Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review

Al2O3/ZrO2 dual-dielectric Gr/CNT nanoribbon vertical tunnel FET based biosensor for genomic classification and S-protein detection in SARS-CoV-2

The ongoing genetic mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) possesses the capacity to inadvertently lead to an increase in both the rates of transmission and mortality. In this study, we showcase the use of an Al2O3/ZrO2 Dual-Dielectric Gr/CNT Nanoribbon vertical tunnel field-effect transistor biosensor for the purpose of detecting spike proteins of SARS-CoV-2 in clinical samples. The proteins mentioned above are situated within the protein capsids of the virus. The effectiveness of the suggested detector has been assessed through measurements of the alteration in current drain. The present study utilizes the dielectric coefficient analogue of viral proteins as a substitute for biomolecules that exhibit internal hybridization nanogaps. The high sensitivity of the suggested detector, as evaluated on a scale ranging from 0 to 115, suggests its potential as a high-quality sensing instrument. The purpose of this study is to examine the sensitivity of DNA charge density with the aim of identifying any alterations in the virus that may impact its ability to spread and infect humans. The chromosomal composition of SARS-CoV-2 has been determined. The CMC Research Centre, situated in Vellore, Tamil Nadu, India, conducted an examination of SARS-CoV-2 samples. The scientists possess the capability to do genome sequencing on these specimens, so facilitating the examination of mutation patterns and the dispersion of different clades. A total of 250 different mutations were found out of the 600 sequences that were evaluated. The sequencing data consists of a complete collection of 250 distinct variants, including 150 missense mutations, 80 synonymous mutations, 15 mutations in noncoding regions, and 5 deletions. The comprehension of genetic variety is significantly dependent on these mutations. The proposed detector is connected to a variety of previously documented biosensors based on field-effect transistors (FETs), which are employed for the examination of genetic modifications.

Fact Finding Instructor-based Clustering Technique for BP Estimation using Human Speech Signals

Blood Pressure (BP) is considered an essential factor that provides information regarding cardiovascular function. Regular monitoring of the BP is required for proper healthcare maintenance that avoids the high risk of life due to high and low BP. Several methods were devised for the estimation of BP, but the estimation accuracy is still a challenging task. Hence this research introduces an efficient BP estimation technique using the Fact Finding Instructor (FFI) based clustering method by considering the speech signal of the patients. An efficient BP extraction technique is introduced using the FFI Optimization algorithm an integration of the mannerism of the fact finder that identifies the suspect who commits the criminal offense and, with the instructor with good knowledge, these make the trainee more efficient. The detection and suspect's arrest contain two phases, the fact-finding phase and the chasing phase. Initially, the speech signal is collected from the database and pre-processed for removing noise and artifacts. Then feature extraction is used for the minimization of the computation overhead that generates a feature vector. The clustering of BP is employed with the k-means clustering algorithm and the proposed FFI optimization algorithm. The FFI Optimization algorithm provides a fast convergence rate due to the fact-finding phase and provides accurate detection of the suspect's location along with that the clustering of classes of patients' BP by considering the feature of the speech signal. The clusters formed using the FFI optimization algorithm are combined with the K-means clustering, by multiplying the clusters the BP estimation is implemented on three criteria Low BP, Normal, and, High BP. Finally, the output generated by both the clustering operations is multiplied together for the estimation of the BP. The performance of the proposed method is evaluated using the metrics like Davies Bouldin score, Homogeneity score, Completeness score, Jacquard Similarity score, Silhouette score, and Dunn's Index which acquired the improvement rate of 0.98, 0.96, 0.96, 0.98, 0.95, and 0.98 for training percentage 90, respectively to the existing Teaching Learning Based Optimization(TLBO) clustering technique.

Biosensor nanoarchitectonics of Cu-Fe-nanoparticles/Zeolite-A/Graphene nanocomposite for enhanced electrooxidation and dopamine detection

Cu-Fe NPs/ZEA/Gr electrochemical biosensor is developed by sol-gel spin coating technique, where copper-iron nanoparticles (Cu-Fe NPs) is synthesized using a chemical reduction method and modified with Zeolite & Graphene to develop a hybrid nanocomposite - Cu-Fe NPs/ZEA/Gr. The synthesized nanocomposite is then mixed with poly (vinyl alcohol) as a binding agent and coated on to the glass substrate to produce thin film electrode. Then the electrode was analyzed for structural and morphological studies using XRD, SEM, TEM, UV-VIS, absorption, and emission spectra. The presence of Cu-Fe NPs, ZEA, and Gr in the nanocomposite is confirmed by the XRD diffraction peaks, while SEM investigation revealed that the hybrid composite has a particle size of around 7.25 nm with a body-centred cubic structure. The TEM images show that bimetallic nanoparticles were incorporated into the ZEA shell, which was surrounded by a layer of transparent graphene. Furthermore, the nanocomposite exhibited a distinct absorption peak at 395 nm, as evidenced by UV-VIS, absorption, and emission spectra. The electrochemical tests demonstrated that the Cu-Fe NPs/ZEA/Gr nanocomposite electrode showed an excellent electrocatalytic and selective properties towards the electrooxidation of dopamine to dopamine-o-quinone. The detection limit of the Cu-Fe NPs/ZEA/Gr nanocomposite thin film was found to be 0.058 μM, with a sensitivity of 1.97 μAμM-1cm-2. The enhanced catalytic performance of the Cu-Fe NPs/ZEA/Gr electrode is attributed to the unique nanostructured materials coating on the glass substrate. The findings suggest that nano-hybrid materials can be a viable option for developing electrochemical biosensors to monitor dopamine levels in biological fluids. This indicates that the concept of nanoarchitectonics utilized to produce dopamine sensors may lead to new diagnostic and therapeutic approaches for neurological disorders associated with dopamine dysregulation.

Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software

Among the wearable sensor family, the triboelectric nanogenerator has excellent potential in human healthcare systems due to its small size, self-powered, and low cost. Here is the design and simulation of the triboelectric nanogenerator using the 3D model in COMSOL Multiphysics software for blood pressure measurement. As a reliable indicator of human physiological health, blood pressure (BP) has been utilized in more and more cases to predict and diagnose potential diseases and the dysfunction caused by hypertension. The main focus of this study is to prognosis and preserve human health against BP. It is one of the significant challenges in predicting and diagnosing BP in the human lifestyle. The self-powered triboelectric nanogenerator can diagnose BP using the wrist pulse pressure. To optimize the performance of the modeled triboelectric nanogenerator, the known wrist pulse pressure is applied explicitly, which converts the applied pressure into an equivalent electrical signal across the output terminals. An output open circuit voltage for the applied pulse pressure is 26 V. The generated output electrical signal is proportional to the applied pulse pressure, which is used to know the BP range. It ensures that the triboelectric nanogenerator is an opted sensor to sense the minute nadi pressure signal. This work validates that the simulated model has the potential to act as several health care monitors such as respiratory rate, heart rate, glucose range, joint motion sensing, gait, and CO₂ detectors.

Model predictive control optimisation using the metaheuristic optimisation for blood pressure control

Given the importance of high blood pressure, it is important to control and maintain a constant blood pressure level in the normal state. The main aim of this article is to design a model predictive controller with a genetic algorithm (GA) for the regulation of arterial blood pressure. The present study is an applied cross‐sectional study. In order to do this research, studies related to designing mathematical models for blood pressure regulation and mechanical models for heart muscle and pressure sensors are investigated. Then, a model predictive controller with GA is designed for blood pressure control. All control and design operations are performed in the MATLAB software. According to the viscoelasticity of blood, transducer, and injection set, we can assume the mechanical model as Mass, Spring, and Damper. Initially, the patient's blood pressure is lower than normal, and after controlling, the patient's blood pressure returned to normal. By using a GA‐based model predictive control (MPC), mathematical validation, and mechanical model, the patient's blood pressure can be adjusted and maintained. The simulation result shows that the GA‐based MPC offers acceptable response and speed of operation and the proposed controller can achieve good tracking and disturbance rejection.

Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm

In recent times, the main problem associated with big data analytics is its high dimensional data over the search space. Such data gathers continuously in search space making traditional algorithms infeasible for data mining in real time environment. Hence, feature selection is an important method to lighten the load during processing while inducing a model for mining. However, mining over such high dimensional data leads to formulation of optimal feature subset, which grows exponentially and leads to intractable computational demand. In this paper, a novel lightweight mechanism is used as a feature selection method, which solves the after effects arising with optimal feature selection. The feature selection in big data mining is done using accelerated flower pollination (AFP) algorithm. This method improves the accuracy of feature selection with reduced processing time. The proposed method is tested under larger set of data with high dimensionality to test the performance of proposed method.

Blood Glucose Regulation for Post-Operative Patients with Diabetics and Hypertension Continuum: A Cascade Control-Based Approach

Management of glycemic level in post-operative condition is critical for hypertensive patients and the post-operative stress may results in hyperglycemia, hyper insulin and osmotic diuresis. Recent medical research shows that diabetic and hypertension hands together in a significant overlap in its etiology and its disease mechanism. It is clear that there is a call for monitoring in the parameter and controlling the glucose level particularly in the presence of hypertension. This paper proposes the novel complex (cascade) control system to control the insulin infusion level particularly in the presence of hypertension. Based on the requirements the structure has been designed and the simulation results indicates that the proposed control strategy shows better results and may achieve potentially better glycemic control to the hypersensitive diabetic patients.

Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model

In this paper, the objective is to generate a mesh model of a spine that simulates numerically the biomedical properties of two vertebrae (L4 and L5) of human spine and an inter vertebrae disc using Finite Element Analysis (FEA) technique. Here, different types of non-linear filters and different edge detection techniques are used to segment the edges and the results are compared. The result shows that median filter obtains improved segmented output results in terms of edge length density, average magnitude, final threshold, initial position, and fine-tuned image. The behaviour of spine FEA model is analysed in terms of various parameters like equivalent elastic strain, total deformation, maximum principal elastic strain, minimum principal elastic strain, shear elastic strain, normal elastic strain, and minimum and maximum principal stress, equivalent stress, shear stress and normal stress. These parameters are used to analyse the human spine model under different conditions and different angles using ANSYS simulation tool. Further, MATLAB is carried out to implement various filters and edge detectors on proposed spine model.