Blood glucose concentration control for type 1 diabetic patients: a multiple‐model strategy

In this study, a multiple‐model strategy is evaluated as an alternative closed‐loop method for subcutaneous insulin delivery in type 1 diabetes. Non‐linearities of the glucose–insulin regulatory system are considered by modelling the system around five different operating points. After conducting some identification experiments in the UVA/Padova metabolic simulator (accepted simulator by the US Food and Drug Administration (FDA)), five transfer functions are obtained for these operating points. Paying attention to some physiological facts, the control objectives such as the required settling time and permissible bounds of overshoots and undershoots are determined for any transfer functions. Then, five PID controllers are tuned to achieve these objectives and a bank of controllers is constructed. To cope with difficulties of the presence of delays in subcutaneous blood glucose (BG) measuring and in administration of insulin, a glucose‐dependent setpoint is considered as the desired trajectory for the BG concentration. The performance of the obtained closed‐loop glucose–insulin regulatory system is investigated on the in silico adult cohort of the UVA/Padova metabolic simulator. The obtained results show that the proposed multiple‐model strategy leads to a closed‐loop mechanism with limited hyperglycemia and no severe hypoglycemia.Inspec keywords: blood, patient diagnosis, medical control systems, biochemistry, three‐term control, closed loop systems, diseases, patient treatment, drugs, sugarOther keywords: blood glucose concentration control, type 1 diabetic patients, multiple‐model strategy, alternative closed‐loop method, subcutaneous insulin delivery, type 1 diabetes, transfer functions, control objectives, PID controllers, subcutaneous blood glucose measuring, glucose‐dependent setpoint, closed‐loop glucose–insulin regulatory system, closed‐loop mechanism     

Arbitrary‐order sliding mode‐based robust control algorithm for the developing artificial pancreas mechanism

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed‐loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed‐loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary‐order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n ‐order controllable canonical system. The proposed control algorithm affirms the finite‐time effective stabilisation of the glucose–insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non‐linear terms. In addition, the necessary discontinuous terms are filtered‐out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.Inspec keywords: diseases, blood, control system synthesis, medical control systems, feedback, sugar, stability, closed loop systems, robust control, variable structure systemsOther keywords: finite‐time effective stabilisation, glucose–insulin regulatory system, sliding manifold, nonlinear terms, necessary discontinuous terms, employment, diabetic patient, arbitrary‐order sliding mode‐based robust control algorithm, developing artificial pancreas mechanism, Diabetes Mellitus, insulin administration, escalated glycaemic concentration, closed‐loop control strategy, exogenous insulin infusion, closed‐loop structure, different feedback control strategies, mode control approach, n‐order controllable canonical system, control algorithm affirms     

Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data

The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed‐loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data‐based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.Inspec keywords: medical control systems, closed loop systems, particle swarm optimisation, parameter estimation, biochemistry, diseases, patient monitoring, patient diagnosis, blood, sugar, patient treatment, medical computingOther keywords: meal intake, metabolic state, mathematical modelling, postprandial state, closed‐loop control system, automatic insulin delivery, T1DM, type 1 diabetes mellitus, therapy decisions, blood glucose concentration, TIDM patients, meal glucose–insulin model, mathematical model, parameter estimation problem, data‐based personalisation, glucose metabolism, insulin supply, carbohydrate intake, glucose records, therapy historical data, glycaemic state     

Robust multi‐objective blood glucose control in Type‐1 diabetic patient

In this study, an automatic robust multi‐objective controller has been proposed for blood glucose (BG) regulation in Type‐1 Diabetic Mellitus (T1DM) patient through subcutaneous route. The main objective of this work is to control the BG level in T1DM patient in the presence of unannounced meal disturbances and other external noises with a minimum amount of insulin infusion rate. The multi‐objective output‐feedback controller with H_∞, H₂ and pole‐placement constraints has been designed using linear matrix inequality technique. The designed controller for subcutaneous insulin delivery was tested on in silico adult and adolescent subjects of UVa/Padova T1DM metabolic simulator. The experimental results show that the closed‐loop system tracks the reference BG level very well and does not show any hypoglycaemia effect even during the long gap of a meal at night both for in silico adults and adolescent. In the presence of 50 gm meal disturbance, average adult experience normoglycaemia 92% of the total simulation time and hypoglycaemia 0% of total simulation time. The robustness of the controller has been tested in the presence of irregular meals and insulin pump noise and error. The controller yielded robust performance with a lesser amount of insulin infusion rate than the other designed controllers reported earlier.Inspec keywords: robust control, patient treatment, diseases, closed loop systems, patient monitoring, biochemistry, medical control systems, blood, organic compoundsOther keywords: robust multiobjective blood glucose control, automatic robust multiobjective controller, blood glucose regulation, Type‐1 Diabetic Mellitus patient, BG level, T1DM patient, insulin infusion rate, multiobjective output‐feedback controller, pole‐placement constraints, linear matrix inequality technique, subcutaneous insulin delivery, total simulation time, insulin pump noise, adolescent subjects, meal disturbance, normoglycaemia 92, in silico adults, UVa‐Padova T1DM metabolic simulator, closed‐loop system, hypoglycaemia effect     

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     

Estimation of parameters for plasma glucose regulation in type‐2 diabetics in presence of meal

In this study, the authors propose a methodology for the estimation of glucose masses in stomach (both in solid and liquid forms), intestine, plasma and tissue; insulin masses in portal vein, liver, plasma and interstitial fluid using only plasma glucose measurement. The proposed methodology fuses glucose–insulin homoeostasis model (in the presence of meal intake) and plasma glucose measurement with a Bayesian non‐linear filter. Uncertainty of the model over individual variations has been incorporated by adding process noise to the homoeostasis model. The estimation is carried out over 24 h for the healthy people as well as a type II diabetes mellitus patients. In simulation, the estimator follows the truth accurately for both the cases. Moreover, the performances of two non‐linear filters, namely the unscented Kalman filter (KF) and cubature quadrature KF are compared in terms of root mean square error. The proposed methodology will be helpful in future to: (i) observe a patient''s insulin–glucose profile, (ii) calculate drug dose for any hyperglycaemic patients and (iii) develop a closed‐loop controller for automated insulin delivery system.Inspec keywords: blood, diseases, biochemistry, parameter estimation, biological tissues, liver, Bayes methods, nonlinear filters, Kalman filters, drugs, drug delivery systems, medical signal processingOther keywords: automated insulin delivery system, closed‐loop controller, hyperglycaemic patients, drug dose, root mean square error, cubature quadrature KF, Kalman filter, type II diabetes mellitus, process noise, Bayesian nonlinear filter, glucose‐insulin homoeostasis model, interstitial fluid, liver, portal vein, insulin mass, biological tissues, intestine, stomach, glucose mass, meal intake, type‐2 diabetics, plasma glucose regulation, parameter estimation     

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     

Hypoglycaemia‐free artificial pancreas project

Driving blood glycaemia from hyperglycaemia to euglycaemia as fast as possible while avoiding hypoglycaemia is a major problem for decades for type‐1 diabetes and is solved in this study. A control algorithm is designed that guaranties hypoglycaemia avoidance for the first time both from the theory of positive systems point of view and from the most pragmatic clinical practice. The solution consists of a state feedback control law that computes the required hyperglycaemia correction bolus in real‐time to safely steer glycaemia to the target. A rigorous proof is given that shows that the control‐law respects the positivity of the control and of the glucose concentration error: as a result, no hypoglycaemic episode occurs. The so‐called hypo‐free strategy control is tested with all the UVA/Padova T1DM simulator patients (i.e. ten adults, ten adolescents, and ten children) during a fasting‐night scenario and in a hybrid closed‐loop scenario including three meals. The theoretical results are assessed by the simulations on a large cohort of virtual patients and encourage clinical trials.Inspec keywords: biochemistry, medical control systems, blood, diseases, medical computing, closed loop systems, biomedical equipment, state feedback, patient treatment, patient monitoring, biomedical measurement, physiological models, sugarOther keywords: fasting‐night scenario, hybrid closed‐loop scenario, hypoglycaemia‐free artificial pancreas project, blood glycaemia, euglycaemia, type‐1 diabetes, control algorithm, guaranties hypoglycaemia avoidance, pragmatic clinical practice, state feedback control law, required hyperglycaemia correction bolus, rigorous proof, control‐law, glucose concentration error, hypo‐free strategy control     

