13 research outputs found
Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm
Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches
Perspective Chapter: Artificial Intelligence in Multiple Sclerosis
In recent times, the words artificial intelligence, machine learning, and deep learning have been making a lot of buzz in different domains and especially in the healthcare sector. In disease areas like multiple sclerosis (MS), these intelligent systems have great potential in aiding the detection and prediction of disease progression and disability, identification of disease subtypes, monitoring, treatment, and novel drug-target identification. The different imaging techniques used to date in multiple sclerosis, various algorithms such as convolutional neural network, Support Vector Machine, long short-term memory networks, JAYA, Random Forest, Naive Bayesian, Sustain, DeepDTnet, and DTINet used in the various domains of multiple sclerosis are explored, along with used cases. Hence it is important for healthcare professionals to have knowledge on artificial intelligence for achieving better healthcare outcomes
Recommended from our members
Molecular interactions and their impact on life sciences
The behaviour and function of biomolecules represent a fundamental aspect in modulating the activity of micro and macro-scale complexes evolved in cells and tissues. The network of interactions of such biomolecules allow for the formation and regulation of the basic machinery of life, yet is commonly studied under non-physiological conditions. In order to characterise the behaviour and function of such biomolecules, they have to be analysed under relevant conditions, ideally in biofluids, cells or artificial systems which mainly imitate these properties. Recent microfluidic applications present an orthogonal approach for determining the interactions between a wide range of biomolecules, thus allow the study of molecular binding in the condensed phase with no need for extensive dilution, sample immobilisation or changes to the molecular environment from the liquid to the gas phase.
As part of my PhD, I capitalised on microfluidic diffusion approaches, developed in the Knowles lab to systematically study the binding and thermodynamics of small heat shock proteins, such as clusterin, αB-crystallin and the Brichos chaperone domain, to aggregated forms of amyloid-beta and α-synuclein, protein aggregates that are associated with a wide range of neurodegenerative diseases. The three chaperones are crucial components of the cellular proteostasis network and characteristically overexpressed during cell stress. Each chaperone type shows distinct binding behaviour to protein aggregates, which can be related to its inhibitory function. While αB-crystallin binding to α-synuclein is entropically driven by conformational rearrangement, the binding of Brichos to amyloid-beta fibrils is shown to be enthalpically driven as it inhibits specifically secondary nucleation processes. In contrast to the specific secondary nucleation inhibition by Brichos, clusterin inhibits specifically fibril elongation of amyloid-beta. I could show that these two specific aggregation processes are affected by the two chaperones, Brichos and clusterin, in a non-cooperative manner.
These molecular details are particularly relevant in the context of the rational design of drug molecules that could, potentially in combination, target multiple specific aggregation steps in a selective manner. Therefore, I further screened the binding of a wide range of monoclonal antibodies to either amyloid-beta monomers or fibrils, which are currently at different stages of clinical phase trials for Alzheimer's therapy. I thus show that the obtained stoichiometry and affinity information of the drug correlates with the distinct inhibition mechanisms and consequently provides mechanistic and structural information.
In contrast to studying disease related model systems in vitro under homogeneous conditions, measurements in complex body fluids are key in medical applied science, e.g. cancer treatment or immunological characterisation. In my research, I have undertaken the challenge of extending the platform developed above to characterise the binding of a wide range of molecules under complex solution conditions. Preliminary data obtained during my PhD underlines the extraordinary capability of the diffusion-based microfluidics to being applicable for investigating the binding parameters of molecules involved in alloimmunisation in human serum.
Along with my main focus on measuring protein interactions with diffusion-based microfluidics, I further developed a technique using selective separation properties, such as particle charge, hydrophobicity, size or immunoaffinity and coupled it with a series of microfluidic devices for an instantaneous and full biophysical characterisation of heterogeneous solutions. This new technique can be used to explore the formation of protein oligomers or protein complexation, characterisation and identification of complex mixtures in the context of amyloid formation and protein homeostasis
Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study
The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes
Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm
Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches