The Importance of Anthraquinone and Its Analogues and Molecular Docking Calculation

In drug-delivery systems containing nano-drug structures, targeting the tumorous tissue by anthraquinone molecules with high biological activity, and reaching and destroying tumors by their tumor-killing effect reveals remarkable results for the treatment of tumors. The various biological activities of anthraquinones and their derivatives depend on molecular conformation; hence, their intra-cell interaction mechanisms including deoxyribonucleic acid (DNA), ribonucleic acid (RNA), enzymes, and hormones. Computer-based drug design plays an important role in the design of drugs and the determination of goals for them. Molecular docking has been widely used in structure-based drug design. The effects of anthraquinone analogues in tumor cells as a result of their interaction with DNA strand has increased the number of studies done on them, and they have been shown to have a wide range of applications in chemistry, medicine, pharmacy, materials, and especially in the field of biomolecules.

Computational Healthcare System With Image Analysis

The quickly extending field of huge information examination has begun to assume a crucial part in the advancement of human services practices and research. In this chapter, challenges like gathering information from complex heterogeneous patient sources, utilizing the patient/information relationships in longitudinal records, understanding unstructured clinical notes in the correct setting and efficiently dealing with expansive volumes of medicinal imaging information, and removing conceivably valuable data is shown. Healthcare and IoT and machine learning along with data mining are also discussed. Image analysis and segmentation methods comparative study is given for the examination of computer vision, imaging handling, and example acknowledgment has gained considerable ground amid the previous quite a few years. Examiners have distributed an abundance of essential science and information reporting the advance and social insurance application on medicinal imaging.

Sequential Importance Sampling for Logistic Regression Model

Logistic regression is one of the most popular models to classify in data science, and in general, it is easy to use. However, in order to conduct a goodness-of-fit test, we cannot apply asymptotic methods if we have sparse datasets. In the case, we have to conduct an exact conditional inference via a sampler, such as Markov Chain Monte Carlo (MCMC) or Sequential Importance Sampling (SIS). In this chapter, the authors investigate the rejection rate of the SIS procedure on a multiple logistic regression models with categorical covariates. Using tools from algebra, they show that in general SIS can have a very high rejection rate even though we apply Linear Integer Programming (IP) to compute the support of the marginal distribution for each variable. More specifically, the semigroup generated by the columns of the design matrix for a multiple logistic regression has infinitely many “holes.” They end with application of a hybrid scheme of MCMC and SIS to NUN study data on Alzheimer disease study.

The Importance of Ionic Liquids and Applications on Their Molecular Modeling

Ionic liquids are salts with melting points generally below 100 °C made of entirely ions by the combination of a large cation and a group of anions. Some ionic liquids are found to have therapeutic properties due to their toxic effects (e.g., anticancer, antibacterial, and antifungal properties). The determination of the most stable molecular structures, that is, the lowest energy conformer of these ionic liquids with versatile biological activities, is of particular importance. Density function theory (DFT) based on quantum mechanical calculation method, one of the molecular modeling methods, is widely used in physics and chemistry to determine the electronic structures of these stable geometries and molecules. With the theory, the energy of the molecule is determined by using the electron density instead of the wave function. It is observed that the theoretical models developed on the ionic liquids in the literature are in agreement with the experimental results because of electron correlations included in the calculation.

Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms.

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.

Development of a Classification Model for CD4 Count of HIV Patients Using Supervised Machine Learning Algorithms

This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform the DT algorithm used.

Deep Learning Models for Biomedical Image Analysis

Biomedical image analysis has become critically important to the public health and welfare. However, analyzing biomedical images is time-consuming and labor-intensive, and has long been performed manually by highly trained human experts. As a result, there has been an increasing interest in applying machine learning to automate biomedical image analysis. Recent progress in deep learning research has catalyzed the development of machine learning in learning discriminative features from data with minimum human intervention. Many deep learning models have been designed and achieved superior performance in various data analysis applications. This chapter starts with the basic of deep learning models and some practical strategies for handling biomedical image applications with limited data. After that, case studies of deep feature extraction for gene expression pattern image annotations, imaging data completion for brain disease diagnosis, and segmentation of infant brain tissue images are discussed to demonstrate the effectiveness of deep learning in biomedical image analysis.

A System to Measure Physiological Response During Social Interaction in VR for Children With ASD

This chapter presents work aimed at investigating interactions between virtual reality (VR) and children with autism spectrum disorder (ASD) using physiological sensing of affective cues. The research objectives are two-fold: 1) develop VR-based social communication tasks and integrate them into the physiological signal acquisition module to enable the capture of one's physiological responses in a time-synchronized manner during participation in the task and 2) conduct a pilot usability study to evaluate a VR-based social interaction system that induces an affective response in ASD and typically developing (TD) individuals by using a physiology-based approach. Physiological results suggest there is a different physiological response in the body in relation to the reported level of the affective states. The preliminary results from a matched pair of participants could provide valuable information about specific affect-eliciting aspects of social communication, and this feedback could drive individualized interventions that scaffold skills and improve social wellbeing.