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control‐based approach

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.Inspec keywords: patient monitoring, adaptive control, diseases, Lyapunov methods, closed loop systems, medical control systems, patient treatment, medical computing, sugar, uncertain systems, blood, nonlinear control systems, physiological modelsOther keywords: blood glucose regulation, type 1 diabetic patients, adaptive parametric compensation control‐based approach, direct adaptive control strategy, nonlinear model, type 1 diabetes mellitus patients, uncertain parameters, appropriate design, adaptation laws, closed‐loop response, plasma glucose concentration, external insulin infusion rate, model parameters, adaptive control scheme, parametric uncertainty, inter‐patient variability     

Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro‐fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro‐fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi–Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute‐infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.Inspec keywords: epidemics, diseases, blood, medical computing, microorganisms, genetic algorithms, fuzzy control, neurocontrollers, adaptive control, medical control systemsOther keywords: genetic algorithm, hepatitis C blood born virus, liver disease, hepatitis C virus infection, epidemiology, intelligent controller, optimal adaptive neuro‐fuzzy controller, adaptive neuro‐fuzzy inference system, ANFIS, genetic algorithm, control signal, state condition, objective function minimisation, Takagi‐Sugeno fuzzy structure, acute‐infected individuals, ordinary optimal control methods, HCV epidemic     

Deploying swarm intelligence in medical imaging identifying metastasis,micro‐calcifications and brain image segmentation

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X‐ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X‐rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence‐learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration‐dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.Inspec keywords: swarm intelligence, image segmentation, brain, neurophysiology, medical image processing, biomedical MRI, computerised tomography, diagnostic radiography, bone, diseases, learning (artificial intelligence), particle swarm optimisation, iterative methods, tumours, medical disordersOther keywords: medical imaging identifying metastasis, microcalcifications, umbrella deployment, stochastic diffusion, metastasis identification, bone scans, mammographs, CT imaging, aorta, nasogastric tube, chest X‐ray, hybrid swarm intelligence‐learning vector quantisation approach, magnetic resonance brain image segmentation, particle swarm optimisation, iteration‐dependent nature, tumour regions, abnormal MR brain imaging     

In silico discovery of significant pathways in colorectal cancer metastasis using a two‐stage optimisation approach

Accurate and reliable modelling of protein–protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine learning methods such as empirical mode decomposition combined with least square support vector machine, and discrete Fourier transform have been widely utilised as a classifier and for automatic discovery of biomarkers for the diagnosis of the disease. The existing methods are, however, less efficient as they tend to ignore interaction with the classifier. In this study, the authors propose a two‐stage optimisation approach to effectively select biomarkers and discover interactions among them. At the first stage, particle swarm optimisation (PSO) and differential evolution (DE) are used to optimise parameters of support vector machine recursive feature elimination algorithm, and dynamic Bayesian network is then used to predict temporal relationship between biomarkers across two time points. Results show that 18 and 25 biomarkers selected by PSO and DE‐based approach, respectively, yields the same accuracy of 97.3% and F1‐score of 97.7 and 97.6%, respectively. The stratified analysis reveals that Alpha‐2‐HS‐glycoprotein was a dominant hub gene with multiple interactions to other genes including Fibrinogen alpha chain, which is also a potential biomarker for colorectal cancer.Inspec keywords: cancer, proteins, particle swarm optimisation, evolutionary computation, support vector machines, recursive functions, Bayes methods, genetics, molecular biophysics, medical computingOther keywords: colorectal cancer metastasis, two‐stage optimisation approach, protein–protein interaction networks, biomarkers, particle swarm optimisation, differential evolution, support vector machine recursive feature elimination, dynamic Bayesian network, stratified analysis, Alpha‐2‐HS‐glycoprotein, hub gene, Fibrinogen alpha chain     

