed for a non‐linear cancer model. Then, the infinite‐horizon optimal control of cancer is transformed into a non‐linear programming problem. The efficient Legendre PS scheme is suggested to solve the proposed problem. Then, the dynamics of the system is modified by immunotherapy is another contribution. To restrict the upper limit of the chemo‐drug dose based on the age of the patients, a Mamdani fuzzy system is designed, which is not present yet. Simulation results on four different dynamics cases how the efficiency of the proposed treatment strategy.Inspec keywords: patient treatment, cancer, convergence, linear programming, optimal control, nonlinear programming, nonlinear control systems, Lyapunov methods, drugs, tumoursOther keywords: nonlinear programming problem, efficient Legendre PS scheme, chemo‐drug dose, Mamdani fuzzy system, treatment strategy, pseudospectral method, drug dosage, mixed chemotherapy–immunotherapy treatment protocol, cancer treatment, desired equilibrium point, immunotherapy alters, cancerous equilibrium point, tumour‐free equilibrium, chemotherapy protocol, pseudospectral controller, high convergence rate, simple implementation structure, controller computational load, Lyapunov‐based controllers, infinite‐horizon optimal control problem, nonlinear cancer model     

Estimation of magnetohydrodynamic radiative nanofluid flow over a porous non‐linear stretching surface: application in biomedical research

The present study investigates the effects of thermal radiation and chemical reaction on magnetohydrodynamic flow, heat, and mass transfer characteristics of nanofluids such as Cu–water and Ag–water over a non‐linear porous stretching surface in the presence of viscous dissipation and heat generation. Using similarity transformation, the governing boundary layer equations of the problem are transformed into non‐linear ordinary differential equations and solved numerically by the shooting method along with the Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme. The influences of various parameters on velocity, temperature, and concentration profiles of the flow field are analysed and the results are plotted graphically. A backpropagation neural network is applied to predict the skin friction coefficient, Nusselt number, and Sherwood number and these results are presented through graphs. The present numerical results are compared with the existing results and are found to be in good agreement. The results of artificial neural network and the obtained numerical values agree well with an error <5%.Inspec keywords: silver, copper, transforms, nanofluidics, friction, backpropagation, heat radiation, water, external flows, partial differential equations, nonlinear differential equations, boundary layers, Runge‐Kutta methods, mass transfer, flow through porous media, magnetohydrodynamicsOther keywords: magnetohydrodynamic radiative nanofluid flow, nonlinear stretching surface, biomedical research, thermal radiation, chemical reaction, magnetohydrodynamic flow, nonlinear porous stretching surface, viscous dissipation, similarity transformation, governing boundary layer equations, nonlinear ordinary differential equations, shooting method, Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme, flow field, backpropagation neural network, Cu–water nanofluid, Ag–water nanofluid, skin friction coefficient, Nusselt number, Sherwood number, artificial neural network, Ag‐H₂ O, Cu‐H₂ O     

Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root‐locus method

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non‐linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root‐locus‐based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of which the system shows a certain dynamical behaviour, such as bistability, oscillation, and asymptotically equilibrium dynamics is shown by considering two mostly studied gene regulatory networks, namely Gardner''s genetic toggle switch and p53 gene network possessing two‐phase (mono‐stable/oscillation) dynamics.Inspec keywords: oscillations, curve fitting, differential equations, bifurcation, genetics, nonlinear dynamical systemsOther keywords: nonlinearities, reaction kinetics, root‐locus‐based bifurcation analysis method, complex dynamics, exact parameter regions, dynamical behaviour, equilibrium dynamics, studied gene regulatory networks, p53 gene network, bistable dynamics, oscillatory dynamics, biological networks, root‐locus method, biological systems, ordinary differential equation models     

Sliding mode control for a fractional‐order non‐linear glucose‐insulin system

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional‐order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose–insulin system of a human body is Bergman''s minimal model. This non‐linear model comprises of integer‐order differential equations. However, comparison with the experimental data shows that the fractional‐order version of Bergman''s minimal model is a better representative of the glucose–insulin system than its original integer‐order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional‐order model, different techniques, including feedback linearisation, have been applied in the literature. The authors’ previous work shows that the fractional‐order version of Bergman''s model describes the glucose–insulin system in a better way than the integer‐order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.Inspec keywords: nonlinear control systems, feedback, variable structure systems, differential equations, medical control systems, diseases, control system synthesis, sugar, nonlinear dynamical systemsOther keywords: fractional‐order nonlinear glucose‐insulin, hereditary effects, fractional‐order modelling, extensively, utilised models, glucose–insulin system, Bergman''s minimal model, nonlinear model, integer‐order differential equations, fractional‐order version, original integer‐order model, fractional‐order model, Bergman''s model, sliding mode control technique     

Sliding mode controller–observer pair for p53 pathway

A significant loss of p53 protein, an anti‐tumour agent, is observed in early cancerous cells. Induction of small molecules based drug is by far the most prominent technique to revive and maintain wild‐type p53 to the desired level. In this study, a sliding mode control (SMC) based robust non‐linear technique is presented for the drug design of a control‐oriented p53 model. The control input generated by conventional SMC is discontinuous; however, depending on the physical nature of the system, drug infusion needs to be continuous. Therefore, to obtain a smooth control signal, a dynamic SMC (DSMC) is designed. Moreover, the boundedness of the zero‐dynamics is also proved. To make the model‐based control design possible, the unknown states of the system are estimated using an equivalent control based, reduced‐order sliding mode observer. The robustness of the proposed technique is assessed by introducing input disturbance and parametric uncertainty in the system. The effectiveness of the proposed control scheme is witnessed by performing in‐silico trials, revealing that the sustained level of p53 can be achieved by controlled drug administration. Moreover, a comparative quantitative analysis shows that both controllers yield similar performance. However, DSMC consumes less control energy.Inspec keywords: control system synthesis, tumours, variable structure systems, observers, cancer, robust control, drugs, proteins, medical control systemsOther keywords: wild‐type p53, nonlinear technique, drug design, control‐oriented p53 model, control input, drug infusion, smooth control signal, dynamic SMC, zero‐dynamics, model‐based control design, input disturbance, controlled drug administration, sliding mode controller–observer pair, cancerous cells, antitumour agent, molecule based drug     

Blood glucose concentration control for type 1 diabetic patients: a non‐linear suboptimal approach

In this study, a closed‐loop treatment strategy is proposed for the control of blood glucose levels in type 1 diabetic patients. Toward this end, a non‐linear technique for designing suboptimal tracking controllers, called the state‐dependent Riccati equation tracker, is used based on a mathematical model of the glucose–insulin regulatory system. Since two state variables of the utilised model are not available to the controller, a non‐linear filter is also designed to estimate these variables using the measured blood glucose concentration. Effects of unannounced meals and regular exercise are investigated for a nominal patient and nine diabetic patients with unknown parameters. Numerical simulations are given to show the effectiveness of the proposed treatment strategy even in the presence of parametric uncertainties and the observation noise.Inspec keywords: blood, biochemistry, diseases, patient treatment, Riccati equations, nonlinear filters, medical signal processingOther keywords: blood glucose concentration control, type 1 diabetic patients, nonlinear suboptimal approach, closed‐loop treatment strategy, suboptimal tracking controllers, state‐dependent Riccati equation tracker, glucose‐insulin regulatory system, nonlinear filter, unannounced meals, regular exercise, treatment strategy, parametric uncertainties, observation noise     

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non‐smokers with lung adenocarcinoma

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non‐coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non‐smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co‐expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA–mRNA pairs which were significantly dysregulated in non‐smokers with lung adenocarcinoma. Also two pairs (miR‐133b/Protein Kinase C Zeta (PRKCZ) and miR‐557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors’ understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.Inspec keywords: cancer, molecular biophysics, patient diagnosis, tumours, RNA, proteins, lung, genetics, medical diagnostic computing, molecular configurationsOther keywords: miRNAs expression profiles, mRNAs expression profiles, smokers, nonsmokers, integrative analysis, lung adenocarcinoma, microRNAs, disease specific biomarkers, noncoding regulators, genetic abnormalities, weighted gene coexpression network analysis     

Controllability and observability analysis of basal ganglia model and feedback linearisation control

