Magnetoresistance and magneto-plasmonic sensors for the detection of cancer biomarkers : A bibliometric analysis and recent advances

The conventional approaches to diagnosing cancer are expensive, often involve exposure to radiation, and struggle to identify early-stage lung cancer. As a result, the five-year survival rate is significantly reduced. Fortunately, promising alternatives using magnetoresistance (MR) and magneto-plasmonic sensors have emerged for swiftly, accurately, and inexpensively detecting cancer in its initial phases. These sensor technologies offer numerous advantages over their counterparts, such as minimal background noise, immunity to environmental influences, compatibility with nanofabrication methods, ability to detect multiple substances simultaneously, straightforward integration, high specificity, distinctive identifying capabilities, real-time monitoring, stability, label-free detection, and remarkable sensitivity for detecting individual molecules. Nevertheless, since the use of these techniques for cancer biomarker detection is relatively new, it is essential to conduct a bibliometric analysis and review recent literature to offer guidance to both early-career and established researchers in this domain. Consequently, this study performs a scientometric evaluation of the literature related to cancer biomarker detection using MR and magneto-plasmonic methods. The objective is to pinpoint current preferred techniques and challenges by examining statistics such as publication numbers, authors, countries, journals, and research interests. Furthermore, the paper also presents the latest advancements in MR and magneto-plasmonic sensors for cancer biomarker detection, with a focus on the last decade. In addition, an overview of the ongoing research in the field of MR and magneto-plasmonic sensors for detecting cancer biomarkers is highlighted. Finally, a summary on the level of current research including the significant accomplishments, challenges, and outlooks of MR and magneto-plasmonic sensors for the detection of cancer biomarkers are highlighted

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology

Abstract Purpose Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. Methods This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model’s performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. Results The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. Conclusions The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature

Hall current and thermal radiation effects of 3D rotating hybrid nanofluid reactive flow via stretched plate with internal heat absorption

The present analysis deals with the impact of a magnetic field, joule heating, rotation parameter, and Hall current, as well as nonlinear thermal radiation, on a rotating hybrid Fe3O4/Al2O3 nanofluid over-stretched plate in the presence of a chemical reaction with thermophoresis and a Brownian motion parameter. The primary focus of this research is on the Brownian motion parameter. Similar transformations are used to translate the governing partial differential equations into a set of nonlinear ordinary differential equations. The shooting technique obtains numerical solutions for that system of equations. The impact of various entry parameters on transversal and longitudinal velocities, temperature, heat flow and surface shear stress are studied numerically and graphically. It was shown that there is a strong connection between the primary research when looking at particular situations that indicate how the current technique meets the convergence requirements. In addition, the physical relevance of the contributed parameters is shown via graphs and tables. The discovery demonstrates that an increase in the particle concentration of the hybrid nanofluid accelerates the flow of the fluid. In addition, factoring in dissipative heat makes it more likely that the fluid temperature will be increased to accommodate the participation of the particle concentration

Roles of Inorganic Oxide Based HTMs towards Highly Efficient and Long-Term Stable PSC—A Review

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication

Assessment of patients radiation doses associated with computed tomography coronary angiography

Computed tomography coronary angiography (CTCA) has generated tremendous interest over the past 20 years by using multidetector computed tomography (MDCT) because of its high diagnostic accuracy and efficacy in assessing patients with coronary artery disease. This technique is related to high radiation doses, which has raised serious concerns in the literature. Effective dose (E, mSv) may be a single parameter meant to reflect the relative risk from radiation exposure. Therefore, it is necessary to calculate this quantity to point to relative radiation risk. The objectives of this study are to evaluate patients’ exposure during diagnostic CCTA procedures and to estimate the risks. Seven hundred ninety patients were estimated during three successive years. The patient’s exposure was estimated based on a CT device’s delivered radiation dose (Siemens Somatom Sensation 64 (64-MDCT)). The participating physicians obtained the parameters relevant to the radiation dose from the scan protocol generated by the CT system after each CCTA study. The parameters included the volume CT dose index (CTDIvol, mGy) and dose length product (DLP, mGy × cm). The mean and range of CTDIvol (mGy) and DLP (mGy × cm) for three respective year was (2018):10.8 (1.14–77.7) and 2369.8 ± 1231.4 (290.4–6188.9), (2019): 13.82 (1.13–348.5), and 2180.5 (501.8–9534.5) and (2020) 10.9 (0.7–52.9) and 1877.3 (149.4–5011.1), respectively. Patients’ effective doses were higher compared to previous studies. Therefore, the CT acquisition parameter optimization is vital to reduce the dose to its minimal value