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

Here, a two‐phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi‐objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache‐based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.Inspec keywords: cancer, biological organs, optimisation, feature extraction, search problems, particle swarm optimisation, pattern classification, geneticsOther keywords: biomarkers, gene expression feature selection, prostate cancer diagnosis, heuristic–deterministic search strategy, two‐phase search strategy, gene expression data, statistical filtering method, noisiest data, multiobjective optimisation, particle swarm optimisation algorithm, chaotic method, selection algorithm, discriminant genes, available challenging prostate cancer data, informative genes     

Adaptive fractional‐order blood glucose regulator based on high‐order sliding mode observer

Type I diabetes is described by the destruction of the insulin‐producing beta‐cells in the pancreas. Hence, exogenous insulin administration is necessary for Type I diabetes patients. In this study, to estimate the states that are not directly available from the Bergman minimal model a high‐order sliding mode observer is proposed. Then fractional calculus is combined with sliding mode control (SMC) for blood glucose regulation to create more robustness performance and make more degree of freedom and flexibility for the proposed method. Then an adaptive fractional‐order SMC is proposed. The adaptive SMC protect controller against disturbance and uncertainties while the fractional calculus provides robust performance. Numerical simulation verifies that the proposed controllers have better performance in the presence of disturbance and uncertainties without chattering.Inspec keywords: variable structure systems, biochemistry, blood, robust control, medical control systems, observers, sugar, diseases, calculus, adaptive control, cellular biophysicsOther keywords: fractional‐order SMC, adaptive SMC, fractional calculus, robust performance, adaptive fractional‐order blood glucose regulator, insulin‐producing beta‐cells, exogenous insulin administration, diabetes patients, Bergman minimal model, mode control, blood glucose regulation, pancreas, type I diabetes, state estimation, high‐order sliding mode observer, sliding mode control, degree of freedom, numerical simulation     

Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients

This study designs a robust closed‐loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back‐stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman''s minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super‐twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller''s robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.Inspec keywords: closed loop systems, robust control, sugar, medical control systems, variable structure systems, control system synthesis, blood, nonlinear control systems, adaptive control, diseasesOther keywords: adaptive robust control design, blood glucose regulation, type 1 diabetes patients, closed‐loop control algorithm, elevated blood glucose level stabilisation, type 1 diabetic patients, novel control action, algebraic meal disturbance estimator, mode control technique, accurate estimation, estimated disturbance, super‐twisting sliding mode control, algebraic disturbance estimator, BISMC controller, algebraic meal disturbance estimation, back‐stepping integral sliding mode control technique     

Control of depth of anaesthesia using fractional‐order adaptive high‐gain controller

This study presents a fractional‐order adaptive high‐gain controller for control of depth of anaesthesia. To determine the depth of anaesthesia, the bispectral index (BIS) is utilised. To attain the desired BIS, the propofol infusion rate (as the control signal) should be appropriately adjusted. The effect of the propofol on the human body is modelled with the pharmacokinetic–pharmacodynamic (PK/PD) model. Physical properties of the patient such as gender, age, height and a like determine the parameters of the PK/PD model. This necessitates us to employ an appropriate adaptive controller. To attain this goal, a fractional‐order adaptive high‐gain controller is constructed to solve the tracking problem for minimum phase systems with relative degree two (such as the PK/PD model). This leads to a time‐varying gain adjusting according to a fractional‐order adaptation mechanism. Simulation results performed on various patients (considering the external disturbance and the measurement noise) show the effectiveness of the proposed method.Inspec keywords: medical control systems, gain control, adaptive control, closed loop systems, time‐varying systemsOther keywords: fractional‐order adaptive high‐gain controller, control signal, pharmacokinetic–pharmacodynamic model, fractional‐order adaptation mechanism, anaesthesia depth control, bispectral index, PK‐PD model, propofol infusion rate, tracking problem, minimum phase systems, time‐varying gain, BIS     