Deep brain stimulation (DBS) is a clinical remedy to control tremor in Parkinson''s disease. In DBS, one of the two main areas of basal ganglia (BG) is stimulated. This stimulation is produced with no feedback of the tremor and often causes a wide range of unpleasant side effects. Using a feedback signal from tremor, the stimulatory signal can be reduced or terminated to avoid extra stimulation and as a result decrease the side effects. To design a closed‐loop controller for the non‐linear BG model, a complete study of controllability and observability of this system is presented in this study. This study shows that the BG model is controllable and observable. The authors also propose the idea of stimulating the two BG areas simultaneously. A two‐part controller is then designed: a feedback linearisation controller for subthalamic nucleus stimulation and a partial state feedback controller for globus pallidus internal stimulation. The controllers are designed to decrease three indicators: the hand tremor, the level of delivered stimulation signal in disease condition, and the ratio of the level of delivered stimulation signal in health condition to disease condition. Considering these three indicators, the simulation results show satisfactory performance.Inspec keywords: feedback, brain, neurophysiology, diseases, medical control systems, closed loop systems, controllers, linearisation techniques, bioelectric phenomenaOther keywords: controllability analysis, observability analysis, basal ganglia model, feedback linearisation control, deep brain stimulation, clinical remedy, tremor control, Parkinson''s disease, feedback signal, closed‐loop controller, nonlinear BG model, feedback linearisation controller, two‐part controller, subthalamic nucleus stimulation, partial state feedback controller, globus pallidus internal stimulation, disease condition, delivered stimulation signal     

Application of conditional robust calibration to ordinary differential equations models in computational systems biology: a comparison of two sampling strategies

Mathematical modelling is a widely used technique for describing the temporal behaviour of biological systems. One of the most challenging topics in computational systems biology is the calibration of non‐linear models; i.e. the estimation of their unknown parameters. The state‐of‐the‐art methods in this field are the frequentist and Bayesian approaches. For both of them, the performance and accuracy of results greatly depend on the sampling technique employed. Here, the authors test a novel Bayesian procedure for parameter estimation, called conditional robust calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin hypercube sampling. CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. They apply CRC with both sampling strategies to the three ordinary differential equations (ODEs) models of increasing complexity. They obtain a more precise and reliable solution through logarithmically spaced samples.Inspec keywords: sampling methods, parameter estimation, Bayes methods, differential equations, iterative methodsOther keywords: CRC, parameter space sampling, parameter density functions, sampling strategies, ordinary differential equations models, logarithmically spaced samples, computational systems biology, mathematical modelling, temporal behaviour, biological systems, challenging topics, nonlinear models, unknown parameters, frequentist approaches, Bayesian approaches, sampling technique, novel Bayesian procedure, parameter estimation, called conditional robust calibration, different sampling techniques     

Non‐linear feedback control of the p 53 protein–mdm 2 inhibitor system using the derivative‐free non‐linear Kalman filter

It is proven that the model of the p 53–mdm 2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p 53 protein at the desirable levels. To estimate the non‐measurable elements of the state vector describing the p 53–mdm 2 system dynamics, the derivative‐free non‐linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p 53–mdm 2 system, the derivative‐free non‐linear Kalman filter is re‐designed as a disturbance observer. The derivative‐free non‐linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non‐linear model. The proposed non‐linear feedback control and perturbations compensation method for the p 53–mdm 2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.Inspec keywords: proteins, molecular biophysics, biochemistry, Kalman filters, inverse problems, perturbation theoryOther keywords: nonlinear feedback control, p53 protein‐mdm2 inhibitor system, derivative‐free nonlinear Kalman filter, differential flatness theory, protein synthesis loop, diffeomorphism, protein synthesis model, feedback control law, nonmeasurable elements, modelling uncertainties, inverse transformation, nonlinear model, perturbation compensation method, chemotherapy schemes, medication infusion     

Chitosan nanoparticles loaded with aspirin and 5‐fluororacil enable synergistic antitumour activity through the modulation of NF‐κB/COX‐2 signalling pathway

Based on the enhancement of synergistic antitumour activity to treat cancer and the correlation between inflammation and carcinogenesis, the authors designed chitosan nanoparticles for co‐delivery of 5‐fluororacil (5‐Fu: an as anti‐cancer drug) and aspirin (a non‐steroidal anti‐inflammatory drug) and induced synergistic antitumour activity through the modulation of the nuclear factor kappa B (NF‐κB)/cyclooxygenase‐2 (COX‐2) signalling pathways. The results showed that aspirin at non‐cytotoxic concentrations synergistically sensitised hepatocellular carcinoma cells to 5‐Fu in vitro. It demonstrated that aspirin inhibited NF‐κB activation and suppressed NF‐κB regulated COX‐2 expression and prostaglandin E2 (PGE2) synthesis. Furthermore, the proposed results clearly indicated that the combination of 5‐Fu and aspirin by chitosan nanoparticles enhanced the intracellular concentration of drugs and exerted synergistic growth inhibition and apoptosis induction on hepatocellular carcinoma cells by suppressing NF‐κB activation and inhibition of expression of COX‐2.Inspec keywords: proteins, molecular biophysics, cellular biophysics, biomedical materials, cancer, nanoparticles, drug delivery systems, enzymes, tumours, nanomedicine, drugsOther keywords: chitosan nanoparticles, aspirin, 5‐fluororacil, synergistic antitumour activity, anticancer drug, nonsteroidal antiinflammatory drug, hepatocellular carcinoma cells, NF‐κB activation, NF‐κB regulated COX‐2 expression, PGE2, synergistic growth inhibition, apoptosis induction, prostaglandin E2 synthesis, intracellular concentration, noncytotoxic concentrations, NF‐κB‐cyclooxygenase‐2 signalling pathways, cyclooxygenase‐2, nuclear factor kappa B     