Enhanced heat transfer and fluid motion in 3D nanofluid with anisotropic slip and magnetic field

A mathematical model is envisaged that discusses the motion of 3D nanofluids (NFs) with anisotropic slip influence magnetic field past a stretching sheet. The heat transportation phenomenon is analysed by melting effect, heat generation, and chemical reaction. The main motivation of this study is to analyse the behaviour of liquid motion and heat transfer (HT) of NFs because this study has huge applications in boiling, solar energy, and micropower generation, which are used in the engineering process. The physical governing partial differential equation is transformed into a coupled non-linear system of ordinary differential equations using suitable appropriate transformations. The translated equations are calculated using Runge–Kutta–Fehlberg method via shooting procedure. The physical characteristics of various parameters on velocities, concentration, and thermal fields are explored in detail. The HT is high in NFs when compared to pure or regular liquids for ascending values of heat source parameter and slip factor. Also, the skin friction coefficients via coordinate axes and rate of Nusselt number were analysed

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Abstract Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage

Maximizing stroke recovery with advanced technologies: A comprehensive assessment of robot-assisted, EMG-Controlled robotics, virtual reality, and mirror therapy interventions

Inconvenience caused by stroke brings physical disability found in many people which restricts their daily life activities. Globally, it was predicted that the prevalence of stroke will rise to 21.9 % by 2030. To improve the sensory and motor recovery of the stroke survivors, they required high attention for their controllability and adaptability. About 80 % of stroke victims have upper-limb motor deficits which affects their daily living activities. Due to technological advancement in rehabilitation engineering with new interventions, traditional physical therapy for stroke patients now translates into new rehabilitation strategies which help in fast recovery of motor tasks with intention motions. Electromyography (EMG) based stroke rehabilitation now being developed for enhancement and assessment of motion control in clinical settings. The integration of EMG-based intervention provides the feasibility to use the concept of myoelectric control with therapeutic settings. In this paper, the use of EMG-based robot aided therapy has been discussed and highlights the contribution of interventions for stroke rehabilitation. Furthermore, discussion also emphasis on virtual reality and mirror therapy and their latest interventions and approaches carried out by different investigators for the evaluations and assessment of stroke rehabilitation in lieu of considerations for improved functional motor task. The most widely used functional outcome measures for conducting clinical assessment of stroke patients in randomized control trials (RCTs) are Fugl-meyer assessment-upper extremity (FMA-UE), a score range from 0 to 66points, Fugl-meyer assessment for lower extremity (FMA-LE) has a score from 0 to 34 points, the Action Research Arm Test (ARAT), a scale from 0 to 57 points, the Functional independence measure (FIM) has a scale of 18–126 points, Modified Ashworth Scale (MAS), a scale from 0 to 5 points and Box and block test (BBT). Furthermore, in most of the RCTs the statistical significance was set at p < 0.05 (95 % confidence interval). Moreover, future directions suggested that for real time autonomous detection of motion intentions, the EMG-based machine learning must be involved with robots, virtual reality and mirror therapy-based interventions to achieve 95 % accuracy that could leads to the development of intelligent rehabilitation interventions for stroke survivors

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease

Assessment of Planting Method and Deficit Irrigation Impacts on Physio-Morphology, Grain Yield and Water Use Efficiency of Maize (Zea mays L.) on Vertisols of Semi-Arid Tropics

Agriculture in a water-limited environment is critically important for today and for the future. This research evaluates the impact of deficit irrigation in different planting methods on the physio-morphological traits, grain yield and WUE of maize (Zea mays L.). The experiment was carried out in 2015 and 2016, consisting of three planting methods (i.e., BBF, SNF, and DWF) and four irrigation levels (i.e., I10D: irrigation once in ten days, I40: irrigation at 40% DASM, I50: irrigation at 50% DASM, and I60: irrigation at 60% DASM). The results reveal that varying degrees of water stress due to planting methods and irrigation levels greatly influenced the maize physio-morphological traits and yield attributes. The combined effect of DWF + I50 benefited the maize in terms of higher leaf area, RWC, SPAD values, CGR, and LAD, followed by the SNF method at 60 DAS. As a result, DWF + I50 and SNF + I50 had higher 100 grain weight (30.5 to 31.8 g), cob weight (181.4 to 189.6 g cob−1) and grain yield (35.3% to 36.4%) compared to other treatments. However, the reduction in the number of irrigations (24.0%) under SNF + I50 resulted in a 34% water saving. Thus, under a water-limited situation in semi-arid tropics, the practice of the SNF method + I50 could be an alternative way to explore the physio-morphological benefits in maize