Extended particle swarm optimisation method for folding protein on triangular lattice

In this study, the authors studied the protein structure prediction problem by the two‐dimensional hydrophobic–polar model on triangular lattice. Particularly the non‐compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors’ algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time.Inspec keywords: proteins, molecular biophysics, molecular configurations, particle swarm optimisation, bioinformaticsOther keywords: extended particle swarm optimisation method, triangular lattice, protein structure prediction problem, two‐dimensional hydrophobic–polar model, noncompact conformation, amino acid sequence, single point adjustment strategy, protein folding     

Efficient heart disease prediction‐based on optimal feature selection using DFCSS and classification by improved Elman‐SFO

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre‐processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function‐based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS–IENN‐based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS–IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high‐classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K‐nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.Inspec keywords: cardiovascular system, medical diagnostic computing, feature extraction, regression analysis, data mining, learning (artificial intelligence), Bayes methods, neural nets, support vector machines, diseases, pattern classification, data handling, decision trees, cardiology, data analysis, feature selectionOther keywords: efficient heart disease prediction‐based, optimal feature selection, improved Elman‐SFO, cardiovascular disease, clinical data analysis, data pre‐processing process, data cleaning, data transformation, values imputation, data normalisation, decision function‐based chaotic salp swarm algorithm, optimal features, feature selection process, improved Elman neural network, data classification, sailfish optimisation algorithm, optimal weight value, DFCSS–IENN‐based SFO algorithm, DFCSS–IESFO, California Irvine Cleveland heart disease dataset, CVD dataset, high‐classification accuracy     

In vitro biological evaluation of anti‐diabetic activity of organic–inorganic hybrid gold nanoparticles

Diabetes mellitus has been considered as a heterogeneous metabolic disorder characterised by complete or relative impairment in the production of insulin by pancreatic β‐cells or insulin resistance. In the present study, propanoic acid, an active biocomponent isolated from Cassia auriculata is employed for the synthesis of propanoic acid functionalised gold nanoparticles (Pa@AuNPs) and its anti‐diabetic activity has been demonstrated in vitro. In vitro cytotoxicity of synthesised Pa@AuNPs was performed in L6 myotubes. The mode of action of Pa@AuNPs exhibiting anti‐diabetic potential was validated by glucose uptake assay in the presence of Genistein (insulin receptor tyrosine kinase inhibitor) and Wortmannin (Phosphatidyl inositide kinase inhibitor). Pa@AuNPs exhibited significant glucose uptake in L6 myotubes with maximum uptake at 50 ng/ml. Assays were performed to study the potential of Pa@AuNPs in the inhibition of protein‐tyrosine phosphatase 1B, α‐glucosidases, and α‐amylase activity.Inspec keywords: molecular biophysics, biomedical materials, sugar, enzymes, nanofabrication, gold, patient treatment, organic‐inorganic hybrid materials, biochemistry, diseases, cellular biophysics, nanoparticles, toxicology, nanomedicineOther keywords: glucose uptake assay, α‐amylase activity, organic–inorganic hybrid gold nanoparticles, diabetes mellitus, heterogeneous metabolic disorder, pancreatic β‐cells, insulin resistance, propanoic acid, antidiabetic potential, antidiabetic activity, in vitro cytotoxicity, L6 myotubes, Genistein, IRTK inhibitor, Wortmannin, P13K inhibitor, protein‐tyrosine phosphatase 1B, α‐glucosidases, Cassia auriculata, Au