Optimal minimum variance‐entropy control of tumour growth processes based on the Fokker–Planck equation

The authors demonstrated an optimal stochastic control algorithm to obtain desirable cancer treatment based on the Gompertz model. Two external forces as two time‐dependent functions are presented to manipulate the growth and death rates in the drift term of the Gompertz model. These input signals represent the effect of external treatment agents to decrease tumour growth rate and increase tumour death rate, respectively. Entropy and variance of cancerous cells are simultaneously controlled based on the Gompertz model. They have introduced a constrained optimisation problem whose cost function is the variance of a cancerous cells population. The defined entropy is based on the probability density function of affected cells was used as a constraint for the cost function. Analysing growth and death rates of cancerous cells, it is found that the logarithmic control signal reduces the growth rate, while the hyperbolic tangent–like control function increases the death rate of tumour growth. The two optimal control signals were calculated by converting the constrained optimisation problem into an unconstrained optimisation problem and by using the real–coded genetic algorithm. Mathematical justifications are implemented to elucidate the existence and uniqueness of the solution for the optimal control problem.Inspec keywords: optimal control, genetic algorithms, cancer, Fokker‐Planck equation, cellular biophysics, stochastic systems, probability, tumours, entropy, medical control systemsOther keywords: cancer treatment, Gompertz model, time‐dependent functions, process input signals, external treatment agents, tumour growth rate, constrained optimisation problem, cost function, cancerous cells population, probability density function, logarithmic control signal, Fokker‐Planck equation, tumour growth process, optimal control signals, optimal control problem, optimal minimum variance‐entropy control, optimal stochastic control algorithm, tumour death rates, hyperbolic tangent‐like control function, unconstrained optimisation problem, real‐coded genetic algorithm     

Serum analysis method combining cellulose acetate membrane purification with surface‐enhanced Raman spectroscopy for non‐invasive HBV screening

A highly sensitive, non‐invasive, and rapid HBV (Hepatitis B virus) screening method combining membrane protein purification with silver nanoparticle‐based surface‐enhanced Raman scattering (SERS) spectroscopy was developed in this study. Reproducible serum protein SERS spectra were obtained from cellulose acetate membrane‐purified human serum from 94 HBV patients and 89 normal groups. Tentative assignments of serum protein SERS spectra showed that the HBV patients primarily led to specific biomedical changes of serum protein. Principal components analysis and linear discriminate analysis were introduced to analyse the obtained spectra, with the diagnostic sensitivity of 92.6% and specificity of 77.5% were achieved for differentiating HBV patients from normal groups.Inspec keywords: patient diagnosis, surface enhanced Raman scattering, proteins, biomembranes, principal component analysis, purification, silver, nanoparticles, nanomedicine, diseasesOther keywords: serum analysis method, cellulose acetate membrane purification, surface‐enhanced Raman spectroscopy, noninvasive HBV screening, rapid HBV screening method, Hepatitis B virus, membrane protein purification, silver nanoparticle‐based surface‐enhanced Raman scattering spectroscopy, reproducible serum protein SERS spectra, cellulose acetate membrane‐purified human serum, linear discriminate analysis, diagnostic sensitivity, HBV patient, principal components analysis     

Small‐sized probe for local measuring electrical properties of the tissues inside of human body: design,modelling and simulation

In this study, a new idea is suggested for designing an appropriate bio‐impedance probe in the form of a biopsy forceps to measure the electrical properties of the tissues inside the body. First, by analytically solving the Laplace equation for wedge‐shaped tissue in the mouth of the probe, the relationship between electric potential (results from excitation current) in a different point on the tissue and the electrical properties of the tissue is obtained. Then, to evaluate the designed bio‐impedance probe using the finite element method and the experimental data obtained for different tissues by Gabriel et al., modelling and simulation at different frequencies from 50 Hz to 5 MHz were done. Finally, to evaluate the performance of the designed probe in comparison to other methods, measurements were carried out using three methods for the same tissue. Nyquist curves were drawn and electrical properties extracted for all the three methods. It was found that the designed probe results are close to the actual values with an error of <2%. The main features of the designed probe are small size and non‐invasive measurement.Inspec keywords: Laplace equations, biological tissues, finite element analysis, electric impedance measurement, bioelectric potentials, biomedical measurementOther keywords: noninvasive measurement, local measuring electrical properties, human body, wedge‐shaped tissue, electric potential, finite element method, bio‐impedance probe, small‐sized probe, biopsy forceps, excitation current, Nyquist curves, frequency 50.0 Hz to 5.0 MHz     

Silver nanoparticles fabricated using medicinal plant extracts show enhanced antimicrobial and selective cytotoxic propensities

Nanoparticles fabricated using medicinal plant extract have great potential in the area of nanomedicine. High surface‐to‐volume ratio of nanoparticle enhances the local active biomolecules concentration, leading to many fold increase in the medicinal potentials. The silver nanoparticles (AgNPs) fabricated using indigenous medicinal plants of India, Azadirachta indica and Syzygium cumini, have shown a significant effect on the viability of prokaryotic and eukaryotic cells. Biofabrication of AgNP was confirmed using different spectroscopic and microscopic techniques. Extraction and purification of AgNP from non‐conjugated plant moieties are done using centrifugation and size exclusion chromatography. The cytotoxic propensity of AgNP formulations was screened against Gram‐positive (Bacillus subtilis), Gram‐negative (Escherichia coli) bacteria, cancerous (HT1080) and non‐cancerous (HEK293) cell lines. The nanoparticle formulations showed a relatively higher cytotoxic propensity against Gram‐positive bacteria and cancerous cell lines. In addition, the surface roughness and reactive oxygen species (ROS) measurements indicated that AgNP formulations mediate the cell activity predominantly by ROS‐mediated disruptive change in membrane morphology upon direct interaction with the membrane. Hence, the nanoparticle formulations show an enhanced selective cytotoxic propensity towards Gram‐positive bacteria and cancerous cell lines.Inspec keywords: nanofabrication, chromatography, nanoparticles, purification, toxicology, drugs, drug delivery systems, antibacterial activity, microorganisms, cancer, silver, surface roughness, nanomedicine, cellular biophysicsOther keywords: medicinal plant extracts, medicinal potentials, prokaryotic cells, eukaryotic cells, microscopic techniques, nonconjugated plant moieties, centrifugation, AgNP formulations, noncancerous cell lines, nanoparticle formulations, Gram‐positive bacteria, cancerous cell lines, surface roughness, cell activity, local active biomolecule concentration     

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non‐linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10‐fold cross‐validation, for 110 real cases, and the results were compared with those of support vector machine and K ‐nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non‐commercial purposes.Inspec keywords: patient diagnosis, fuzzy logic, diseases, medical computing, cognition, learning (artificial intelligence), fuzzy set theory, Hebbian learning, neural nets, support vector machinesOther keywords: ischemic stroke, controllable factors, uncontrollable factors, blood pressure, heart disease, intelligent system, immediate treatment, soft computing method, fuzzy cognitive mapping, nonlinear Hebbian learning method, fuzzy cognitive maps training, risk rate     

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method

This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra‐patient parametric variation and non‐availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed‐loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post‐prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed‐loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post‐prandial hyperglycemia are significantly reduced in the presence of bounded (±30% ) parametric variability and uncertain exogenous meal disturbance.Inspec keywords: medical control systems, observers, uncertain systems, nonlinear control systems, robust control, control system synthesis, linear matrix inequalities, feedback, sugar, closed loop systems, diseasesOther keywords: virtual T1D patients, type 1 diabetes patients, closed‐loop simulations, uncertain conditions, post‐prandial hyperglycemia, designed controller, closed‐loop response, controller gains, linear matrix inequality, controller conditions, T1D patient model, control technique, intra‐patient parametric variation, principal objective, plasma glucose concentration, uncertain nonlinear system, robust observer based output feedback control law, attractive ellipsoid method, plasma glucose regulation